diff --git a/.Rbuildignore b/.Rbuildignore
index 0dd08a2..17d3d7f 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -12,3 +12,5 @@
 ^pkgdown$
 ^CRAN-SUBMISSION$
 ^CONTRIBUTING\.md$
+^inst/joss$
+^inst/tutorials$
diff --git a/DESCRIPTION b/DESCRIPTION
index 278ae0e..24f5339 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: SSN2
 Title: Spatial Modeling on Stream Networks
-Version: 0.1.1
+Version: 0.2.0
 Authors@R: c(
     person(given = "Michael",
            family = "Dumelle",
@@ -26,7 +26,7 @@ License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Depends: 
     R (>= 2.10)
 Imports: 
@@ -36,10 +36,10 @@ Imports:
     generics,
     tibble,
     graphics,
-    parallel,
     spmodel,
     RSQLite,
-    utils
+    utils,
+    withr
 Suggests: 
     rmarkdown,
     knitr,
diff --git a/NAMESPACE b/NAMESPACE
index b49b317..a5edd8c 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -94,7 +94,6 @@ S3method(model.frame,ssn_glm)
 S3method(model.frame,ssn_lm)
 S3method(model.matrix,ssn_glm)
 S3method(model.matrix,ssn_lm)
-S3method(names,SSN)
 S3method(plot,Torgegram)
 S3method(plot,ssn_glm)
 S3method(plot,ssn_lm)
@@ -132,6 +131,7 @@ export(Torgegram)
 export(augment)
 export(copy_lsn_to_temp)
 export(covmatrix)
+export(create_netgeom)
 export(dispersion_initial)
 export(dispersion_params)
 export(euclid_initial)
@@ -152,6 +152,7 @@ export(ssn_glm)
 export(ssn_import)
 export(ssn_import_predpts)
 export(ssn_lm)
+export(ssn_names)
 export(ssn_put_data)
 export(ssn_rbeta)
 export(ssn_rbinom)
@@ -205,10 +206,6 @@ importFrom(graphics,legend)
 importFrom(graphics,par)
 importFrom(graphics,points)
 importFrom(graphics,title)
-importFrom(parallel,detectCores)
-importFrom(parallel,makeCluster)
-importFrom(parallel,parLapply)
-importFrom(parallel,stopCluster)
 importFrom(sf,st_as_sf)
 importFrom(sf,st_bbox)
 importFrom(sf,st_centroid)
@@ -290,4 +287,5 @@ importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
 importFrom(utils,read.table)
 importFrom(utils,tail)
+importFrom(withr,local_dir)
 useDynLib(SSN2)
diff --git a/NEWS.md b/NEWS.md
index cc5b261..33c2ec4 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,25 @@
+# SSN2 0.2.0
+
+## Major Updates
+
+* Significant testing, documentation, and auxiliary (e.g., `README.md`) updates as part of a submission to *Journal of Open Source Software*. Relevant issues associated with the review are available at [#11](https://github.com/USEPA/SSN2/issues/11), [#12](https://github.com/USEPA/SSN2/issues/12), [#13](https://github.com/USEPA/SSN2/issues/13), [#14](https://github.com/USEPA/SSN2/issues/14), [#15](https://github.com/USEPA/SSN2/issues/15), [#16](https://github.com/USEPA/SSN2/issues/16), [#17](https://github.com/USEPA/SSN2/issues/17), [#20](https://github.com/USEPA/SSN2/issues/20), [#21](https://github.com/USEPA/SSN2/issues/21). The review is [linked here](https://github.com/openjournals/joss-reviews/issues/6389).
+* Added support for geopackage file formats in the `.ssn` folder that is accessed when importing SSN objects via `ssn_import()`.
+
+## Minor Updates
+
+* Added `ssn_names()` to return column names in the `edges`, `obs`, and `preds` elements of an SSN object.
+* Changed `Matrix::rankMatrix(X, method = "tolNorm2")` to `Matrix::rankMatrix(X, method = "qr")` to enhance stability when determining linear independence in `X`, the design matrix of explanatory variables.
+* Replaced an error message with a warning message when `X` has perfect collinearities (i.e., is not full rank).
+* Removed `format_additive` argument from `ssn_import()` because of transition to geopackage support, which eliminates the need to convert additive function values to text.
+* Added the `create_netgeom()` function to create the network geometry column for the `edges`, `obs`, and `preds` elements in an SSN object.
+* Minor vignette updates.
+* Minor documentation updates.
+
+## Bug Fixes
+
+* Fixed a bug in `SSN_to_SSN2()` that caused an error using `ssn_write()` with no prediction sites.
+* Replaced `names.SSN()` with `ssn_names()`, as `names.SSN()` prevented proper naming of elements in the SSN object.
+
 # SSN2 0.1.1
 
 ## Minor Updates
diff --git a/R/SSN2.R b/R/SSN2.R
index 71a169a..b93c144 100644
--- a/R/SSN2.R
+++ b/R/SSN2.R
@@ -7,7 +7,6 @@
 #' @importFrom RSQLite dbConnect dbDisconnect dbExistsTable dbListTables dbReadTable dbRemoveTable dbWriteTable SQLite
 #' @importFrom generics tidy glance augment
 #' @importFrom graphics abline legend par points title
-#' @importFrom parallel detectCores makeCluster parLapply stopCluster
 #' @importFrom sf st_as_sf st_bbox st_centroid st_coordinates st_crs st_delete st_drop_geometry
 #'   st_geometry_type st_intersects st_read st_set_crs st_set_geometry st_write
 #' @importFrom spmodel AICc covmatrix dispersion_initial dispersion_params glances loocv pseudoR2 randcov_initial randcov_params varcomp
@@ -18,4 +17,7 @@
 #'   reformulate rbeta rgamma rnbinom rnorm rpois rstandard terms var vcov
 #' @importFrom tibble tibble as_tibble
 #' @importFrom utils read.table tail
+#' @importFrom withr local_dir
 NULL
+
+# #' @importFrom parallel detectCores makeCluster parLapply stopCluster
diff --git a/R/SSN_to_SSN2.R b/R/SSN_to_SSN2.R
index 0ff9d9e..25e6138 100644
--- a/R/SSN_to_SSN2.R
+++ b/R/SSN_to_SSN2.R
@@ -4,12 +4,6 @@
 #'   created in the SSN package to an S3 \code{SSN} object used in the
 #'   SSN2 package.
 #' @param object A SpatialStreamNetwork object
-#' @param edge_additive A character vector of additive function value
-#'   column names found in edges. Default is NULL. See Details for
-#'   more information.
-#' @param site_additive A character vector of additive function value
-#'   column names found in the observed sites and prediction
-#'   sites. See Details for more information. Default is NULL.
 #' @details \command{SSN_to_SSN2()} has been made available to help users
 #'   migrate from the \code{SSN} package to the updated \code{SSN2}
 #'   package. It is used to convert existing S4 \code{SpatialStreamNetwork}
@@ -18,34 +12,12 @@
 #'   used to create an S3 \code{SSN} object from data stored locally in a .ssn
 #'   directory.
 #'
-#'   Additive function values are used to generate spatial weights for
-#'   the tail-up covariance function used in \code{ssn_glm}. The range
-#'   of additive function values are restricted to \eqn{0 \le AFV \le
-#'   1}. In the \code{SSN2} package, columns containing additive
-#'   function values are stored as text, rather than numeric
-#'   format. This prevents values less than 1 with more than 10 digits
-#'   from being truncated when writing/reading shapefiles (and their
-#'   .dbf tables). The columns containing additive function values are
-#'   specified using the \code{edge_additive} and \code{site_additive} arguments
-#'   and converted to character format in the \code{SSN} class object
-#'   returned. The arguments \code{edge_additive} and \code{site_additive}
-#'   accept a single column name in character format, or a vector
-#'   containing multiple column names. Note that, column names for
-#'   additive function values on the edges, sites, and prediction
-#'   sites may differ. If a column specified in \code{edge_additive} or
-#'   \code{site_additive} is not present, the function will return a
-#'   warning, rather than an error. Columns containing additive
-#'   function values can also be converted to text manually using the
-#'   \code{\link[base]{formatC}} function, which provides the
-#'   flexibility needed to store the values with their full precision.
-#'
-#' @return An S3 \code{SSN} class object, with additive function value
-#'   columns converted to text format.
+#' @return An S3 \code{SSN} class object.
 #'
 #' @name SSN_to_SSN2
 #' @export
-SSN_to_SSN2 <- function(object, edge_additive = NULL, site_additive = NULL) {
-
+#'
+SSN_to_SSN2 <- function(object) {
   .Deprecated(new = "ssn_import")
 
   if (!requireNamespace("sp", quietly = TRUE)) {
@@ -65,41 +37,43 @@ SSN_to_SSN2 <- function(object, edge_additive = NULL, site_additive = NULL) {
 
     nl.coords <- object@network.line.coords
 
-    edges$netgeom <- paste("ENETWORK",
-      paste("(",
-        paste(
-          nl.coords$NetworkID,
-          nl.coords$SegmentID,
-          nl.coords$DistanceUpstream,
-          sep = " "
-        ),
-        ")",
-        sep = ""
-      ),
-      sep = " "
-    )
-
-    ## Convert additive function values to text
-    if (!is.null(edge_additive)) {
-      if (sum(edge_additive %in% colnames(edges)) != length(edge_additive)) {
-        warning(paste0(
-          "AFV column '",
-          edge_additive[!edge_additive %in% colnames(edges)],
-          "' not found in edges"
-        ), call. = FALSE)
-      } else {
-        for (j in seq_len(length(edge_additive))) {
-          tmp <- edges[, edge_additive[j]]
-          tmp <- st_set_geometry(tmp, NULL)
-          ## tmp<- 'st_geometry<-'(tmp, NULL)
-          ## edges[,edge_additive[j]]<- as.character(tmp[,1])
-          edges[, edge_additive[j]] <- formatC(tmp[, 1],
-            digits = 254, format = "f",
-            drop0trailing = TRUE
-          )
-        }
-      }
-    }
+    edges <- create_netgeom(edges, type = "LINESTRING", overwrite = TRUE) 
+
+    ## edges$netgeom <- paste("ENETWORK",
+    ##   paste("(",
+    ##     paste(
+    ##       nl.coords$NetworkID,
+    ##       nl.coords$SegmentID,
+    ##       nl.coords$DistanceUpstream,
+    ##       sep = " "
+    ##     ),
+    ##     ")",
+    ##     sep = ""
+    ##   ),
+    ##   sep = " "
+    ## )
+
+    ## ## Convert additive function values to text
+    ## if (!is.null(edge_additive)) {
+    ##   if (sum(edge_additive %in% colnames(edges)) != length(edge_additive)) {
+    ##     warning(paste0(
+    ##       "AFV column '",
+    ##       edge_additive[!edge_additive %in% colnames(edges)],
+    ##       "' not found in edges"
+    ##     ), call. = FALSE)
+    ##   } else {
+    ##     for (j in seq_len(length(edge_additive))) {
+    ##       tmp <- edges[, edge_additive[j]]
+    ##       tmp <- st_set_geometry(tmp, NULL)
+    ##       ## tmp<- 'st_geometry<-'(tmp, NULL)
+    ##       ## edges[,edge_additive[j]]<- as.character(tmp[,1])
+    ##       edges[, edge_additive[j]] <- formatC(tmp[, 1],
+    ##         digits = 254, format = "f",
+    ##         drop0trailing = TRUE
+    ##       )
+    ##     }
+    ##   }
+    ## }
 
 
     ## ------------------------------------------------
@@ -119,47 +93,49 @@ SSN_to_SSN2 <- function(object, edge_additive = NULL, site_additive = NULL) {
     np.coords <- object@obspoints@SSNPoints[[1]]@network.point.coords
     np.coords <- cbind(np.coords, sites[, c("ratio", "locID")])
     np.coords$pid <- rownames(object@obspoints@SSNPoints[[1]]@network.point.coords)
-    sites$netgeom <- paste(
-      "SNETWORK",
-      paste(
-        "(",
-        paste(
-          np.coords$NetworkID,
-          np.coords$SegmentID,
-          np.coords$DistanceUpstream,
-          np.coords$ratio,
-          np.coords$pid,
-          np.coords$locID,
-          sep = " "
-        ),
-        ")",
-        sep = ""
-      ),
-      sep = " "
-    )
+
+    sites <- create_netgeom(sites, type = "POINT", overwrite = TRUE)
+    ## sites$netgeom <- paste(
+    ##   "SNETWORK",
+    ##   paste(
+    ##     "(",
+    ##     paste(
+    ##       np.coords$NetworkID,
+    ##       np.coords$SegmentID,
+    ##       np.coords$DistanceUpstream,
+    ##       np.coords$ratio,
+    ##       np.coords$pid,
+    ##       np.coords$locID,
+    ##       sep = " "
+    ##     ),
+    ##     ")",
+    ##     sep = ""
+    ##   ),
+    ##   sep = " "
+    ## )
 
     rm(np.coords)
 
-    ## Convert additive function values to text
-    if (!is.null(site_additive)) {
-      if (sum(site_additive %in% colnames(sites)) != length(site_additive)) {
-        warning(paste0(
-          "AFV column '",
-          site_additive[!site_additive %in% colnames(sites)],
-          "' not found in observed sites"
-        ), call. = FALSE)
-      } else {
-        for (j in seq_len(length(site_additive))) {
-          ## tmp<- 'st_geometry<-'(sites[,site_additive[j]], NULL)
-          tmp <- st_set_geometry(sites[, site_additive[j]], NULL)
-          ## sites[,site_additive[j]]<- as.character(tmp[,])
-          sites[, site_additive[j]] <- formatC(tmp[, ],
-            digits = 254, format = "f",
-            drop0trailing = TRUE
-          )
-        }
-      }
-    }
+    ## ## Convert additive function values to text
+    ## if (!is.null(site_additive)) {
+    ##   if (sum(site_additive %in% colnames(sites)) != length(site_additive)) {
+    ##     warning(paste0(
+    ##       "AFV column '",
+    ##       site_additive[!site_additive %in% colnames(sites)],
+    ##       "' not found in observed sites"
+    ##     ), call. = FALSE)
+    ##   } else {
+    ##     for (j in seq_len(length(site_additive))) {
+    ##       ## tmp<- 'st_geometry<-'(sites[,site_additive[j]], NULL)
+    ##       tmp <- st_set_geometry(sites[, site_additive[j]], NULL)
+    ##       ## sites[,site_additive[j]]<- as.character(tmp[,])
+    ##       sites[, site_additive[j]] <- formatC(tmp[, ],
+    ##         digits = 254, format = "f",
+    ##         drop0trailing = TRUE
+    ##       )
+    ##     }
+    ##   }
+    ## }
 
 
     ## ------------------------------------------------------------- If
@@ -187,46 +163,48 @@ SSN_to_SSN2 <- function(object, edge_additive = NULL, site_additive = NULL) {
           tmp.sf[, c("ratio", "locID")]
         )
         np.coords$pid <- rownames(object@predpoints@SSNPoints[[i]]@network.point.coords)
-        tmp.sf$netgeom <- paste(
-          "SNETWORK",
-          paste(
-            "(",
-            paste(
-              np.coords$NetworkID,
-              np.coords$SegmentID,
-              np.coords$DistanceUpstream,
-              np.coords$ratio,
-              np.coords$pid,
-              np.coords$locID,
-              sep = " "
-            ),
-            ")",
-            sep = ""
-          ),
-          sep = " "
-        )
-
 
-        ## Convert additive function values to text
-        if (!is.null(site_additive)) {
-          if (sum(site_additive %in% colnames(tmp.sf)) != length(site_additive)) {
-            warning(paste0(
-              "AFV column '",
-              site_additive[!site_additive %in% colnames(tmp.sf)],
-              "' not found in prediction dataset ", pred.name
-            ), call. = FALSE)
-          } else {
-            for (j in seq_len(length(site_additive))) {
-              ## tmp<- 'st_geometry<-'(tmp.sf[,site_additive[j]], NULL)
-              tmp <- st_set_geometry(tmp.sf[, site_additive[j]], NULL)
-              ## tmp.sf[,site_additive[j]]<- as.character(tmp[,])
-              tmp.sf[, site_additive[j]] <- formatC(tmp[, ],
-                digits = 254, format = "f",
-                drop0trailing = TRUE
-              )
-            }
-          }
-        }
+        tmp.sf<- create_netgeom(tmp.sf, type = "POINT", overwrite = TRUE)
+        ## tmp.sf$netgeom <- paste(
+        ##   "SNETWORK",
+        ##   paste(
+        ##     "(",
+        ##     paste(
+        ##       np.coords$NetworkID,
+        ##       np.coords$SegmentID,
+        ##       np.coords$DistanceUpstream,
+        ##       np.coords$ratio,
+        ##       np.coords$pid,
+        ##       np.coords$locID,
+        ##       sep = " "
+        ##     ),
+        ##     ")",
+        ##     sep = ""
+        ##   ),
+        ##   sep = " "
+        ## )
+
+
+        ## ## Convert additive function values to text
+        ## if (!is.null(site_additive)) {
+        ##   if (sum(site_additive %in% colnames(tmp.sf)) != length(site_additive)) {
+        ##     warning(paste0(
+        ##       "AFV column '",
+        ##       site_additive[!site_additive %in% colnames(tmp.sf)],
+        ##       "' not found in prediction dataset ", pred.name
+        ##     ), call. = FALSE)
+        ##   } else {
+        ##     for (j in seq_len(length(site_additive))) {
+        ##       ## tmp<- 'st_geometry<-'(tmp.sf[,site_additive[j]], NULL)
+        ##       tmp <- st_set_geometry(tmp.sf[, site_additive[j]], NULL)
+        ##       ## tmp.sf[,site_additive[j]]<- as.character(tmp[,])
+        ##       tmp.sf[, site_additive[j]] <- formatC(tmp[, ],
+        ##         digits = 254, format = "f",
+        ##         drop0trailing = TRUE
+        ##       )
+        ##     }
+        ##   }
+        ## }
 
         pred.list[i] <- list(tmp.sf)
         names(pred.list)[i] <- pred.name
@@ -237,6 +215,7 @@ SSN_to_SSN2 <- function(object, edge_additive = NULL, site_additive = NULL) {
     ## -----------------------------------------------
     ## Construct SSN object
     ## -----------------------------------------------
+
     ssnlist <- list(
       edges = edges,
       obs = sites
@@ -245,7 +224,7 @@ SSN_to_SSN2 <- function(object, edge_additive = NULL, site_additive = NULL) {
     if (exists("pred.list")) {
       ssnlist$preds <- pred.list
     } else {
-      ssnlist$preds <- NA
+      ssnlist$preds <- list()
     }
 
     ssnlist$path <- object@path
@@ -265,7 +244,5 @@ SSN_to_SSN2 <- function(object, edge_additive = NULL, site_additive = NULL) {
 # library(SSN2)
 #
 # ## Convert S4 SpatialStreamNetworkObject to S3 SSN object
-# new_SSN <- SSN_to_SSN2(old_SSN,
-#   edge_additive = "afvArea",
-#   site_additive = "afvArea"
+# new_SSN <- SSN_to_SSN2(old_SSN)
 # )
diff --git a/R/Torgegram.R b/R/Torgegram.R
index b27589e..cad515e 100644
--- a/R/Torgegram.R
+++ b/R/Torgegram.R
@@ -135,6 +135,14 @@ Torgegram <- function(formula, ssn.object,
   new_esv_list
 }
 
+#' Get a single empirical semivariogram for flow-connected, flow-unconnected, or Euclidean distance
+#'
+#' @param dist_vector Distance vector
+#' @param resid2_vector Residual squared vector
+#' @param bins Distance bins
+#' @param cutoff Distance cutoff
+#'
+#' @noRd
 get_esv <- function(dist_vector, resid2_vector, bins, cutoff) {
   if (is.null(cutoff)) {
     cutoff <- max(dist_vector) * 0.5
diff --git a/R/amongSitesDistMat.R b/R/amongSitesDistMat.R
index 7ae834f..330f428 100644
--- a/R/amongSitesDistMat.R
+++ b/R/amongSitesDistMat.R
@@ -1,8 +1,13 @@
+#' Helper function to determining distance matrices among sites
+#'
+#' @param ssn An SSN object.
+#' @param pids A list of pid values for prediction sites
+#' @param name The network name (obs or prediction name)
+#' @param bin.table A binaryID table for the network.
+#'
+#' @return A distance matrix
+#' @noRd
 amongSitesDistMat <- function(ssn, pids, name = "obs", bin.table) {
-  ## ssn = SSN object
-  ## pids = list of pid values for prediction sites
-  ## bin.table = binaryID table for the network
-
   site.no <- length(pids)
 
   among_distance_matrix <- matrix(NA, nrow = site.no, ncol = site.no)
@@ -25,9 +30,9 @@ amongSitesDistMat <- function(ssn, pids, name = "obs", bin.table) {
     pid.data <- ssn_get_netgeom(ssn$obs[ind.pids, ], c(
       "pid", "SegmentID", "locID",
       "DistanceUpstream"
-      ), reformat = TRUE)
+    ), reformat = TRUE)
 
-    ##pid.data <- as.data.frame(sapply(pid.data, as.numeric))
+    ## pid.data <- as.data.frame(sapply(pid.data, as.numeric))
     colnames(pid.data) <- c("pid", "rid", "locID", "upDist")
   }
 
diff --git a/R/anova.R b/R/anova.R
index ce080f4..b9a8b6d 100644
--- a/R/anova.R
+++ b/R/anova.R
@@ -167,6 +167,14 @@ anova.ssn_lm <- function(object, ..., test = TRUE, Terms, L) {
 #' @export
 anova.ssn_glm <- anova.ssn_lm
 
+
+#' Get marginal (type III) chi-square statistic for ANOVA
+#'
+#' @param L Matrix of contrasts.
+#' @param object Model object.
+#'
+#' @return A marginal chi-square statistic
+#' @noRd
 get_marginal_Chi2 <- function(L, object) {
   # make matrix if a numeric vector
   if (!is.matrix(L)) {
@@ -215,6 +223,8 @@ tidy.anova.ssn_glm <- tidy.anova.ssn_lm
 
 #' Get relevant L lists for anova
 #'
+#' @description This function iterates through each call to get_L_vector (which is defined below).
+#'
 #' @param assign_index A single assign value from the model matrix
 #' @param assign_indices The assign values from the model matrix
 #'
@@ -227,6 +237,14 @@ get_L_list <- function(assign_index, assign_indices) {
   do.call(rbind, L_vectors)
 }
 
+#' Get relevant L vectors for anova
+#'
+#' @param assign_index Relevant assign value from the model matrix
+#' @param assign_indices The assign values from the model matrix
+#'
+#' @return L vectors for anova
+#'
+#' @noRd
 get_L_vector <- function(assign_val, assign_indices) {
   L_vector <- matrix(0, nrow = 1, ncol = length(assign_indices))
   L_vector[, assign_val] <- 1
diff --git a/R/checks.R b/R/checks.R
index 587031a..a7cfb2e 100644
--- a/R/checks.R
+++ b/R/checks.R
@@ -1,3 +1,11 @@
+#' Various model checks for ssn_lm
+#'
+#' @param initial_object Initial value object.
+#' @param ssn.object SSN object.
+#' @param additive Additive function value name.
+#' @param estmethod Estimation method.
+#'
+#' @noRd
 check_ssn_lm <- function(initial_object, ssn.object, additive, estmethod) {
   if (is.null(additive)) {
     if (!grepl("none", class(initial_object$tailup))) {
@@ -14,6 +22,14 @@ check_ssn_lm <- function(initial_object, ssn.object, additive, estmethod) {
   }
 }
 
+#' Various model checks for ssn_glm
+#'
+#' @param initial_object Initial value object.
+#' @param ssn.object SSN object.
+#' @param additive Additive function value name.
+#' @param estmethod Estimation method.
+#'
+#' @noRd
 check_ssn_glm <- function(initial_object, ssn.object, additive, estmethod) {
   if (is.null(additive)) {
     if (!grepl("none", class(initial_object$tailup_initial))) {
@@ -30,6 +46,12 @@ check_ssn_glm <- function(initial_object, ssn.object, additive, estmethod) {
   }
 }
 
+
+#' Check for valid tailup type
+#'
+#' @param tailup_type The tailup covariance type.
+#'
+#' @noRd
 check_tailup_type <- function(tailup_type) {
   tailup_valid <- c("linear", "spherical", "exponential", "mariah", "epa", "none")
 
@@ -38,6 +60,11 @@ check_tailup_type <- function(tailup_type) {
   }
 }
 
+#' Check for valid taildown type
+#'
+#' @param taildown_type The taildown covariance type.
+#'
+#' @noRd
 check_taildown_type <- function(taildown_type) {
   taildown_valid <- c("linear", "spherical", "exponential", "mariah", "epa", "none")
 
@@ -46,6 +73,11 @@ check_taildown_type <- function(taildown_type) {
   }
 }
 
+#' Check for valid Euclidean type
+#'
+#' @param euclid_type The Euclidean covariance type.
+#'
+#' @noRd
 check_euclid_type <- function(euclid_type) {
   euclid_valid <- c(
     "spherical", "exponential", "gaussian", "cosine",
@@ -58,6 +90,11 @@ check_euclid_type <- function(euclid_type) {
   }
 }
 
+#' Check for valid nugget type
+#'
+#' @param nugget_type The nugget covariance type.
+#'
+#' @noRd
 check_nugget_type <- function(nugget_type) {
   nugget_valid <- c("nugget", "none")
   if (!(nugget_type %in% nugget_valid)) {
@@ -65,6 +102,12 @@ check_nugget_type <- function(nugget_type) {
   }
 }
 
+#' Check for valid ssn_glm family and dispersion parameter
+#'
+#' @param family The ssn_glm family.
+#' @param dispersion The dispersion parameter.
+#'
+#' @noRd
 check_dispersion <- function(family, dispersion) {
   # family must be a character here
   family_valid <- c("binomial", "poisson", "nbinomial", "Gamma", "inverse.gaussian", "beta")
@@ -78,6 +121,13 @@ check_dispersion <- function(family, dispersion) {
   }
 }
 
+#' Various checks on the response variable in ssn_glm
+#'
+#' @param family The ssn_glm family.
+#' @param y The response variable.
+#' @param size The number of trials (if family is binomial)
+#'
+#' @noRd
 response_checks_glm <- function(family, y, size) {
   # checks on y
   if (family == "binomial") {
@@ -121,7 +171,13 @@ response_checks_glm <- function(family, y, size) {
   }
 }
 
-# check if whole number
+
+#' Check if value is a whole number.
+#'
+#' @param x A vector.
+#' @param tol Tolerance to check whether x is a whole number.
+#'
+#' @noRd
 is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) {
   abs(x - round(x)) < tol
 }
diff --git a/R/cov_betahat_adjust.R b/R/cov_betahat_adjust.R
index d4ca315..5065cd2 100644
--- a/R/cov_betahat_adjust.R
+++ b/R/cov_betahat_adjust.R
@@ -1,3 +1,16 @@
+#' Specify spatial indexing covariance matrix adjustment. Not currently
+#'   relevant as spatial indexing has not yet been implemented.
+#'
+#' @param invcov_betahat_list Placeholder.
+#' @param betahat_list Placeholder.
+#' @param betahat Placeholder.
+#' @param eigenprods_list Placeholder.
+#' @param data_object Placeholder.
+#' @param params_object Placeholder.
+#' @param cov_betahat_noadjust Placeholder.
+#' @param var_adjust Placeholder.
+#'
+#' @noRd
 cov_betahat_adjust <- function(invcov_betahat_list, betahat_list,
                                betahat, eigenprods_list, data_object, params_object,
                                cov_betahat_noadjust, var_adjust) {
diff --git a/R/cov_initial_search.R b/R/cov_initial_search.R
index b5579ff..7a45202 100644
--- a/R/cov_initial_search.R
+++ b/R/cov_initial_search.R
@@ -1,3 +1,12 @@
+#' Find initial values for optimization.
+#'
+#' @param initial_NA_object Initial object with relevant NAs to indicate which require estimation.
+#' @param ssn.object SSN object.
+#' @param data_object Data object.
+#' @param estmethod Estimation method.
+#'
+#' @return Initial values
+#' @noRd
 cov_initial_search <- function(initial_NA_object, ssn.object, data_object, estmethod) {
   # find the initial "new" sample variance
   s2 <- data_object$s2
@@ -104,6 +113,16 @@ cov_initial_search <- function(initial_NA_object, ssn.object, data_object, estme
 
 
 
+#' Evaluate initial values
+#'
+#' @param cov_grid_split A set of initial values.
+#' @param initial_NA_object Initial object with relevant NAs to indicate which require estimation.
+#' @param ssn.object SSN object.
+#' @param data_object Data object.
+#' @param estmethod Estimation method.
+#'
+#' @return The minus twice log likelihood
+#' @noRd
 eval_grid <- function(cov_grid_split, initial_NA_object, ssn.object, data_object, estmethod) {
   # turn list element into a vector
   cov_grid <- unlist(cov_grid_split)
@@ -118,6 +137,14 @@ eval_grid <- function(cov_grid_split, initial_NA_object, ssn.object, data_object
   get_minustwologlik(gll_prods, data_object, estmethod)
 }
 
+#' Replace initial values with known values.
+#'
+#' @param cov_grid A grid of initial values.
+#' @param initial_object An initial object.
+#' @param data_object Data object.
+#'
+#' @return Initial values with known values replaced.
+#' @noRd
 cov_grid_replace <- function(cov_grid, initial_object, data_object) {
   # replace each relevant component with a known value if applicable
 
diff --git a/R/cov_initial_search_glm.R b/R/cov_initial_search_glm.R
index b17500b..e52f23d 100644
--- a/R/cov_initial_search_glm.R
+++ b/R/cov_initial_search_glm.R
@@ -1,3 +1,12 @@
+#' Find initial values for optimization.
+#'
+#' @param initial_NA_object Initial object with relevant NAs to indicate which require estimation.
+#' @param ssn.object SSN object.
+#' @param data_object Data object.
+#' @param estmethod Estimation method.
+#'
+#' @return Initial values
+#' @noRd
 cov_initial_search_glm <- function(initial_NA_object, ssn.object, data_object, estmethod) {
   # find the initial "new" sample variance
   s2 <- data_object$s2
@@ -111,6 +120,16 @@ cov_initial_search_glm <- function(initial_NA_object, ssn.object, data_object, e
   best_params <- list(initial_object = updated_NA_object)
 }
 
+#' Evaluate initial values
+#'
+#' @param cov_grid_split A set of initial values.
+#' @param initial_NA_object Initial object with relevant NAs to indicate which require estimation.
+#' @param ssn.object SSN object.
+#' @param data_object Data object.
+#' @param estmethod Estimation method.
+#'
+#' @return The minus twice log likelihood
+#' @noRd
 eval_grid_glm <- function(cov_grid_split, initial_NA_object, ssn.object, data_object, estmethod) {
   cov_grid <- unlist(cov_grid_split)
   # params object
@@ -123,6 +142,14 @@ eval_grid_glm <- function(cov_grid_split, initial_NA_object, ssn.object, data_ob
   get_minustwolaploglik(lapll_prods, data_object, estmethod)
 }
 
+#' Replace initial values with known values.
+#'
+#' @param cov_grid A grid of initial values.
+#' @param initial_object An initial object.
+#' @param data_object Data object.
+#'
+#' @return Initial values with known values replaced.
+#' @noRd
 cov_grid_replace_glm <- function(cov_grid, initial_object, data_object) {
   if (!is.na(initial_object$tailup_initial$initial[["de"]])) {
     cov_grid[, "tailup_de"] <- initial_object$tailup_initial$initial[["de"]]
diff --git a/R/cov_matrix.R b/R/cov_matrix.R
index e675c1d..e6f64bb 100644
--- a/R/cov_matrix.R
+++ b/R/cov_matrix.R
@@ -2,6 +2,15 @@
 # GENERIC SETUP
 ###############################################################################
 
+
+#' Create a covariance matrix
+#'
+#' @param params Parameter object.
+#' @param dist_object Distance matrix object.
+#' @param ... Additional arguments
+#'
+#' @return A covariance matrix
+#' @noRd
 cov_matrix <- function(params, dist_object, ...) {
   UseMethod("cov_matrix", params)
 }
diff --git a/R/cov_vector.R b/R/cov_vector.R
index 6e25ef4..b2fc2e2 100644
--- a/R/cov_vector.R
+++ b/R/cov_vector.R
@@ -2,6 +2,14 @@
 # GENERIC SETUP COVARIANCES
 ###############################################################################
 
+#' Create a covariance vector
+#'
+#' @param params Parameter object.
+#' @param dist_pred_object Prediction distance matrix object.
+#' @param ... Additional arguments
+#'
+#' @return A covariance vector
+#' @noRd
 cov_vector <- function(params, dist_pred_object, ...) {
   UseMethod("cov_vector", params)
 }
diff --git a/R/createBinaryID.R b/R/createBinaryID.R
index a0f451b..df5b623 100644
--- a/R/createBinaryID.R
+++ b/R/createBinaryID.R
@@ -1,3 +1,10 @@
+#' Create the binary ID database.
+#'
+#' @param ssn SSN object
+#' @param overwrite Should binaryID.db be deleted if it already exists?
+#'
+#' @return binary ID database
+#' @noRd
 createBinaryID <- function(ssn, overwrite) {
   ## If binaryID.db exists
   if (file.exists("binaryID.db") == TRUE) {
diff --git a/R/create_netgeom.R b/R/create_netgeom.R
new file mode 100644
index 0000000..b051341
--- /dev/null
+++ b/R/create_netgeom.R
@@ -0,0 +1,143 @@
+#' @title Create netgeom column in SSN object
+#'
+#' @description Create netgeom column for edges, observed sites,
+#'   and/or prediction sites in a Landscape Network (LSN).
+#'
+#' @param sf_data An \code{sf} object with LINESTING or POINT geometry
+#'   created using \code{link{lsn_to_ssn}} (see Details).
+#' @param type Character string defining geometry type of
+#'   \code{sf_data}. Default = \code{NULL}.
+#' @param overwrite Logical indicating whether existing data should be
+#'   overwritten if present. Default = \code{FALSE}.
+#'
+#' @details Most users will not need to run \code{create_netgeom}
+#'   themselves because it is called internally when \code{lsn_to_ssn}
+#'   is run or an \code{SSN} is imported using
+#'   \code{link[SSN2]{ssn_import}} found in the \code{SSN2}
+#'   package. For users who do wish to run \code{create_netgeom}, the
+#'   \code{sf_data} object must represent edges, observed sites, or
+#'   prediction sites in a \code{SSN} object created using
+#'   \code{link{lsn_to_ssn}}.
+#'
+#'   The netgeom column contains information in character format used
+#'   to describe the topology of the LSN. The format and content of
+#'   the netgeom column differs depending on whether \code{sf_data}
+#'   contains LINESTRING (edges) or POINT (observed or prediction
+#'   sites) geometry. For edges, the netgeom format is:
+#'   \itemize{
+#'       \item{\code{'ENETWORK (netID, rid, upDist)'}}
+#'   }
+#'
+#'
+#'   For observed or prediction sites, the netgeom format is:
+#'   \itemize{
+#'       \item{\code{'SNETWORK (netID, rid, upDist, ratio, pid, locID)'}}
+#'   }
+#'
+#' The rid, ratio, upDist, netID, pid, and locID columns must be
+#' present in \code{sf_data} before netgeom is added.
+#'
+#' If \code{overwrite = TRUE} and a column named netgeom is present in
+#' \code{sf_data}, the data will be overwritten. Default = FALSE.
+#'
+#' @return An \code{sf} object containing the original data from
+#'   \code{sf_data} and an additional column named netgeom.
+#'
+#' @export
+#' @examples
+#' ## Create local temporary copy of MiddleFork04.ssn found in
+#' ## the SSN2 package. Only necessary for this example.
+#' copy_lsn_to_temp()
+#'
+#' # Import the SSN object with prediction points, pred1km
+#' mf04<- ssn_import(
+#'    paste0(tempdir(), "/MiddleFork04.ssn"),
+#'    predpts = c("pred1km"),
+#'    overwrite = TRUE
+#' )
+#'
+#' # Recalculate the netgeom column for the observed sites
+#' sf_obs <- create_netgeom(
+#'     mf04$obs,
+#'     type = "POINT",
+#'     overwrite = TRUE
+#' )
+#'
+#' # Recalculate the netgeom column for the edges
+#' sf_edges <- create_netgeom(
+#'     mf04$edges,
+#'     type = "LINESTRING",
+#'     overwrite = TRUE
+#' )
+create_netgeom <- function(sf_data, type = NULL, overwrite = FALSE) {
+  ## check sf object
+  if (!inherits(sf_data, "sf")) {
+    stop("sf_data must be an sf object.", call. = FALSE)
+  }
+
+  ## Can we overwrite netgeom if it exists?
+  if ("netgeom" %in% colnames(sf_data) & overwrite == FALSE) {
+    stop("netgeom column is present in sf_data and overwrite = FALSE")
+  }
+
+  ## Check type argument
+  if (type != "POINT" & type != "LINESTRING") {
+    stop("type argument must be set to POINT or LINESTRING")
+  }
+
+  if (type == "POINT") {
+    ## Check that columns exist
+    c.names <- c("netID", "rid", "upDist", "ratio", "pid", "locID")
+    ind <- !c.names %in% colnames(sf_data)
+    if (sum(ind) > 0) {
+      stop(paste0("columns ", paste(c.names[ind], collapse = ", "),
+                  " not found in sf_data. Is sf_data part of an SSN object and does it have POINT geometry?"))
+    }
+    ## Create netgeom
+    sf_data[, "netgeom"] <- paste0("SNETWORK (", paste(
+      sf_data$netID, sf_data$rid, sf_data$upDist,
+      sf_data$ratio, sf_data$pid, sf_data$locID
+    ), ")", sep = "")
+  }
+  if (type == "LINESTRING") {
+    ## Check that columns exist
+    c.names <- c("netID", "rid", "upDist")
+    ind <- !c.names %in% colnames(sf_data)
+    if (sum(ind) > 0) {
+      stop(paste0("columns ", paste(c.names[ind], collapse = ", "),
+                  " not found in sf_data. Is sf_data part of an SSN object and does it have LINESTRING geometry?"))
+    }
+
+    ## Create netgeom
+    sf_data[, "netgeom"] <- paste0("ENETWORK (", paste(
+      sf_data$netID,
+      sf_data$rid,
+      sf_data$upDist
+    ),
+    ")",
+    sep = ""
+    )
+  }
+  return(sf_data) ## Return sf data.frame with netgeom column added
+}
+
+
+
+
+
+
+
+# The rid, upDist and netID columns must already be present in edges
+#   before netgeom is added. These columns are created using
+#   \code{link{lines_to_lsn}}, \code{\link{updist_edges}}, and
+#   \code{link{lsn_to_ssn}}, respectively.
+#
+#   For observed or prediction sites, the netgeom format is:
+#   \itemize{
+#       \item{\code{'SNETWORK (netID, rid, upDist, ratio, pid, locID)'}}
+#   }
+#
+# The rid, ratio, upDist, netID, pid, and locID columns must be
+# present in \code{sf_data} and are created using \code{SSNbler} package functions
+# \code{sites_to_lsn}, \code{updist_sites}, and
+# \code{ssn_assemble}, respectively.
diff --git a/R/create_netgeometry.R b/R/create_netgeometry.R
deleted file mode 100644
index 3def30e..0000000
--- a/R/create_netgeometry.R
+++ /dev/null
@@ -1,19 +0,0 @@
-## Add netgeom column to sf data.frame sf_data
-create_netgeom <- function(sf_data, type = NULL) {
-  if (type == "point") {
-    sf_data[, "netgeom"] <- paste0("SNETWORK (", paste(
-      sf_data$netID, sf_data$rid, sf_data$upDist,
-      sf_data$ratio, sf_data$pid, sf_data$locID
-    ), ")", sep = "")
-  } else {
-    sf_data[, "netgeom"] <- paste0("ENETWORK (", paste(
-      sf_data$netID,
-      sf_data$rid,
-      sf_data$upDist
-    ),
-    ")",
-    sep = ""
-    )
-  }
-  return(sf_data) ## Return sf data.frame with netgeom column added
-}
diff --git a/R/data.R b/R/data.R
index 69ac620..20af008 100644
--- a/R/data.R
+++ b/R/data.R
@@ -4,24 +4,22 @@
 #' and topological information needed to construct an SSN object using
 #' the \code{SSN2} package.
 #'
-#' The \code{MiddleFork04.ssn} folder contains five shapefiles:
+#' The \code{MiddleFork04.ssn} folder contains five geopackages:
 #'
 #' \itemize{
-#'    \item edges: polyline shapefile representing the stream network
-#'    \item sites: point shapefile representing the observed site locations
-#'    \item pred1km: point shapefile representing prediction site locations at
+#'    \item edges: geopackage with LINESTRING geometry representing the stream network
+#'    \item sites: geopackage with POINT geometry representing the observed site locations
+#'    \item pred1km: geopackage with POINT geometry prediction site locations at
 #'      approximately 1km intervals throughout the stream network
-#'    \item Knapp: point shapefile representing prediction site locations on the Knapp River
-#'    \item CapeHorn: point shapefile representing prediction site locations on the Cape Horn River
+#'    \item CapeHorn: geopackage with POINT geometry representing prediction site locations on the Cape Horn River
 #' }
 #'
-#' The \code{MiddleFork04.ssn} includes one text file, \code{netID1.txt}, which contains the
-#' topological information for the stream network in the Middle Fork 2004
+#' The \code{MiddleFork04.ssn} includes two text files, \code{netID1.txt} and \code{netID2.txt}, which contain the
+#' topological information for the two stream networks in the Middle Fork 2004
 #' dataset.
 #'
 #' The distance folder contains four folders that store the hydrologic
-#' distance matrices for each of the point shapefiles (\code{obs}, \code{CapeHorn},
-#' \code{Knapp}, and \code{pred1km}). See [ssn_create_distmat()] for a
+#' distance matrices for each of the point datasets (\code{obs}, \code{CapeHorn}, and \code{pred1km}). See [ssn_create_distmat()] for a
 #' detailed description of the distance matrix file structure.
 #'
 #' Attribute data is also stored within each of the spatial
@@ -29,16 +27,16 @@
 #'
 #'  \code{edges}:
 #'   \itemize{
+#'     \item rid: Reach identifier
 #'     \item COMID: Common identifier of an NHD feature or relationship
 #'     \item GNIS_Name: Feature name as found in the Geographic Names Information System
 #'     \item REACHCODE: Unique identifier for a reach. The first 8 digits contain the identfier for the HUC8 and the last 6 digits are a unique within-HUC8 identifier for the reach
 #'     \item FTYPE: three-digit integer used to classify hydrography features in the NHD and define subtypes
 #'     \item FCODE: Numeric code that contains the feature type and its attributes as found in the NHDFCode lookup table
-#'     \item CDRAINAG: Cumulative drainage area (km2) for the lowermost location on the edge
 #'     \item AREAWTMAP: Area weighted mean annual precipitation (mm) at the lowermost location on the edge
 #'     \item SLOPE: Slope of the edge (cm/cm)
-#'     \item h2oAreaKm2: Watershed area (km2) for the lowermost location on the line segment
-#'     \item rid: Reach identifier
+#'     \item rcaAreaKm2: Reach contributing area (km2) for each edge feature. The RCA represents the land area that drains directly to the edge feature
+#'     \item h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature
 #'     \item areaPI: Segment proportional influence value, calculated using watershed area (h2oAreaKm2)
 #'     \item afvArea: Additive function value, calculated using areaPI
 #'     \item upDist: Distance from the stream outlet (most downstream location in the the stream network) to the uppermost location on the line segment
@@ -47,9 +45,10 @@
 #' }
 #'   \code{sites}:
 #'   \itemize{
+#'     \item rid: Reach identifier of the edge the site resides on
+#'     \item pid: Point identifier
 #'     \item STREAMNAME: Stream name
 #'     \item COMID: Common identifier of an NHD feature or relationship
-#'     \item CDRAINAG: Cumulative drainage area (km2)
 #'     \item AREAWTMAP: Area weighted mean annual precipitation (mm) at lowermost
 #'       location on the line segment where the site resides
 #'     \item SLOPE: Slope of the line segment (cm/cm) where the site resides
@@ -69,34 +68,33 @@
 #'       from 1980-2009 across 10 COOP air stations within the domain.
 #'       MWMT = maximum 7-day moving average of the maximum daily temperature
 #'       (i.e. maximum of all the 7-day maximums)
-#'     \item NEAR_X: x coordinate
-#'     \item NEAR_Y: y coordinate
-#'     \item rid: Reach identifier of the edge the site resides on
-#'     \item ratio: Site ratio value; provides the proportional distance along the edge to the site location
+#'     \item rcaAreaKm2: Reach contributing area (km2) for each edge feature the site resides on. The RCA represents the land area that drains directly to the edge feature
+#'     \item h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature the site resides on
+#'     \item ratio: Site ratio value; provides the proportional distance from the downstream node of the edge to the site location
 #'     \item upDist: Distance upstream from the stream outlet (m)
 #'     \item afvArea: Additive function value calculated using waterhsed area (h2oAreaKm2)
 #'     \item locID: Location identifier
 #'     \item netID: Stream network identifier
-#'     \item pid: Point identifier
+#'     \item snapdist: Distance (m) that site was moved when it was snapped to the edges
 #' }
 #'
-#'   \code{pred1km}, \code{CapeHorn}, and \code{Knapp}:
+#'   \code{pred1km} and \code{CapeHorn}:
 #'   \itemize{
+#'     \item rid: Reach identifier of the edge the site resides on
+#'     \item pid: Point identifier
 #'     \item COMID: Common identifier of an NHD feature or relationship
 #'     \item GNIS_Name: Feature name of the edge the site resides on, as found in the Geographic Names Information System
-#'     \item CDRAINAG: Cumulative drainage area (km2)
 #'     \item AREAWTMAP: Area weighted mean annual precipitation (mm) at lowermost location on the line segment where the site resides
 #'     \item SLOPE: Slope of the line segment (cm/cm) where the site resides
 #'     \item ELEV_DEM: Elevation at the site based on a 30m DEM
-#'     \item NEAR_X: x coordinate
-#'     \item NEAR_Y: y coordinate
-#'     \item rid: Reach identifier of the edge the site resides on
+#'     \item rcaAreaKm2: Reach contributing area (km2) for each edge feature the site resides on. The RCA represents the land area that drains directly to the edge feature
+#'     \item h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature the site resides on
 #'     \item ratio: Site ratio value; provides the proportional distance along the edge to the site location
 #'     \item upDist: Distance upstream from the stream outlet (m)
 #'     \item afvArea: Additive function value calculated using watershed area (h2oAreaKm2)
 #'     \item locID: Location identifier
 #'     \item netID: Stream network identifier
-#'     \item pid: Point identifier
+#'     \item snapdist: Distance (m) that site was moved when it was snapped to the edges
 #'     \item FlowCMS: Average stream flow (cubic meters per sec) for August, by year, from 1950-2010 across 9 USGS gauges in the region
 #'     \item AirMEANc: Average mean air temperature (C) from July 15 - August 31, from 1980-2009 across 10 COOP air stations within the domain
 #'     \item AirMWMTc: Average maximum air temperature (C) from July 15 - August 31, from 1980-2009 across 10 COOP air stations within the domain. MWMT = maximum 7-day moving average of the maximum daily temperature(i.e. maximum of all the 7-day maximums)
@@ -104,8 +102,8 @@
 #'
 #' @source \code{edges} are a modified version of the United States
 #'   National Hydrography Dataset
-#'   (http://nhd.usgs.gov/). \code{sites}, \code{pred1km}, \code{CapeHorn}
-#'   and \code{Knapp} are unpublished United States Forest Service data.
+#'   (http://nhd.usgs.gov/). \code{sites}, \code{pred1km} and \code{CapeHorn}
+#'   are unpublished United States Forest Service data.
 #'
 #' @docType data
 #'
diff --git a/R/get.rid.fc.R b/R/get.rid.fc.R
index 63fb4d7..a7741b4 100644
--- a/R/get.rid.fc.R
+++ b/R/get.rid.fc.R
@@ -1,3 +1,10 @@
+#' Get reach ids (in C)
+#'
+#' @param binIDs Binary ID values in the stream network
+#' @param referenceBinID A reference set of Binary ID values in the stream network
+#'
+#' @return The reach ids are unique ids for each stream segment in the stream network.
+#' @noRd
 get.rid.fc <- function(binIDs, referenceBinID) {
   ind.match <- .Call("test_fc", binIDs, referenceBinID)
   data.frame(
diff --git a/R/getObsRelationshipsDF.R b/R/getObsRelationshipsDF.R
index 9c04982..889fe35 100644
--- a/R/getObsRelationshipsDF.R
+++ b/R/getObsRelationshipsDF.R
@@ -1,3 +1,15 @@
+#' Get spatial relationships for observed points
+#'
+#' @param ssn SSN object
+#' @param pid integer pid value of interest
+#' @param junk data.frame with columns fc (logical) and binaryID
+#' @param ind vector indicator saying whether it is a duplicate
+#' @param ob data.frame with pid, rid, locID, and binaryID for sites on network ordered by pid
+#' @param ob_by_locID data.frame with pid, rid, locID, and binaryID for sites on network
+#'   ordered by locID factor level (not true locID)
+#' @param bin binaryID table
+#'
+#' @noRd
 getObsRelationshipsDF <- function(ssn, pid, junk, ind, ob, ob_by_locID, bin) {
   ## ssn = SpatialStreamNetwork object
   ## pid = integer pid value of interest
diff --git a/R/getPredRelationshipsDF.R b/R/getPredRelationshipsDF.R
index b55e8cb..2733bc2 100644
--- a/R/getPredRelationshipsDF.R
+++ b/R/getPredRelationshipsDF.R
@@ -1,3 +1,14 @@
+#' Get spatial relationships for prediction points
+#'
+#' @param ssn SSN object
+#' @param predpts Index of predpts
+#' @param ind Index for pred values on the network
+#' @param bin binaryID table for the network
+#' @param ob Data frame  with pid, rid, locID, and binaryID for sites on the
+#'   network ordered by pid (ob.i)
+#' @param j Row j of data.frame ob
+#'
+#' @noRd
 getPredRelationshipsDF <- function(ssn, predpts, ind, bin, ob, j) {
   ## ssn = SpatialStreamNetwork object
   ## num = index of predpts in SSN
diff --git a/R/get_Torgegram_initial_object.R b/R/get_Torgegram_initial_object.R
index 0b134d7..fc07c36 100644
--- a/R/get_Torgegram_initial_object.R
+++ b/R/get_Torgegram_initial_object.R
@@ -1,3 +1,8 @@
+#' Get initial object for use with Torgegram calculations
+#'
+#' @param type The Torgegram type (flow-connected, flow-unconnected, Euclidean)
+#'
+#' @noRd
 get_Torgegram_initial_object <- function(type) {
   if (any(!type %in% c("flowcon", "flowuncon", "euclid"))) {
     stop("All elements of type must be \"flowcon\", \"flowuncon\", or \"euclid\".", call. = FALSE)
diff --git a/R/get_anisotropy_corrected.R b/R/get_anisotropy_corrected.R
index bae082b..95309ba 100644
--- a/R/get_anisotropy_corrected.R
+++ b/R/get_anisotropy_corrected.R
@@ -1,3 +1,11 @@
+#' Correct anisotropy argument if initial values specified
+#'
+#' @param anisotropy Anisotropy argument
+#' @param initial_object Initial object
+#'
+#' @return A corrected anisotropy argument. Values specified in the initial object
+#'   take precedence over the anisotropy argument.
+#' @noRd
 get_anisotropy_corrected <- function(anisotropy, initial_object) {
   if (inherits(initial_object$euclid_initial, "euclid_none")) {
     anisotropy <- FALSE
diff --git a/R/get_cholprods.R b/R/get_cholprods.R
index d2681ac..bd405fd 100644
--- a/R/get_cholprods.R
+++ b/R/get_cholprods.R
@@ -15,6 +15,7 @@ get_cholprods <- function(cov_matrix, X, y) {
   list(Sig_lowchol = Sig_lowchol, SqrtSigInv_X = SqrtSigInv_X, SqrtSigInv_y = SqrtSigInv_y)
 }
 
+# A vectorized version of get_cholprods
 get_cholprods_parallel <- function(cluster_list) {
   cov_matrix <- cluster_list$c
   X <- cluster_list$x
@@ -22,7 +23,7 @@ get_cholprods_parallel <- function(cluster_list) {
   get_cholprods(cov_matrix, X, y)
 }
 
-#' Find relevant Cholesky quantities (upper, lower, products, and inverse)
+#' Find relevant Cholesky quantities (upper, lower, products, and inverse) for glms
 #'
 #' @param cov_matrix A covariance matrix
 #' @param X A model matrix
@@ -45,6 +46,7 @@ get_cholprods_glm <- function(cov_matrix, X, y) {
   )
 }
 
+# A vectorized version of get_cholprods_glm
 get_cholprods_glm_parallel <- function(cluster_list) {
   cov_matrix <- cluster_list$c
   X <- cluster_list$x
diff --git a/R/get_cov_matrix.R b/R/get_cov_matrix.R
index 200d28c..9f64835 100644
--- a/R/get_cov_matrix.R
+++ b/R/get_cov_matrix.R
@@ -1,3 +1,16 @@
+#' A helper to get the overall covariance matrix.
+#'
+#' @param params_object Parameter object.
+#' @param dist_object_oblist The distance matrices for the observed data.
+#' @param randcov_list Random effect list.
+#' @param partition_list Partition factor list.
+#' @param anisotropy Whether there is anisotropy.
+#' @param de_scale A scaling parameter on the de parameter that helps ensure
+#'   numeric stability of the covariance matrix.
+#' @param diagtol A tolerance added to the diagonal of the covariance matrix
+#'   that helps ensure numeric stability of the covariance matrix.
+#'
+#' @noRd
 get_cov_matrix <- function(params_object, dist_object_oblist,
                            randcov_list = NULL, partition_list = NULL, anisotropy,
                            de_scale, diagtol) {
@@ -24,6 +37,7 @@ get_cov_matrix <- function(params_object, dist_object_oblist,
   cov_matrix
 }
 
+# A vectorized version of get_cov_matrix
 get_cov_matrix_list <- function(params_object, data_object) {
   # this is a list of elements
 
diff --git a/R/get_cov_vector.R b/R/get_cov_vector.R
index 786fee6..2e5e7ee 100644
--- a/R/get_cov_vector.R
+++ b/R/get_cov_vector.R
@@ -1,3 +1,13 @@
+#' A helper to get the overall covariance matrix.
+#'
+#' @param params_object Parameter object.
+#' @param dist_pred_object The distance matrices between observed and prediction data.
+#' @param data The data.
+#' @param newdata The prediction data.
+#' @param partition_factor The name of the partition factor.
+#' @param anisotropy Whether there is anisotropy.
+#'
+#' @noRd
 get_cov_vector <- function(params_object, dist_pred_object, data, newdata, partition_factor = NULL, anisotropy) {
   tailup_none <- inherits(params_object$tailup, "tailup_none")
   taildown_none <- inherits(params_object$taildown, "taildown_none")
@@ -20,6 +30,13 @@ get_cov_vector <- function(params_object, dist_pred_object, data, newdata, parti
   cov_vector
 }
 
+#' Compute Euclidean covariance for prediction and a possible adjustment to Euclidean covariance for anisotropy
+#'
+#' @param params The Euclidean covariance parameters
+#' @param dist_pred_object The distance matrices between observed and prediction data.
+#' @param anisotropy Whether there is anisotropy.
+#'
+#' @noRd
 get_euclid_pred <- function(params, dist_pred_object, anisotropy) {
   if (anisotropy) {
     new_coords_observed <- transform_anis(
diff --git a/R/get_data_object.R b/R/get_data_object.R
index 1296ac3..08a356a 100644
--- a/R/get_data_object.R
+++ b/R/get_data_object.R
@@ -1,3 +1,19 @@
+#' Get the data object for use with many other functions.
+#'
+#' @param formula Model formula.
+#' @param ssn.object SSN object.
+#' @param additive Name of the additive function value column.
+#' @param anisotropy Whether there is anisotropy.
+#' @param initial_object The initial value object.
+#' @param random Random effect formula.
+#' @param randcov_initial The initial random effect object.
+#' @param partition_factor Partition factor formula.
+#' @param local Spatial indexing argument (not yet implemented)
+#' @param ... Additional arguments
+#'
+#' @return The data object that contains various pieces of important information
+#'   required for modeling.
+#' @noRd
 get_data_object <- function(formula, ssn.object, additive, anisotropy,
                             initial_object, random, randcov_initial, partition_factor, local, ...) {
   sf_column_name <- attributes(ssn.object$obs)$sf_column
@@ -37,9 +53,9 @@ get_data_object <- function(formula, ssn.object, additive, anisotropy,
   dots$contrasts <- attr(X, "contrasts")
   xlevels <- .getXlevels(terms_val, obdata_model_frame)
   # find p
-  p <- as.numeric(Matrix::rankMatrix(X))
+  p <- as.numeric(Matrix::rankMatrix(X, method = "qr"))
   if (p < NCOL(X)) {
-    stop("Perfect collinearities detected in X. Remove redundant predictors.", call. = FALSE)
+    warning("There are perfect collinearities detected in X (the matrix of explanatory variables). This may make the model fit unreliable or may cause an error while model fitting. Consider removing redundant explanatory variables and refitting the model.", call. = FALSE)
   }
   # find sample size
   n <- NROW(X)
diff --git a/R/get_data_object_glm.R b/R/get_data_object_glm.R
index 2147ae8..ae082bd 100644
--- a/R/get_data_object_glm.R
+++ b/R/get_data_object_glm.R
@@ -1,3 +1,19 @@
+#' Get the glm data object for use with many other functions.
+#'
+#' @param formula Model formula.
+#' @param ssn.object SSN object.
+#' @param additive Name of the additive function value column.
+#' @param anisotropy Whether there is anisotropy.
+#' @param initial_object The initial value object.
+#' @param random Random effect formula.
+#' @param randcov_initial The initial random effect object.
+#' @param partition_factor Partition factor formula.
+#' @param local Spatial indexing argument (not yet implemented)
+#' @param ... Additional arguments
+#'
+#' @return The glm data object that contains various pieces of important information
+#'   required for modeling.
+#' @noRd
 get_data_object_glm <- function(formula, ssn.object, family, additive, anisotropy,
                                 initial_object, random, randcov_initial, partition_factor, local, ...) {
   sf_column_name <- attributes(ssn.object$obs)$sf_column
@@ -37,9 +53,9 @@ get_data_object_glm <- function(formula, ssn.object, family, additive, anisotrop
   dots$contrasts <- attr(X, "contrasts")
   xlevels <- .getXlevels(terms_val, obdata_model_frame)
   # find p
-  p <- as.numeric(Matrix::rankMatrix(X))
+  p <- as.numeric(Matrix::rankMatrix(X, method = "qr"))
   if (p < NCOL(X)) {
-    stop("Perfect collinearities detected in X. Remove redundant predictors.", call. = FALSE)
+    warning("There are perfect collinearities detected in X (the matrix of explanatory variables). This may make the model fit unreliable or may cause an error while model fitting. Consider removing redundant explanatory variables and refitting the model.", call. = FALSE)
   }
   # find sample size
   n <- NROW(X)
diff --git a/R/get_dist_object.R b/R/get_dist_object.R
index 8da4739..850885f 100644
--- a/R/get_dist_object.R
+++ b/R/get_dist_object.R
@@ -1,4 +1,12 @@
-# parent function to get the distance object
+#' Get the distance matrix oibject
+#'
+#' @param ssn.object SSN object.
+#' @param initial_object Initial value object.
+#' @param additive Name of the additive function value column.
+#' @param anisotropy Whether there is anisotropy.
+#'
+#' @return A distance matrix object that contains various distance matrices used in modeling.
+#' @noRd
 get_dist_object <- function(ssn.object, initial_object, additive, anisotropy) {
   # get netgeom
   netgeom <- ssn_get_netgeom(ssn.object$obs, reformat = TRUE)
@@ -60,7 +68,7 @@ get_dist_object <- function(ssn.object, initial_object, additive, anisotropy) {
   dist_object
 }
 
-# implement list structure for when local is implemented (big data)
+# a vectorized version of get_dist_object
 get_dist_object_oblist <- function(dist_object, observed_index, local_index) {
   # find names of the distance object
   dist_object_names <- names(dist_object)
@@ -87,6 +95,14 @@ get_dist_object_oblist <- function(dist_object, observed_index, local_index) {
   dist_object_oblist
 }
 
+#' Get list of full distance matrices
+#'
+#' @param ssn.object SSN object.
+#' @param initial_object Initial value object.
+#' @param additive Name of the additive function value column.
+#' @param order_list A list of order by pid and network.
+#'
+#' @noRd
 get_dist_matlist <- function(ssn.object, initial_object, additive,
                              order_list) {
   network_index <- order_list$network_index
@@ -171,8 +187,14 @@ get_dist_matlist <- function(ssn.object, initial_object, additive,
   dist_matlist
 }
 
-# auxiliary functions
-# auxiliary functions
+
+#' Get distance to the nearest junction matrix
+#'
+#' @param network_index Network index
+#' @param ssn.object SSN object
+#' @param newdata_name Name of the newdata matrix (if relevant)
+#'
+#' @noRd
 get_distjunc_matlist <- function(network_index, ssn.object, newdata_name = NULL) {
   if (is.null(newdata_name)) {
     ext <- "obs"
@@ -208,7 +230,13 @@ get_distjunc_matlist <- function(network_index, ssn.object, newdata_name = NULL)
 }
 
 
-# subset relevant objects in the distance object
+#' Subset relevant objects in the distance object
+#'
+#' @param dist_object_name Name of the relevant element in the distance object
+#' @param dist_object Distance object
+#' @param index Index of the distance matrix elements for ordering
+#'
+#' @noRd
 subset_dist_object <- function(dist_object_name, dist_object, index) {
   # store the distance object element
   dist_object_element <- dist_object[[dist_object_name]]
@@ -230,7 +258,12 @@ subset_dist_object <- function(dist_object_name, dist_object, index) {
   dist_sub
 }
 
-# find the mask matrix list for each network
+#' Find the mask matrix list for each network
+#'
+#' @param distjunc_list Create list of mask matrices (which zero out tailup
+#'   and taildown covariance for observations on separate networks).
+#'
+#' @noRd
 get_mask_matlist <- function(distjunc_list) {
   mask_list <- lapply(distjunc_list, function(x) {
     n_i <- dim(x)[[1]]
@@ -238,7 +271,11 @@ get_mask_matlist <- function(distjunc_list) {
   })
 }
 
-# find the a matrix list (largest stream distance) for each network
+#' Find the a matrix list for each network
+#'
+#' @param distjunc_list Create list of a matrices (largest stream distance) for each network.
+#'
+#' @noRd
 get_a_matlist <- function(distjunc_list) {
   # find a matrix list
   a_matlist <- lapply(distjunc_list, function(x) {
@@ -247,7 +284,11 @@ get_a_matlist <- function(distjunc_list) {
   })
 }
 
-# find the b matrix list (smallest stream distance) for each network
+#' Find the b matrix list for each network
+#'
+#' @param distjunc_list Create list of a matrices (smallest stream distance) for each network.
+#'
+#' @noRd
 get_b_matlist <- function(distjunc_list) {
   # find a matrix list
   b_matlist <- lapply(distjunc_list, function(x) {
@@ -256,13 +297,21 @@ get_b_matlist <- function(distjunc_list) {
   })
 }
 
-# find the hydrologic distance matrix list for each network
+#' Find the hydrologic distance matrix list for each network
+#'
+#' @param distjunc_list Create list of matrices (hydrologic stream distance) for each network.
+#'
+#' @noRd
 get_hydro_matlist <- function(distjunc_list) {
   # find hydro matrix list
   hydro_matlist <- lapply(distjunc_list, function(x) x + t(x))
 }
 
-# find the additive matrix list for each network
+#' Find the w matrix list for each network
+#'
+#' @param distjunc_list Create list of w matrices (additive function weight) for each network.
+#'
+#' @noRd
 get_w_matlist <- function(ssn.object, additive, network_index, dist_order, b_matlist, mask_matlist, newdata_name = NULL) {
   if (is.null(newdata_name)) {
     additive_val <- as.numeric(ssn.object$obs[[additive]]) # remember a character here
diff --git a/R/get_dist_pred_object.R b/R/get_dist_pred_object.R
index 581e88e..0d46a28 100644
--- a/R/get_dist_pred_object.R
+++ b/R/get_dist_pred_object.R
@@ -1,4 +1,10 @@
-# parent function to get the distance object
+#' Get prediction distance object
+#'
+#' @param object Data object.
+#' @param newdata_name Name of the prediction data set.
+#' @param initial_object Initial value object.
+#'
+#' @noRd
 get_dist_pred_object <- function(object, newdata_name, initial_object) {
   # get netgeom
   netgeom <- ssn_get_netgeom(object$ssn.object$obs, reformat = TRUE)
@@ -84,10 +90,9 @@ get_dist_pred_object <- function(object, newdata_name, initial_object) {
   dist_pred_object
 }
 
-# get the list of prediction matrices
+# vectorized version of get_dist_pred_object
 get_dist_pred_matlist <- function(ssn.object, newdata_name, initial_object, additive,
                                   order_list_pred) {
-
   # store network indices and orders
   network_index <- order_list_pred$network_index
   dist_order <- order_list_pred$dist_order
@@ -185,7 +190,13 @@ get_dist_pred_matlist <- function(ssn.object, newdata_name, initial_object, addi
   dist_pred_matlist
 }
 
-# get list of distance junction matrices
+#' Get list of prediction distance junction matrices
+#'
+#' @param ssn.object SSN object.
+#' @param newdata_name Name of the prediction data set.
+#' @param order_list_pred The order for observations in the prediction data set.
+#'
+#' @noRd
 get_distjunc_pred_matlist <- function(ssn.object, newdata_name, order_list_pred) {
   # check and make sure there is missing data to predict
   if (newdata_name %in% names(ssn.object$preds) && NROW(ssn.object$preds[[newdata_name]]) == 0) {
@@ -303,12 +314,23 @@ get_distjunc_pred_matlist <- function(ssn.object, newdata_name, order_list_pred)
   distjunc_pred_matlist <- list(distjunca = distjunca, distjuncb = distjuncb)
 }
 
+
+#' Get mask prediction distance matrices
+#'
+#' @param distjunc_pred_matlist
+#'
+#' @noRd
 get_mask_pred_matlist <- function(distjunc_pred_matlist) {
   mask_pred_list <- lapply(distjunc_pred_matlist$distjunca, function(x) {
     Matrix::Matrix(1, nrow = dim(x)[1], ncol = dim(x)[2], sparse = TRUE)
   })
 }
 
+#' Get a prediction distance matrices
+#'
+#' @param distjunc_pred_matlist
+#'
+#' @noRd
 get_a_pred_matlist <- function(distjunc_pred_matlist) {
   a_matrix_list <- mapply(
     a = distjunc_pred_matlist$distjunca,
@@ -320,6 +342,11 @@ get_a_pred_matlist <- function(distjunc_pred_matlist) {
   )
 }
 
+#' Get b prediction distance matrices
+#'
+#' @param distjunc_pred_matlist
+#'
+#' @noRd
 get_b_pred_matlist <- function(distjunc_pred_matlist) {
   a_matrix_list <- mapply(
     a = distjunc_pred_matlist$distjunca,
@@ -331,6 +358,11 @@ get_b_pred_matlist <- function(distjunc_pred_matlist) {
   )
 }
 
+#' Get hydrologic prediction distance matrices
+#'
+#' @param distjunc_pred_matlist
+#'
+#' @noRd
 get_hydro_pred_matlist <- function(distjunc_pred_matlist) {
   a_matrix_list <- mapply(
     a = distjunc_pred_matlist$distjunca,
@@ -342,12 +374,17 @@ get_hydro_pred_matlist <- function(distjunc_pred_matlist) {
   )
 }
 
+#' Get w prediction distance matrices
+#'
+#' @param distjunc_pred_matlist
+#'
+#' @noRd
 get_w_pred_matlist <- function(ssn.object, newdata_name, order_list_pred, additive, b_pred_matlist, mask_pred_matlist) {
   # make list
   network_index_obs <- as.numeric(as.character(order_list_pred$network_index))
   network_index_pred <- as.numeric(as.character(order_list_pred$network_index_pred))
   network_index_vals <- sort(unique(c(network_index_obs, network_index_pred)))
- # network_index_integer <- seq_along(network_index_vals)
+  # network_index_integer <- seq_along(network_index_vals)
 
   dist_order <- order_list_pred$dist_order
 
diff --git a/R/get_dist_predbk_object.R b/R/get_dist_predbk_object.R
index 9b18379..581cefa 100644
--- a/R/get_dist_predbk_object.R
+++ b/R/get_dist_predbk_object.R
@@ -1,4 +1,10 @@
-# parent function to get the distance object
+#' Get prediction by prediction distance matrix for block Kriging
+#'
+#' @param object Model object.
+#' @param newdata_name Name of prediction data set.
+#' @param initial_object Initial value object.
+#'
+#' @noRd
 get_dist_predbk_object <- function(object, newdata_name, initial_object) {
   # get netgeom
   netgeom <- ssn_get_netgeom(object$ssn.object$preds[[newdata_name]], reformat = TRUE)
@@ -62,6 +68,7 @@ get_dist_predbk_object <- function(object, newdata_name, initial_object) {
   dist_object
 }
 
+# vectorized version of get_dist_predbk_object
 get_dist_predbk_matlist <- function(ssn.object, newdata_name, initial_object, additive,
                                     order_list) {
   network_index <- order_list$network_index
diff --git a/R/get_eigenprods.R b/R/get_eigenprods.R
index c61aa27..34b1f8e 100644
--- a/R/get_eigenprods.R
+++ b/R/get_eigenprods.R
@@ -1,3 +1,13 @@
+#' Get eigenproducts for relevant covariance computations
+#'
+#' @param cov_matrix A covariance matrix.
+#' @param X Data model matrix.
+#' @param y Data response value.
+#' @param ones A vector of ones with the same dimension as y
+#'
+#' @return A list with various products with eigenvectors and eigenvalues that
+#'   return products with square root matrices.
+#' @noRd
 get_eigenprods <- function(cov_matrix, X, y, ones) {
   eig <- eigen(Matrix::forceSymmetric(cov_matrix))
   SigInv_p1 <- t(t(eig$vectors) * 1 / eig$values)
@@ -17,6 +27,7 @@ get_eigenprods <- function(cov_matrix, X, y, ones) {
   )
 }
 
+# a vectorized version of get_eigenprods
 get_eigenprods_parallel <- function(cluster_list) {
   cov_matrix <- cluster_list$c
   X <- cluster_list$x
@@ -25,6 +36,16 @@ get_eigenprods_parallel <- function(cluster_list) {
   get_eigenprods(cov_matrix, X, y, o)
 }
 
+#' Get eigenproducts for relevant covariance computations (for glms)
+#'
+#' @param cov_matrix A covariance matrix.
+#' @param X Data model matrix.
+#' @param y Data response value.
+#' @param ones A vector of ones with the same dimension as y
+#'
+#' @return A list with various products with eigenvectors and eigenvalues that
+#'   return products with square root matrices.
+#' @noRd
 get_eigenprods_glm <- function(cov_matrix, X, y, ones) {
   eig <- eigen(Matrix::forceSymmetric(cov_matrix))
   SigInv_p1 <- t(t(eig$vectors) * 1 / eig$values)
@@ -46,6 +67,7 @@ get_eigenprods_glm <- function(cov_matrix, X, y, ones) {
   )
 }
 
+# a vectorized version of get_eigenprods_glm
 get_eigenprods_glm_parallel <- function(cluster_list) {
   cov_matrix <- cluster_list$c
   X <- cluster_list$x
diff --git a/R/get_model_stats.R b/R/get_model_stats.R
index 5923d13..6783762 100644
--- a/R/get_model_stats.R
+++ b/R/get_model_stats.R
@@ -1,3 +1,10 @@
+#' Get relevant model fit statistics and diagnostics
+#'
+#' @param cov_est_object Covariance parameter estimation object.
+#' @param data_object Data object.
+#' @param estmethod Estimation method.
+#'
+#' @noRd
 get_model_stats <- function(cov_est_object, data_object, estmethod) {
   # store the covariance matrix list
   cov_matrix_list <- get_cov_matrix_list(cov_est_object$params_object, data_object)
@@ -143,6 +150,10 @@ get_model_stats <- function(cov_est_object, data_object, estmethod) {
   )
 }
 
+###############################################################################
+############### helpers to store relevant statistics as list objects
+###############################################################################
+
 get_coefficients <- function(betahat, params_object) {
   # store fixed effect and random coefficients as list
   list(fixed = betahat, params_object = params_object)
diff --git a/R/get_model_stats_glm.R b/R/get_model_stats_glm.R
index 5a361ac..2073c44 100644
--- a/R/get_model_stats_glm.R
+++ b/R/get_model_stats_glm.R
@@ -1,3 +1,10 @@
+#' Get relevant model fit statistics and diagnostics for glms
+#'
+#' @param cov_est_object Covariance parameter estimation object.
+#' @param data_object Data object.
+#' @param estmethod Estimation method.
+#'
+#' @noRd
 get_model_stats_glm <- function(cov_est_object, data_object, estmethod) {
   cov_matrix_list <- get_cov_matrix_list(cov_est_object$params_object, data_object)
 
@@ -212,6 +219,10 @@ get_model_stats_glm <- function(cov_est_object, data_object, estmethod) {
   )
 }
 
+###############################################################################
+############### helpers to store relevant statistics as list objects
+###############################################################################
+
 get_coefficients_glm <- function(betahat, params_object) {
   list(fixed = betahat, params_object = params_object)
 }
diff --git a/R/get_sf_obj.R b/R/get_sf_obj.R
new file mode 100644
index 0000000..9351d6b
--- /dev/null
+++ b/R/get_sf_obj.R
@@ -0,0 +1,57 @@
+#' Read relevant sf objects from user shalpefiles or geopackages
+#'
+#' @param fn Path to shapefile or geopackage
+#'
+#' @noRd
+get_sf_obj <- function(fn) {
+  f.ext.shp <- substr(fn, nchar(fn) - 3, nchar(fn)) == ".shp"
+  f.ext.gpkg <- substr(fn, nchar(fn) - 4, nchar(fn)) == ".gpkg"
+
+  ## Check if shapefile or geopackage exists----------
+  ## If shapefile specified, check if it exists
+  if (f.ext.shp) {
+    shp.exists <- file.exists(fn)
+    gpkg.exists <- FALSE
+
+    fn <- substr(fn, 1, nchar(fn) - 4)
+  }
+  ## If geopackage specified, check if it exists
+  if (f.ext.gpkg) {
+    shp.exists <- FALSE
+    gpkg.exists <- file.exists(fn)
+
+    fn <- substr(fn, 1, nchar(fn) - 5)
+  }
+
+  ## If no file extension specified, check for both
+  if (!f.ext.shp & !f.ext.gpkg) {
+    shp.exists <- file.exists(paste0(fn, ".shp"))
+    gpkg.exists <- file.exists(paste0(fn, ".gpkg"))
+  }
+
+  ## If both exist without file extension, return error
+  if (gpkg.exists & shp.exists) {
+    ## sfobj<- st_read(paste0(fn, ".gpkg"), quiet = TRUE)
+    stop(paste0(
+      dirname(fn), " contains both ", basename(fn), ".shp and ",
+      basename(fn), ".gpkg."
+    ))
+  }
+  ## Read in shapefile
+  if (shp.exists & !gpkg.exists) {
+    sfobj <- st_read(paste0(fn, ".shp"), quiet = TRUE)
+  }
+  ## Read in geopackage
+  if (gpkg.exists & !shp.exists) {
+    sfobj <- st_read(paste0(fn, ".gpkg"), quiet = TRUE)
+  }
+  ## Neither exists
+  if (!gpkg.exists & !shp.exists) {
+    stop(paste0(
+      basename(fn), " not found in ", dirname(fn), ". ",
+      basename(fn), " must reside in path and be in .shp or .gpkg format."
+    ))
+  }
+
+  return(sfobj)
+}
diff --git a/R/gloglik.R b/R/gloglik.R
index b27a9f5..d7fa908 100644
--- a/R/gloglik.R
+++ b/R/gloglik.R
@@ -1,3 +1,11 @@
+#' Compute log likeihood
+#'
+#' @param par Optimization parameters
+#' @param orig2optim_object An optimization object that performs necessary transformations
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#'
+#' @noRd
 gloglik <- function(par, orig2optim_object, data_object, estmethod) {
   # find parameters on the original scale
   cov_orig_val <- optim2orig(orig2optim_object, par)
diff --git a/R/gloglik_products.R b/R/gloglik_products.R
index 1373a5e..7589428 100644
--- a/R/gloglik_products.R
+++ b/R/gloglik_products.R
@@ -1,3 +1,10 @@
+#' Get products needed to evaluate log likelihood
+#'
+#' @param params_object Object with covariance parameters.
+#' @param data_object Data object
+#' @param estmethod Estimation Method
+#'
+#' @noRd
 gloglik_products <- function(params_object, data_object, estmethod) {
   cov_matrix_list <- get_cov_matrix_list(params_object, data_object)
   # cholesky products (no local)
@@ -60,6 +67,13 @@ gloglik_products <- function(params_object, data_object, estmethod) {
   }
 }
 
+#' Get minus twice the log likelihood
+#'
+#' @param gloglik_products The relevant log likelihood products
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#'
+#' @noRd
 get_minustwologlik <- function(gloglik_products, data_object, estmethod) {
   if (estmethod == "reml") {
     minustwologlik <- as.numeric(gloglik_products$l1 + gloglik_products$l2 + gloglik_products$l3 + (data_object$n - data_object$p) * log(2 * pi))
diff --git a/R/initial.R b/R/initial.R
index afc97b0..cbcd6ee 100644
--- a/R/initial.R
+++ b/R/initial.R
@@ -220,6 +220,18 @@ nugget_initial <- function(nugget_type, nugget, known) {
   new_nugget_initial
 }
 
+#' A helper to create an initial value object
+#'
+#' @param tailup_type Tailup covariance type
+#' @param taildown_type Taildown covariance type
+#' @param euclid_type Euclidean covariance type
+#' @param nugget_type Nugget covariance type
+#' @param tailup_initial Tailup initial object (if specified)
+#' @param taildown_initial Taildown initial object (if specified)
+#' @param euclid_initial Euclidean initial object (if specified)
+#' @param nugget_initial Nugget initial object (if specified)
+#'
+#' @noRd
 get_initial_object <- function(tailup_type, taildown_type, euclid_type, nugget_type,
                                tailup_initial, taildown_initial, euclid_initial, nugget_initial) {
   if (is.null(tailup_initial)) tailup_initial <- tailup_initial(tailup_type)
@@ -241,6 +253,20 @@ get_initial_object <- function(tailup_type, taildown_type, euclid_type, nugget_t
   initial_object
 }
 
+#' A helper to create an initial value glm object
+#'
+#' @param tailup_type Tailup covariance type
+#' @param taildown_type Taildown covariance type
+#' @param euclid_type Euclidean covariance type
+#' @param nugget_type Nugget covariance type
+#' @param tailup_initial Tailup initial object (if specified)
+#' @param taildown_initial Taildown initial object (if specified)
+#' @param euclid_initial Euclidean initial object (if specified)
+#' @param nugget_initial Nugget initial object (if specified)
+#' @param family The generalized linear model family
+#' @param dispersion_inital Dispersion initial object
+#'
+#' @noRd
 get_initial_object_glm <- function(tailup_type, taildown_type, euclid_type, nugget_type,
                                    tailup_initial, taildown_initial, euclid_initial, nugget_initial,
                                    family, dispersion_initial) {
@@ -261,6 +287,12 @@ get_initial_object_glm <- function(tailup_type, taildown_type, euclid_type, nugg
   initial_object
 }
 
+#' Create a vector that specifies whether covariance parameters are to be assumed known
+#'
+#' @param params  A covariance parameter vector
+#' @param known Which covariance parameters assumed known
+#'
+#' @noRd
 get_is_known <- function(params, known) {
   if (missing(known)) {
     is_known <- rep(FALSE, length(params))
diff --git a/R/initial_NA.R b/R/initial_NA.R
index 426da34..95f1f1a 100644
--- a/R/initial_NA.R
+++ b/R/initial_NA.R
@@ -1,3 +1,9 @@
+#' An initial value object with NA values that indicate where estimation is required
+#'
+#' @param initial_object Initial value object
+#' @param data_object Data object
+#'
+#' @noRd
 get_initial_NA_object <- function(initial_object, data_object) {
   # get each initial NA object
   tailup_initial_NA_val <- tailup_initial_NA(initial_object$tailup_initial)
@@ -19,7 +25,11 @@ get_initial_NA_object <- function(initial_object, data_object) {
   initial_NA_object
 }
 
-# the tailup initial NA object
+#' Tailup initial NA object
+#'
+#' @param initial An initial value specification
+#'
+#' @noRd
 tailup_initial_NA <- function(initial) {
   tailup_names <- c("de", "range")
 
@@ -37,7 +47,11 @@ tailup_initial_NA <- function(initial) {
   new_initial
 }
 
-# the taildown initial NA object
+#' Taildown initial NA object
+#'
+#' @param initial An initial value specification
+#'
+#' @noRd
 taildown_initial_NA <- function(initial) {
   taildown_names <- c("de", "range")
 
@@ -55,7 +69,11 @@ taildown_initial_NA <- function(initial) {
   new_initial
 }
 
-# the euclid initial NA object
+#' Euclidean initial NA object
+#'
+#' @param initial An initial value specification
+#'
+#' @noRd
 euclid_initial_NA <- function(initial, data_object) {
   euclid_names <- c("de", "range", "rotate", "scale")
 
@@ -79,6 +97,11 @@ euclid_initial_NA <- function(initial, data_object) {
   new_initial
 }
 
+#' Nugget initial NA object
+#'
+#' @param initial An initial value specification
+#'
+#' @noRd
 nugget_initial_NA <- function(initial) {
   nugget_names <- c("nugget")
 
@@ -96,6 +119,14 @@ nugget_initial_NA <- function(initial) {
   new_initial
 }
 
+#' Insert NA values when covariance parameters assumed unknown
+#'
+#' @param names The names to assume unknown
+#' @param val_default The default value of values (NA)
+#' @param known_default The default value of whether parameters are known
+#' @param initial An initial value specification
+#'
+#' @noRd
 insert_initial_NA <- function(names, val_default, known_default, initial) {
   # find names with known initial values
   names_replace <- setdiff(names, names(initial$initial))
@@ -110,6 +141,12 @@ insert_initial_NA <- function(names, val_default, known_default, initial) {
   initial
 }
 
+#' Insert NA values when random effect parameters assumed unknown
+#'
+#' @param randcov_initial Random effect initial object
+#' @param data_object Data object
+#'
+#' @noRd
 randcov_initial_NA <- function(randcov_initial, data_object) {
   if (is.null(randcov_initial)) {
     randcov_initial <- NULL
@@ -134,6 +171,12 @@ randcov_initial_NA <- function(randcov_initial, data_object) {
   randcov_initial
 }
 
+#' An initial value object with NA values that indicate where estimation is required for glms
+#'
+#' @param initial_object Initial value object
+#' @param data_object Data object
+#'
+#' @noRd
 get_initial_NA_object_glm <- function(initial_object, data_object) {
   tailup_initial_NA_val <- tailup_initial_NA(initial_object$tailup_initial)
   taildown_initial_NA_val <- taildown_initial_NA(initial_object$taildown_initial)
@@ -154,6 +197,12 @@ get_initial_NA_object_glm <- function(initial_object, data_object) {
   initial_NA_object
 }
 
+#' Insert NA values when dispersion parameters assumed unknown
+#'
+#' @param initial Initial value object
+#' @param data_object Data object
+#'
+#' @noRd
 dispersion_initial_NA <- function(initial, data_object) {
   dispersion_names <- c("dispersion")
 
diff --git a/R/laploglik.R b/R/laploglik.R
index ba40ad7..0080240 100644
--- a/R/laploglik.R
+++ b/R/laploglik.R
@@ -1,3 +1,11 @@
+#' Evaluate Laplace log likelihood
+#'
+#' @param par Current optimization parameter
+#' @param orig2optim_object Optimization object that controls transformations
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#'
+#' @noRd
 laploglik <- function(par, orig2optim_object, data_object, estmethod) {
   # find covariance parameters on original scale
   cov_orig_val <- optim2orig_glm(orig2optim_object, par)
diff --git a/R/laploglik_products.R b/R/laploglik_products.R
index ca9396d..e91f337 100644
--- a/R/laploglik_products.R
+++ b/R/laploglik_products.R
@@ -1,3 +1,10 @@
+#' Get products involved in Laplace log likelihood (glms)
+#'
+#' @param params_object Covariance parameter object
+#' @param data_object Data object
+#' @param estmethod Estimation Method
+#'
+#' @noRd
 laploglik_products <- function(params_object, data_object, estmethod) {
   cov_matrix_list <- get_cov_matrix_list(params_object, data_object)
   # cholesky products (no local)
@@ -81,6 +88,13 @@ laploglik_products <- function(params_object, data_object, estmethod) {
   }
 }
 
+#' Get minus twice the Laplace log likelihood
+#'
+#' @param laploglik_products Laplace log likelihood products
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#'
+#' @noRd
 get_minustwolaploglik <- function(laploglik_products, data_object, estmethod) {
   if (estmethod == "reml") {
     minustwolaploglik <- as.numeric(laploglik_products$l00 + laploglik_products$l01 + laploglik_products$l1 +
@@ -96,6 +110,18 @@ get_minustwolaploglik <- function(laploglik_products, data_object, estmethod) {
 }
 
 
+#' Get prediction and hessian of latent effects
+#'
+#' @param data_object Data object
+#' @param dispersion Dispersion parameter
+#' @param SigInv_list List of inverse covariance matrices for each block
+#' @param SigInv_X Product between inverse covariance matrix and model matrix
+#' @param cov_betahat Covariance of fixed effects
+#' @param cov_betahat_Inv Inverse covariance of fixed effects (precision)
+#' @param estmethod Estimation method
+#' @param ret_mHInv Should -(H)^-1 be retained for later use?
+#'
+#' @noRd
 get_w_and_H <- function(data_object, dispersion, SigInv_list, SigInv_X, cov_betahat, cov_betahat_Inv, estmethod, ret_mHInv = FALSE) {
   family <- data_object$family
   SigInv <- Matrix::bdiag(SigInv_list)
@@ -230,7 +256,15 @@ get_w_and_H <- function(data_object, dispersion, SigInv_list, SigInv_X, cov_beta
   w_and_H_list
 }
 
-# gradient of w (lowcase d)
+#' Get the gradient of w (called d)
+#'
+#' @param family The glm family
+#' @param w The latent effect (link mean)
+#' @param y The response
+#' @param size Number of trails (for binomial response)
+#' @param dispersion Dispersion parameter
+#'
+#' @noRd
 get_d <- function(family, w, y, size, dispersion) {
   if (family == "poisson") {
     d <- -exp(w) + y
@@ -251,7 +285,15 @@ get_d <- function(family, w, y, size, dispersion) {
   d
 }
 
-# Hessian of w (cap D)
+#' Get the Hessian of w (called D)
+#'
+#' @param family The glm family
+#' @param w The latent effect (link mean)
+#' @param y The response
+#' @param size Number of trails (for binomial response)
+#' @param dispersion Dispersion parameter
+#'
+#' @noRd
 get_D <- function(family, w, y, size, dispersion) {
   w <- as.vector(w)
 
@@ -275,6 +317,13 @@ get_D <- function(family, w, y, size, dispersion) {
   D <- Diagonal(x = D_vec)
 }
 
+#' Get initial values for w
+#'
+#' @param w The latent effect (link mean)
+#' @param y The response
+#' @param dispersion Dispersion parameter
+#'
+#' @noRd
 get_w_init <- function(family, y, dispersion) {
   if (family == "poisson") {
     w_init <- 0.5 * log(y + 1)
@@ -292,6 +341,15 @@ get_w_init <- function(family, y, dispersion) {
   w_init
 }
 
+#' Get glm distribution piece of Laplace log likelihood
+#'
+#' @param family The glm family
+#' @param w The latent effect (link mean)
+#' @param y The response
+#' @param size Number of trails (for binomial response)
+#' @param dispersion Dispersion parameter
+#'
+#' @noRd
 get_l00 <- function(family, w, y, size, dispersion) {
   w <- as.vector(w)
   y <- as.vector(y)
diff --git a/R/local.R b/R/local.R
index 0702067..7abc85d 100644
--- a/R/local.R
+++ b/R/local.R
@@ -1,3 +1,12 @@
+#' Parameters that control spatial indexing (not yet implemented for spatial indexing
+#'   and function calls to this just act as placeholders)
+#'
+#' @param local Placeholder
+#' @param data Placeholder
+#' @param n  Placeholder
+#' @param partition_factor Placeholder
+#'
+#' @noRd
 get_local_list_estimation <- function(local, data, n, partition_factor) {
   # size can be an integer and sets group size
   # alternatively, set the number of groups
@@ -83,6 +92,14 @@ get_local_list_estimation <- function(local, data, n, partition_factor) {
   local
 }
 
+#' A helper to get spatial indexes (not yet implemented for spatial indexing
+#'   and function calls to this just act as placeholders)
+#'
+#' @param local Placeholder
+#' @param data Placeholder
+#' @param n  Placeholder
+#'
+#' @noRd
 get_local_estimation_index <- function(local, data, n) {
   # if (local$method == "random") {
   #   index <- sample(rep(seq_len(local$groups), times = local$size)[seq_len(n)])
@@ -97,6 +114,13 @@ get_local_estimation_index <- function(local, data, n) {
   # index
 }
 
+
+#' Parameters that control local neighborhood prediction(not yet implemented
+#'   and function calls to this just act as placeholders)
+#'
+#' @param local Placeholder
+#'
+#' @noRd
 get_local_list_prediction <- function(local) {
   # set local neighborhood size
   # method can be "all" (for all data), "distance" (for local distance neighborhoods)
diff --git a/R/loocv.R b/R/loocv.R
index 00f1769..a887cea 100644
--- a/R/loocv.R
+++ b/R/loocv.R
@@ -112,6 +112,15 @@ loocv.ssn_lm <- function(object, cv_predict = FALSE, se.fit = FALSE, ...) {
     return(loocv_out)
   }
 }
+
+#' A helper to get the leave one out predictions
+#'
+#' @param object Model object
+#' @param cv_predict Whether cross validation predictions should be returned
+#' @param se.fit Whether the standard error should be returned
+#' @param ... Additional arguments
+#'
+#' @noRd
 get_loocv.ssn_lm <- function(object, cv_predict = FALSE, se.fit = FALSE, ...) {
   # local stuff (save for later)
   # if (missing(local)) local <- NULL
@@ -206,6 +215,7 @@ get_loocv.ssn_lm <- function(object, cv_predict = FALSE, se.fit = FALSE, ...) {
   cv_output
 }
 
+# not currently used (but will be later when local prediction implemented)
 loocv_local <- function(row, se.fit, cov_matrix_val, total_var, Xmat, y, betahat, cov_betahat, local) {
   # new_cov_matrix_val <- cov_matrix_val[-row, -row, drop = FALSE]
   # new_cov_vector_val <- cov_matrix_val[row, -row, drop = FALSE]
@@ -241,6 +251,18 @@ loocv_local <- function(row, se.fit, cov_matrix_val, total_var, Xmat, y, betahat
   # pred_list
 }
 
+#' Get leave one out cross validation value
+#'
+#' @param obs Index of observed data
+#' @param Sig Covariance matrix
+#' @param SigInv Inverse covariance matrix
+#' @param Xmat Model matrix
+#' @param y response
+#' @param yX Matrix containing model matrix and response
+#' @param SigInv_yX Product of inverse covariance matrix with model matrix and response
+#' @param se.fit Whether to return standard errors of leave one out predictions
+#'
+#' @noRd
 get_loocv <- function(obs, Sig, SigInv, Xmat, y, yX, SigInv_yX, se.fit) {
   SigInv_mm <- SigInv[obs, obs] # a constant
   SigInv_om <- SigInv[-obs, obs, drop = FALSE]
diff --git a/R/loocv_glm.R b/R/loocv_glm.R
index 569f9e3..afac62d 100644
--- a/R/loocv_glm.R
+++ b/R/loocv_glm.R
@@ -43,6 +43,14 @@ loocv.ssn_glm <- function(object, cv_predict = FALSE, se.fit = FALSE, ...) {
   }
 }
 
+#' A helper to get the leave one out predictions for glms
+#'
+#' @param object Model object
+#' @param cv_predict Whether cross validation predictions should be returned
+#' @param se.fit Whether the standard error should be returned
+#' @param ... Additional arguments
+#'
+#' @noRd
 get_loocv.ssn_glm <- function(object, cv_predict = FALSE, se.fit = FALSE, ...) {
   # turn local off for now
   local <- FALSE
@@ -151,6 +159,14 @@ get_loocv.ssn_glm <- function(object, cv_predict = FALSE, se.fit = FALSE, ...) {
   cv_output
 }
 
+#' Helper to get each row leave one out prediction
+#'
+#' @param row The row of interest
+#' @param object Model object
+#' @param se.fit Whether to return the standard error
+#' @param local_list Local list (not yet functional)
+#'
+#' @noRd
 loocv_local_glm <- function(row, object, se.fit, local_list) {
   object$fitted$link <- object$fitted$link[-row] # w needs to be subset
   newdata <- object$obdata[row, , drop = FALSE]
@@ -158,6 +174,19 @@ loocv_local_glm <- function(row, object, se.fit, local_list) {
   predict(object, newdata = newdata, se.fit = se.fit, local = local_list)
 }
 
+#' Get leave one out cross validation value for glm
+#'
+#' @param obs Index of observed data
+#' @param Sig Covariance matrix
+#' @param SigInv Inverse covariance matrix
+#' @param Xmat Model matrix
+#' @param w The predicted latent effects
+#' @param wX Matrix containing w and response
+#' @param SigInv_yX Product of inverse covariance matrix with w and response
+#' @param mHinv The minus of the inverse Hessian of w
+#' @param se.fit Whether to return standard errors of leave one out predictions
+#'
+#' @noRd
 get_loocv_glm <- function(obs, Sig, SigInv, Xmat, w, wX, SigInv_wX, mHinv, se.fit) {
   SigInv_mm <- SigInv[obs, obs] # a constant
   SigInv_om <- SigInv[-obs, obs, drop = FALSE]
diff --git a/R/names.SSN.R b/R/names.SSN.R
deleted file mode 100644
index 3c35396..0000000
--- a/R/names.SSN.R
+++ /dev/null
@@ -1,31 +0,0 @@
-#' names SSN object
-#'
-#' @description Extract and print names from the SSN object
-#'
-#' @param x An SSN object.
-#' @param ... Other arguments. Not used (needed for generic consistency).
-#'
-#' @return Print variable names to console
-#'
-#' @name names.SSN
-#' @method names SSN
-#' @export
-
-
-names.SSN <- function(x, ...) {
-  d <- x$obs
-  no <- names(d)
-  np <- length(x$preds)
-  namesList <- vector("list", np + 1)
-  namesList[[1]] <- no
-  names4List <- "obs"
-  if (np > 0) {
-    for (i in seq_len(np)) {
-      names4List <- c(names4List, names(x$preds)[[i]])
-      d <- x$preds[[i]]
-      namesList[[i + 1]] <- names(d)
-    }
-  }
-  names(namesList) <- names4List
-  return(namesList)
-}
diff --git a/R/optim.R b/R/optim.R
index aedc390..07c3e64 100644
--- a/R/optim.R
+++ b/R/optim.R
@@ -97,6 +97,15 @@ check_optim_method <- function(optim_par, optim_dotlist) {
   optim_dotlist
 }
 
+#' Get parameters to optimize over in optim (remove known parameters) for glms
+#'
+#' @param spcov_orig2optim A \code{spcov_orig2optim} object
+#' @param dispersion_orig2optim A \code{dispersion_orig2optim} object
+#' @param randcov_orig2optim A \code{randcov_orig2optim} object
+#'
+#' @return The parameters to optimize over in optim
+#'
+#' @noRd
 get_optim_par_glm <- function(spcov_orig2optim, dispersion_orig2optim, randcov_orig2optim = NULL) {
   if (is.null(randcov_orig2optim)) {
     par <- spcov_orig2optim$value[!spcov_orig2optim$is_known]
diff --git a/R/optim2orig.R b/R/optim2orig.R
index b297bf8..34786db 100644
--- a/R/optim2orig.R
+++ b/R/optim2orig.R
@@ -1,3 +1,9 @@
+#' Transform parameters from optim to original scale
+#'
+#' @param orig2optim_object An object that contains the parameters on the original scale
+#' @param par Current parameter values
+#'
+#' @noRd
 optim2orig <- function(orig2optim_object, par) {
   # fill optim parameter vector with known parameters
   fill_optim_par_val <- fill_optim_par(orig2optim_object, par)
@@ -39,6 +45,12 @@ optim2orig <- function(orig2optim_object, par) {
   list(orig_ssn = fill_orig_val_ssn, orig_randcov = fill_orig_val_randcov)
 }
 
+#' Transform parameters from optim to original scale for glms
+#'
+#' @param orig2optim_object An object that contains the parameters on the original scale
+#' @param par Current parameter values
+#'
+#' @noRd
 optim2orig_glm <- function(orig2optim_object, par) {
   fill_optim_par_val <- fill_optim_par(orig2optim_object, par)
 
diff --git a/R/orig2optim.R b/R/orig2optim.R
index 9cbb7f0..2050975 100644
--- a/R/orig2optim.R
+++ b/R/orig2optim.R
@@ -1,3 +1,8 @@
+#' Transform parameters from original to optim scale
+#'
+#' @param initial_object Initial value object
+#'
+#' @noRd
 orig2optim <- function(initial_object) {
   # find parameters on optim (transformed) scale
 
@@ -109,7 +114,11 @@ orig2optim <- function(initial_object) {
   )
 }
 
-
+#' Transform parameters from original to optim scale for glms
+#'
+#' @param initial_object Initial value object
+#'
+#' @noRd
 orig2optim_glm <- function(initial_object) {
   # tailup
   tailup_de <- initial_object$tailup_initial$initial[["de"]]
diff --git a/R/params.R b/R/params.R
index fd07414..8f84e23 100644
--- a/R/params.R
+++ b/R/params.R
@@ -162,6 +162,11 @@ get_params_object <- function(classes, cov_orig_val) {
   params_object
 }
 
+#' Get the initial values that are known
+#'
+#' @param initial_object Initial value object
+#'
+#' @noRd
 get_params_object_known <- function(initial_object) {
   classes <- c(
     tailup = class(initial_object$tailup_initial), taildown = class(initial_object$taildown_initial),
@@ -208,6 +213,12 @@ get_params_object_known <- function(initial_object) {
   params_object
 }
 
+#' Get the parameter object for glms
+#'
+#' @param initial_object Initial value object
+#' @param cov_orig_val The original covariance parameter values
+#'
+#' @noRd
 get_params_object_glm <- function(classes, cov_orig_val) {
   classes <- remove_covtype(classes)
 
@@ -266,6 +277,11 @@ get_params_object_glm <- function(classes, cov_orig_val) {
   params_object
 }
 
+#' Get known parameters for glms
+#'
+#' @param initial_object Initial value object
+#'
+#' @noRd
 get_params_object_glm_known <- function(initial_object) {
   classes <- c(
     tailup = class(initial_object$tailup_initial), taildown = class(initial_object$taildown_initial),
@@ -319,6 +335,12 @@ get_params_object_glm_known <- function(initial_object) {
   params_object
 }
 
+#' Get grid of possible initial values
+#'
+#' @param cov_grid_vector A configuration of initial covariance parameters
+#' @param initial_NA_object The inital NA object (which has information on the known parameters)
+#'
+#' @noRd
 get_params_object_grid <- function(cov_grid_vector, initial_NA_object) {
   classes <- c(
     tailup = class(initial_NA_object$tailup_initial), taildown = class(initial_NA_object$taildown_initial),
@@ -368,6 +390,12 @@ get_params_object_grid <- function(cov_grid_vector, initial_NA_object) {
   )
 }
 
+#' Get grid of possible initial values for glms
+#'
+#' @param cov_grid_vector A configuration of initial covariance parameters
+#' @param initial_NA_object The inital NA object (which has information on the known parameters)
+#'
+#' @noRd
 get_params_object_grid_glm <- function(cov_grid_vector, initial_NA_object) {
   classes <- c(
     tailup = class(initial_NA_object$tailup_initial), taildown = class(initial_NA_object$taildown_initial),
diff --git a/R/partition.R b/R/partition.R
index 6518b3a..f4aab44 100644
--- a/R/partition.R
+++ b/R/partition.R
@@ -26,13 +26,24 @@ partition_matrix <- function(partition_factor = NULL, data) {
 }
 
 
-# could use old version for partition names
+
+#' Get names of partition factors
+#'
+#' @param partition_factor A partition factor
+#'
+#' @noRd
 get_partition_names <- function(partition_factor) {
+  # could use old version for partition names
   # get the partition formula and turn it into a named vector here
   labels_initial <- labels(terms(partition_factor))
   new_labels <- unlist(lapply(labels_initial, get_partition_name))
 }
 
+#' Get adjusted name of a partition factor
+#'
+#' @param label A partition factor name
+#'
+#' @noRd
 get_partition_name <- function(label) {
   if (grepl("|", label, fixed = TRUE)) {
     new_label <- label
diff --git a/R/predict.R b/R/predict.R
index b90f406..0b14f08 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -4,7 +4,9 @@
 #'
 #' @param object A fitted model object from [ssn_lm()] or [ssn_glm()].
 #' @param newdata A character vector that indicates the name of the prediction data set
-#'   in the SSN object for which predictions are desired. If omitted, predictions
+#'   for which predictions are desired (accessible via \code{object$ssn.object$preds}).
+#'   Note that the prediction data must be in the original SSN object used to fit the model.
+#'   If \code{newdata} is omitted, predictions
 #'   for all prediction data sets are returned. Note that the name \code{".missing"}
 #'   indicates the prediction data set that contains the missing observations in the data used
 #'   to fit the model.
@@ -58,8 +60,6 @@
 #' predict(ssn_mod, "pred1km")
 predict.ssn_lm <- function(object, newdata, se.fit = FALSE, interval = c("none", "confidence", "prediction"),
                            level = 0.95, block = FALSE, ...) {
-
-
   # match interval argument so the three display
   interval <- match.arg(interval)
 
@@ -284,6 +284,26 @@ predict.ssn_lm <- function(object, newdata, se.fit = FALSE, interval = c("none",
   }
 }
 
+#' Title
+#'
+#' @param newdata_list A row of prediction data
+#' @param se.fit Whether standard errors should be returned
+#' @param interval The interval type
+#' @param formula Model formula
+#' @param obdata Observed data
+#' @param cov_matrix_val Covariance matrix
+#' @param total_var Total variance in the process
+#' @param cov_lowchol Lower triangular of Cholesky decomposition matrix
+#' @param Xmat Model matrix
+#' @param y Response variable
+#' @param offset A possible offset
+#' @param betahat Fixed effect estimates
+#' @param cov_betahat Covariance of fixed effects
+#' @param contrasts Possible contrasts
+#' @param local Local neighborhood options (not yet implemented)
+#' @param xlevels Levels of explanatory variables
+#'
+#' @noRd
 get_pred <- function(newdata_list, se.fit, interval, formula, obdata, cov_matrix_val, total_var, cov_lowchol,
                      Xmat, y, offset, betahat, cov_betahat, contrasts, local, xlevels) {
   cov_vector_val <- newdata_list$c0
@@ -332,9 +352,6 @@ get_pred <- function(newdata_list, se.fit, interval, formula, obdata, cov_matrix
 
 
 predict_block <- function(object, newdata, se.fit, interval, level, ...) {
-
-
-
   # deal with local (omitted for now)
   # if (missing(local)) local <- NULL
   # if (is.null(local)) local <- FALSE
diff --git a/R/predict_glm.R b/R/predict_glm.R
index 8a34284..8492141 100644
--- a/R/predict_glm.R
+++ b/R/predict_glm.R
@@ -298,6 +298,30 @@ predict.ssn_glm <- function(object, newdata, type = c("link", "response"), se.fi
 
 
 
+#' Get a prediction (and its standard error) for glms
+#'
+#' @param newdata_list A row of prediction data
+#' @param se.fit Whether standard errors should be returned
+#' @param interval The interval type
+#' @param formula Model formula
+#' @param obdata Observed data
+#' @param cov_matrix_val Covariance matrix
+#' @param total_var Total variance in the process
+#' @param cov_lowchol Lower triangular of Cholesky decomposition matrix
+#' @param Xmat Model matrix
+#' @param y Response variable
+#' @param betahat Fixed effect estimates
+#' @param cov_betahat Covariance of fixed effects
+#' @param contrasts Possible contrasts
+#' @param local Local neighborhood options (not yet implemented)
+#' @param family glm family
+#' @param w Latent effects
+#' @param size Number of binomial trials
+#' @param dispersion Dispersion parameter
+#' @param predvar_adjust_ind Whether prediction variance should be adjusted for uncertainty in w
+#' @param xlevels Levels of explanatory variables
+#'
+#' @noRd
 get_pred_glm <- function(newdata_list, se.fit, interval,
                          formula, obdata, cov_matrix_val, total_var, cov_lowchol,
                          Xmat, y, betahat, cov_betahat, contrasts, local,
@@ -342,6 +366,19 @@ get_pred_glm <- function(newdata_list, se.fit, interval,
   pred_list
 }
 
+#' Get weights by which to adjust variances involving w
+#'
+#' @param family glm family
+#' @param Xmat Model matrix
+#' @param y Response variable
+#' @param w Latent effects
+#' @param size Number of binomial trials
+#' @param dispersion Dispersion parameter
+#' @param cov_lowchol Lower triangular of Cholesky decomposition matrix
+#' @param x0 Explanatory variable values for newdata
+#' @param c0 Covariance between observed and newdata
+#'
+#' @noRd
 get_wts_varw <- function(family, Xmat, y, w, size, dispersion, cov_lowchol, x0, c0) {
   SigInv <- chol2inv(t(cov_lowchol)) # works on upchol
   SigInv_X <- SigInv %*% Xmat
diff --git a/R/print.SSN.R b/R/print.SSN.R
index 8f51c24..3bc6f6e 100644
--- a/R/print.SSN.R
+++ b/R/print.SSN.R
@@ -14,6 +14,10 @@ print.SSN <- function(x, ...) {
   cat("Object of class SSN\n\n")
 
   nobs <- dim(x$obs)
+  # no observed data
+  if (is.null(nobs)) {
+    nobs <- c(0, 0)
+  }
   nobs <- matrix(nobs, 1, )
   np <- length(x$preds)
   if (np > 0) {
@@ -26,12 +30,12 @@ print.SSN <- function(x, ...) {
   print(st_bbox(x$edges))
   cat("\n")
 
-  if (nrow(nobs) == 2) {
-    cat("Object also includes", nrow(nobs) - 1, "set of prediction points with", sum(nobs[, 1]) - nobs[1, 1], "locations\n\n")
-  } else if (nrow(nobs) > 2) {
-    cat("Object also includes", nrow(nobs) - 1, "sets of prediction points with a total of", sum(nobs[, 1]) - nobs[1, 1], "locations\n\n")
+  if (np == 1) {
+    cat("Object also includes", 1, "set of prediction points with", sum(nobs[, 1]) - nobs[1, 1], "locations\n\n")
+  } else if (np > 1) {
+    cat("Object also includes", np, "sets of prediction points with a total of", sum(nobs[, 1]) - nobs[1, 1], "locations\n\n")
   }
-  cat("Variable names are (found using names(object)):\n")
+  cat("Variable names are (found using ssn_names(object)):\n")
   ## print(names(x$preds))
-  print(names(x))
+  ssn_names(x)
 }
diff --git a/R/random.R b/R/random.R
index 38fb05e..9810810 100644
--- a/R/random.R
+++ b/R/random.R
@@ -19,12 +19,29 @@ get_randcov_list <- function(index, randcov_Zs = NULL, randcov_names = NULL) {
   randcov_list
 }
 
+#' Get list of random effects
+#'
+#' @param index_val A spatial index (not yet functional) to compare
+#' @param index A spatial index (not yet functional)
+#' @param randcov_Zs Random effect design matrices
+#' @param randcov_names Random effect names
+#'
+#' @noRd
 get_randcov_index_list <- function(index_val, index, randcov_Zs, randcov_names) {
   Z_lists <- lapply(randcov_names, function(x) get_randcov_var_list(x, index_val, index, randcov_Zs))
   names(Z_lists) <- randcov_names
   Z_lists
 }
 
+#' Get list of random effect variances
+#'
+#'
+#' @param randcov_name Random effect name
+#' @param index_val A spatial index (not yet functional) to compare
+#' @param index A spatial index (not yet functional)
+#' @param randcov_Zs Random effect design matrices
+#'
+#' @noRd
 get_randcov_var_list <- function(randcov_name, index_val, index, randcov_Zs) {
   Z_list <- randcov_Zs[[randcov_name]][["Z"]][index_val, , drop = FALSE]
   if (is.null(randcov_Zs[[randcov_name]][["ZZt"]])) {
@@ -40,6 +57,15 @@ get_randcov_var_list <- function(randcov_name, index_val, index, randcov_Zs) {
   list(Z = Z_list, ZZt = ZZt_list, ZtZ = ZtZ_list)
 }
 
+#' Get random effect design matrices
+#'
+#' @param data Data
+#' @param randcov_names Random effect names
+#' @param ZZt Product of random effect design matrix and its transpose
+#' @param ZtZ Transpose of ZZt
+#' @param xlev_list Levels of random effects
+#'
+#' @noRd
 get_randcov_Zs <- function(data, randcov_names = NULL, ZZt = TRUE, ZtZ = FALSE, xlev_list = NULL) {
   if (is.null(randcov_names)) {
     randcov_Zs <- NULL
@@ -50,6 +76,15 @@ get_randcov_Zs <- function(data, randcov_names = NULL, ZZt = TRUE, ZtZ = FALSE,
   randcov_Zs
 }
 
+#' Get a random effect design matrix
+#'
+#' @param randcov_name Random effect name
+#' @param randcov_names Random effect names
+#' @param ZZt Product of random effect design matrix and its transpose
+#' @param ZtZ Transpose of ZZt
+#' @param xlev_list Levels of random effects
+#'
+#' @noRd
 get_randcov_Z <- function(randcov_name, data, ZZt = TRUE, ZtZ = FALSE, xlev_list = NULL) {
   bar_split <- unlist(strsplit(randcov_name, " | ", fixed = TRUE))
   Z_reform <- reformulate(bar_split[[2]], intercept = FALSE)
@@ -112,6 +147,11 @@ get_randcov_names <- function(random = NULL) {
   new_labels
 }
 
+#' Adjust a single name of a random effect
+#'
+#' @param label Name of a random effect
+#'
+#' @noRd
 get_randcov_name <- function(label) {
   if (grepl("|", label, fixed = TRUE)) {
     new_label <- label
diff --git a/R/restruct_ssn_missing.R b/R/restruct_ssn_missing.R
index 55babd7..0abe134 100644
--- a/R/restruct_ssn_missing.R
+++ b/R/restruct_ssn_missing.R
@@ -1,3 +1,13 @@
+#' Restructure a SSN object with missing response data
+#'
+#' @param ssn.object SSN object
+#' @param observed_index Index of observed (non-NA) response values
+#' @param missing_index Index of missing (NA) response values
+#'
+#' @return An SSN object with missing data stored as a prediction data set and
+#'   observed data adjusted accordingly
+#'
+#' @noRd
 restruct_ssn_missing <- function(ssn.object, observed_index, missing_index) {
   if (length(missing_index) > 0) {
     # only return .missing object if necessary
diff --git a/R/ssn_create_distmat.R b/R/ssn_create_distmat.R
index bebb86c..034f29e 100644
--- a/R/ssn_create_distmat.R
+++ b/R/ssn_create_distmat.R
@@ -114,7 +114,7 @@
 #' copy_lsn_to_temp()
 #' ## Import SSN data
 #' mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-#'   predpts = c("pred1km.shp", "Knapp"),
+#'   predpts = c("pred1km.gpkg", "CapeHorn"),
 #'   overwrite = TRUE
 #' )
 #'
@@ -129,27 +129,32 @@
 #' ## Distance matrices for observations and pred1km prediction sites are
 #' ## not recalculated.
 #' ssn_create_distmat(mf04p,
-#'   predpts = "Knapp", overwrite = TRUE,
+#'   predpts = "CapeHorn", overwrite = TRUE,
 #'   among_predpts = TRUE, only_predpts = TRUE
 #' )
 ssn_create_distmat <- function(ssn.object, predpts = NULL, overwrite = FALSE,
                                among_predpts = FALSE, only_predpts = FALSE) {
-
   # iterate if predpts is not null
   if (length(predpts) > 1) {
     if (only_predpts) {
-      x <- lapply(predpts, function(x) ssn_create_distmat(ssn.object,
-                                                          predpts = x,
-                                                          overwrite,
-                                                          among_predpts,
-                                                          only_predpts))
+      x <- lapply(predpts, function(x) {
+        ssn_create_distmat(ssn.object,
+          predpts = x,
+          overwrite,
+          among_predpts,
+          only_predpts
+        )
+      })
     } else {
       x1 <- ssn_create_distmat(ssn.object, overwrite = overwrite)
-      x2 <- lapply(predpts, function(x) ssn_create_distmat(ssn.object,
-                                                           predpts = x,
-                                                           overwrite,
-                                                           among_predpts,
-                                                           only_predpts = TRUE))
+      x2 <- lapply(predpts, function(x) {
+        ssn_create_distmat(ssn.object,
+          predpts = x,
+          overwrite,
+          among_predpts,
+          only_predpts = TRUE
+        )
+      })
     }
     return(invisible(NULL))
   }
@@ -220,10 +225,12 @@ ssn_create_distmat <- function(ssn.object, predpts = NULL, overwrite = FALSE,
 
   ## Get netID with observed or predicted sites
   if (!is.null(predpts)) {
-    site.nets<- unique(c(levels(ssn$obs$NetworkID),
-                         levels(ssn$preds[[predpts]]$NetworkID)))
+    site.nets <- unique(c(
+      levels(ssn$obs$NetworkID),
+      levels(ssn$preds[[predpts]]$NetworkID)
+    ))
   } else {
-    site.nets<- unique(c(levels(ssn$obs$NetworkID)))
+    site.nets <- unique(c(levels(ssn$obs$NetworkID)))
   }
 
   net.count <- length(site.nets)
@@ -258,7 +265,7 @@ ssn_create_distmat <- function(ssn.object, predpts = NULL, overwrite = FALSE,
   ## ----------------------------------------------------------------
   for (i in seq_len(net.count)) {
     ## Set network number and name
-    ##net.num <- levels(ssn$edges$NetworkID)[i]
+    ## net.num <- levels(ssn$edges$NetworkID)[i]
     net.num <- site.nets[i]
     net.name <- paste("net", net.num, sep = "")
 
@@ -369,8 +376,9 @@ ssn_create_distmat <- function(ssn.object, predpts = NULL, overwrite = FALSE,
 
       ## Create data.frame for obs with columns pid, rid, locID
       ob.i <- ssn_get_netgeom(ssn$obs[ind.obs, ], c("pid", "SegmentID", "locID"),
-                                  reformat = TRUE)
-      ##ob.i <- as.data.frame(sapply(ob.i, as.numeric))
+        reformat = TRUE
+      )
+      ## ob.i <- as.data.frame(sapply(ob.i, as.numeric))
       colnames(ob.i) <- c("pid", "rid", "locID")
       ob.i$locID <- as.factor(ob.i$locID)
 
diff --git a/R/ssn_get_netgeometry.R b/R/ssn_get_netgeom.R
similarity index 100%
rename from R/ssn_get_netgeometry.R
rename to R/ssn_get_netgeom.R
diff --git a/R/ssn_import.R b/R/ssn_import.R
index 584063d..5825a65 100644
--- a/R/ssn_import.R
+++ b/R/ssn_import.R
@@ -5,59 +5,37 @@
 #' @param path Filepath to the .ssn directory. See details.
 #' @param include_obs default = \code{TRUE}. Logical indicating
 #'   whether observed sites should be included in the SSN object.
-#' @param predpts Vector of shapefile basenames for prediction sites
-#'   found within the .ssn folder.
-#' @param format_additive Logical indicating whether the columns containing
-#'   the addtive function values should be formated for
-#'   \code{SSN2}. Default = \code{FALSE}.
-#' @param names_additive Character vector of column names in observed and
-#'   prediction site datasets containing additive function values.
-#'   Must be defined if \code{format_additive = TRUE}. Default =
-#'   \code{NULL}.
+#' @param predpts Vector of prediction site dataset names found within
+#'   the .ssn folder. See details.
 #' @param overwrite default = \code{FALSE}. If \code{TRUE}, overwrite
-#'   existing binaryID.db files.
-#' @details The \command{importSSN} function imports spatial data from a .ssn
-#'   folder to create an \code{SSN} object. The information contained in the
-#'   .ssn folder can be generated using a number of proprietary and
-#'   open source software tools:
-#'   \itemize{
-#'     \item{The Spatial Tools for the Analysis of River Systems
-#'     (STARS) tools for ArcGIS Desktop versions 9.3x-10.8x (Peterson
-#'     and Ver Hoef 2014). This custom ArcGIS toolset is designed to
-#'     work with existing streams data in vector format.}
-#'     \item{The openSTARS package (Kattwinkel et al. 2020) extends
-#'     the functionality of the STARS toolset, which makes use of R
-#'     and GRASS GIS. It is open source and designed to derive streams
-#'     in raster format from a digital elevation model (DEM).}
-#'     \item{The SSNbler package (currently in development as of
-#'     September 2023) is an open source version of the STARS toolset,
-#'     which makes use of the functionality found in the sf package to
-#'     process streams data in vector format.}
-#'   }
-#'   When spatial data are processed using one of these software
-#'   tools, a .ssn directory is output which contains all of the
-#'   spatial, topological and attribute data needed to fit a spatial
-#'   statistical stream network model to streams data.  This includes:
-#'   \itemize{
-#'     \item{An edges shapefile of lines that represent the stream
-#'     network.}
-#'     \item{A sites shapefile of points where observed data were
-#'     collected on the stream network.}
-#'     \item{Prediction sites shapefile(s) of locations where
-#'     predictions will be made.}
-#'     \item{netID.dat files for each distinct network, which store
-#'     the topological relationships of the line segments in edges.}
-#'   }
+#'   existing binaryID.db files and netgeom column(s) if it exists in the
+#'   edges, observed sites (if \code{include_obs = TRUE}), and
+#'   prediction site datasets (if they exist).
+#'
+#' @details The \command{ssn_import} function imports spatial data (shapefile or GeoPackage format)
+#'   from a .ssn folder generated using the
+#'   \code{SSNbler} package function \code{SSNbler::lsn_to_ssn}. The .ssn folder contains all of the spatial, topological and
+#'   attribute data needed to fit a spatial statistical stream network
+#'   model to streams data.  This includes:
+#' \itemize{
+#'     \item{An edges dataset with LINESTRING geometry representing the stream network.}
+#'     \item{A sites dataset with POINT geometry where observed data were collected on
+#'   the stream network.}
+#'     \item{Prediction sites dataset(s) representing
+#'   locations where predictions will be made.}
+#'     \item{netID.dat file(s) for each distinct network, which stores the topological
+#'   relationships of the line features in edges.}}
+#'
 #'   A more detailed description of the .ssn directory and its
 #'   contents is provided in Peterson and Ver Hoef (2014).
 #'
-#'   The \command{ssn_import} imports the edges, observed sites, and
-#'   prediction sites as \code{sf data.frame} objects. A new column named 'netgeom'
-#'   is created to store important data that represents
+#'   The \command{ssn_import} imports the edges, observed sites (optional), and
+#'   prediction sites (optional) as \code{sf data.frame} objects. A new column named 'netgeom'
+#'   is created to store important data representing
 #'   topological relationships in a spatial stream network
 #'   model. These data are stored in character format, which is less
 #'   likely to be inadvertantly changed by users. See
-#'   \code{\link[SSN2]{ssn_get_netgeom}} for a more detailed description of
+#'   \code{\link[SSN2]{create_netgeom}} for a more detailed description of
 #'   the format and contents of 'netgeom'.
 #'
 #'   The information contained in the netID text files is imported
@@ -71,20 +49,25 @@
 #'   file already exists within the .ssn directory, it will be
 #'   overwriten when the \code{SSN} object is created.
 #'
-#'   At a minimum, an \code{SSN} object must always contain streams, which
-#'   are referred to as edges. The \code{SSN} object would also typically
-#'   contain a set of observed sites, where measurements have been
-#'   collected and only one observed dataset is permitted. When
-#'   \code{include_obs=FALSE}, an \code{SSN} object is created without
-#'   observations. This option provides flexibility for users who
-#'   would like to simulate data on a set of artifical sites on an
-#'   existing stream network. Note that observation sites must be
-#'   included in the \code{SSN} object in order to fit models using
-#'   \command{ssn_lm} or \command{ssn_glm}. The \code{SSN} object may contain
-#'   multiple sets of prediction points (or none), which are stored as
-#'   separate shapefiles in the .ssn directory. The
-#'   \code{\link[SSN2]{ssn_import_predpts}} function allows users to import additional
-#'   sets of prediction sites to a an existing \code{SSN} object.
+#'   At a minimum, an \code{SSN} object must always contain streams,
+#'   which are referred to as edges. The \code{SSN} object would also
+#'   typically contain a set of observed sites, where measurements
+#'   have been collected. Only one observed dataset is permitted in an
+#'   \code{SSN} object. When \code{include_obs=FALSE}, an \code{SSN}
+#'   object is created without observations. This option provides
+#'   flexibility for users who would like to simulate data on a set of
+#'   artifical sites on an existing stream network. Note that
+#'   observation sites must be included in the \code{SSN} object in
+#'   order to fit models using \command{ssn_lm} or
+#'   \command{ssn_glm}. The \code{SSN} object may contain multiple
+#'   sets of prediction points (or none), which are stored as separate
+#'   datasets in the .ssn directory. If \code{predpts} is a named
+#'   vector, the names of the \code{preds} list in the \code{SSN}
+#'   object correspond to the vector names. Otherwise, they are set to
+#'   the basename of the prediction site dataset file(s) specified in
+#'   \code{predpts}. The \code{\link[SSN2]{ssn_import_predpts}}
+#'   function allows users to import additional sets of prediction
+#'   sites to a an existing \code{SSN} object.
 #'
 #' @return \code{ssn_import} returns an object of class SSN, which is a list
 #'   with four elements containing:
@@ -95,18 +78,12 @@
 #'     with an additional 'netgeom' column. NA if \code{include_obs =
 #'     FALSE}.}
 #'     \item{\code{preds}: A list of sf data.frames containing prediction
-#'     site locations. The names of the preds list correspond to the
-#'     basenames of the prediction site shapefiles (without the .shp
-#'     extension) specified in \code{predpts}. Empty list if \code{predpts} is not provided.}
-#'     \item{path: The local file to the .ssn directory associated with the \code{SSN}
+#'     site locations. An empty list is returned if \code{predpts} is not provided.}
+#'     \item{path: The local filepath for the .ssn directory associated with the \code{SSN}
 #'     object.}
 #'   }
-#' @seealso [ssn_get_netgeom]
+#'
 #' @references
-#' Kattwinkel, M., Szocs, E., Peterson, E., and Schafer,
-#'   R.B. (2020) Preparing GIS data for analysis of stream monitoring
-#'   data: The R package openSTARS. \emph{PLOS One} \bold{15(9)},
-#'   e0239237.
 #' Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
 #'   ArcGIS toolset used to calculate the spatial information needed
 #'   to fit spatial statistical stream network models to stream
@@ -126,238 +103,156 @@
 #' ## Import SSN object with 3 sets of prediction sites
 #' mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
 #'   predpts = c(
-#'     "pred1km.shp",
-#'     "CapeHorn.shp",
-#'     "Knapp.shp"
+#'     "pred1km",
+#'     "CapeHorn"
 #'   ),
 #'   overwrite = TRUE
 #' )
 #'
-ssn_import <- function(path, include_obs = TRUE, predpts,
-                       format_additive = FALSE, names_additive = NULL,
+ssn_import <- function(path, include_obs = TRUE, predpts = NULL,
                        overwrite = FALSE) {
-  ## Extract dirname
-  file <- path
-  ssn_folder <- file
+  if (!dir.exists(path)) stop("Cannot find the .ssn folder.")
 
   # Get wd
   old_wd <- getwd()
   on.exit(setwd(old_wd)) # if the function crashes or finishes, it will restore the initial working directory
 
-  ## Adjust if relative pathname is supplied and fix edges
-  if (substr(ssn_folder, start = 1, stop = 2) == "./") {
-    rel.path <- substr(ssn_folder, start = 2, stop = nchar(ssn_folder))
-    ssn_folder <- paste0(old_wd, rel.path)
-  }
+  local_dir(path)
 
-  if (!dir.exists(ssn_folder)) stop("Cannot find the .ssn folder.")
+  ## Adjust if relative pathname is supplied in path
+  if (substr(path, start = 1, stop = 2) == "./") {
+    rel.path <- substr(path, start = 2, stop = nchar(path))
+    path <- paste0(old_wd, rel.path)
+  }
 
-  # Set wd
-  setwd(ssn_folder)
+  #################################################################
+  ## Check format of predpts
+  ################################################################
 
-  ## Check inputs, generally
-  missingsites <- !include_obs
-  missingpreds <- missing(predpts)
+  ## If names are provided, use them
+  if (is.vector(predpts) & !is.null(names(predpts))) {
+    p.names <- names(predpts)
+  }
 
-  # Check observation sites exist and import
-  if (!missingsites) {
-    obs_sites <- "sites.shp"
+  ## If no names provided assign based on name of file without extension
+  if (is.vector(predpts) & is.null(names(predpts))) {
+    p.names <- NULL
 
-    # Check the file exists and then ingest, process
-    if (file.exists(obs_sites)) {
-      sfsites <- st_read(obs_sites, quiet = TRUE)
+    for (d in 1:length(predpts)) {
+      shp.ext <- substr(predpts[d], nchar(predpts[d]) - 3, nchar(predpts[d])) == ".shp"
+      gpkg.ext <- substr(predpts[d], nchar(predpts[d]) - 4, nchar(predpts[d])) == ".gpkg"
 
-      ## Check geometry type
-      if (sum(st_geometry_type(sfsites, by_geometry = TRUE) == "POINT") != nrow(sfsites)) {
-        stop("Observed sites do not have POINT geometry")
-      }
-      ## Add network geometry column
-      sfsites[, "netgeom"] <- paste0("SNETWORK (", paste(
-        sfsites$netID, sfsites$rid, sfsites$upDist,
-        sfsites$ratio, sfsites$pid, sfsites$locID
-      ),
-      ")",
-      sep = ""
+      p.names[d] <- ifelse(
+        shp.ext == TRUE, substr(predpts[d], 1, nchar(predpts[d]) - 4),
+        ifelse(gpkg.ext == TRUE,
+          substr(predpts[d], 1, nchar(predpts[d]) - 5),
+          ifelse(shp.ext == FALSE & gpkg.ext == FALSE, predpts[d])
+        )
       )
-
-      if (format_additive == TRUE) {
-        if (is.null(names_additive)) {
-          stop("names_additive is required when format_additive = TRUE")
-        }
-        for (q in seq_len(length(names_additive))) {
-          if (!names_additive[q] %in% colnames(sfsites)) {
-            warning(paste0(names_additive[q], " is not found in obs"), call. = FALSE)
-          } else {
-            ## Extract afv column
-            tmp.col <- st_drop_geometry(sfsites[, names_additive[q]])
-            ## convert to character if column is numeric
-            if (!inherits(tmp.col[, 1], "numeric")) {
-              message(paste0(names_additive[q], " is not numeric. AFV values were not modified"))
-            } else {
-              tmp.col2 <- formatC(tmp.col[, 1],
-                digits = 254, format = "f",
-                drop0trailing = TRUE
-              )
-              sfsites[, names_additive[q]] <- tmp.col2
-            }
-          }
-        }
-      }
-    } else {
-      stop("This shapefile does not exist: ", obs_sites)
     }
   }
 
-  if (!missingpreds) {
-    # Append .shp if necessary
-    nofe <- !grepl(".shp$", predpts)
-    if (any(nofe)) predpts[nofe] <- paste0(predpts[nofe], ".shp")
-
-    ## Check for relative pathnames
-    ## rel.name <- substr(predpts, start = 1, stop = 2)== "./"
-    ## if(any(rel.name)) predpts[rel.name]<- basename(predpts[rel.name])
 
-    # Check the files exist and then import prediction points
-    if (all(file.exists(predpts))) {
-      sfpreds <- vector(mode = "list", length = length(predpts))
-
-      for (m in seq_len(length(predpts))) {
-        tmp.preds <- st_read(paste0(file, "/", predpts[m]), quiet = TRUE)
-
-        ## Check geometry type
-        if (sum(st_geometry_type(tmp.preds, by_geometry = TRUE) == "POINT") != nrow(tmp.preds)) {
-          stop(paste0(predpts[m], " do not have POINT geometry"))
-        }
+  ## Remove path to predpts files, if included
+  if (!is.null(predpts)) {
+    predpts <- basename(predpts)
+  }
 
-        if (format_additive == TRUE) {
-          if (is.null(names_additive)) {
-            stop("names_additive is required when format_additive = TRUE")
-          }
-          for (q in seq_len(length(names_additive))) {
-            if (!names_additive[q] %in% colnames(tmp.preds)) {
-              warning(paste0(names_additive[q], " is not found in ", predpts[m]), call. = FALSE)
-            } else {
-              ## Extract afv column
-              tmp.col <- st_drop_geometry(tmp.preds[, names_additive[q]])
-              ## convert to character if column is numeric
-              if (!inherits(tmp.col[, 1], "numeric")) {
-                message(paste0(names_additive[q], " is not numeric. AFV values were not modified"))
-              } else {
-                tmp.col2 <- formatC(tmp.col[, 1],
-                  digits = 254, format = "f",
-                  drop0trailing = TRUE
-                )
-                tmp.preds[, names_additive[q]] <- tmp.col2
-              }
-            }
-          }
-        }
 
-        ## Add network geometry column
-        tmp.preds[, "netgeom"] <- paste0("SNETWORK (", paste(
-          tmp.preds$netID,
-          tmp.preds$rid,
-          tmp.preds$upDist,
-          tmp.preds$ratio,
-          tmp.preds$pid,
-          tmp.preds$locID
-        ),
-        ")",
-        sep = ""
-        )
-        sfpreds[[m]] <- tmp.preds
-        names(sfpreds)[m] <- substr(basename(predpts[m]),
-          start = 1,
-          stop = nchar(basename(predpts[m])) - 4
-        )
-        rm(tmp.preds)
-      }
-    } else {
-      stop("At least one of these shapefiles does not exist: ", predpts)
-    }
-  }
+  ## ----------------------------------------------------
   ## Import edges
-  if (file.exists("edges.shp")) {
-    # Get the edges
-    sfedges <- st_read("edges.shp", quiet = TRUE)
+  ## ----------------------------------------------------
+  sfedges <- get_sf_obj("edges")
 
+  if (exists("sfedges")) {
     ## Check geometry type
-    if (sum(st_geometry_type(sfedges, by_geometry = TRUE) == "LINESTRING") != nrow(sfedges)) {
+    if (sum(st_geometry_type(sfedges, by_geometry = TRUE) ==
+      "LINESTRING") != nrow(sfedges)) {
       stop("Edges do not have LINESTRING geometry")
     }
 
-    if (format_additive == TRUE) {
-      if (is.null(names_additive)) {
-        stop("names_additive is required when format_additive = TRUE")
-      }
-      for (q in seq_len(length(names_additive))) {
-        if (!names_additive[q] %in% colnames(sfedges)) {
-          warning(paste0(names_additive[q], " is not found in obs"), call. = FALSE)
-        } else {
-          ## Extract afv column
-          tmp.col <- st_drop_geometry(sfedges[, names_additive[q]])
-          ## convert to character if column is numeric
-          if (!inherits(tmp.col[, 1], "numeric")) {
-            message(paste0(names_additive[q], " is not numeric. AFV values were not modified"))
-          } else {
-            tmp.col2 <- formatC(tmp.col[, 1],
-              digits = 254, format = "f",
-              drop0trailing = TRUE
-            )
-            sfedges[, names_additive[q]] <- tmp.col2
-          }
-        }
-      }
+    ## Ensure geometry column is named geometry
+    if (!"geometry" %in% colnames(sfedges)) {
+      sf::st_geometry(sfedges) <- "geometry"
     }
 
-
     ## Add network geometry column to edges
-    sfedges[, "netgeom"] <- paste0("ENETWORK (", paste(
-      sfedges$netID,
-      sfedges$rid,
-      sfedges$upDist
-    ),
-    ")",
-    sep = ""
-    )
-    ssnlist <- list(edges = sfedges)
+    sfedges<- create_netgeom(sfedges, type = "LINESTRING",
+                               overwrite = overwrite)
+    
   } else {
-    stop(paste0("Edges is missing from ", file))
+    stop(paste0("Edges is missing from ", path))
   }
 
-  ## }
+  ## ----------------------------------------------------
+  ## Import obs
+  ## ----------------------------------------------------
 
-  ## )
+  ## Check observation sites exist and import
+  if (include_obs == TRUE) {
+    ## Check the file exists and then ingest, process
+    sfsites <- get_sf_obj("sites")
+
+    ## Check geometry type
+    if (sum(st_geometry_type(sfsites, by_geometry = TRUE) ==
+      "POINT") != nrow(sfsites)) {
+      stop("Observed sites do not have POINT geometry")
+    }
+
+    ## Ensure geometry column is named geometry
+    if (!"geometry" %in% colnames(sfsites)) {
+      sf::st_geometry(sfsites) <- "geometry"
+    }
 
-  ## Add observation sites and prediction sites if present
-  if (missingsites) {
-    ssnlist$obs <- NA
+    ## ## Add network geometry column
+    sfsites<- create_netgeom(sfsites, type = "POINT",
+                               overwrite = overwrite)
+         
   } else {
-    ssnlist$obs <- sfsites
+    sfsites <- NA
   }
 
-  if (!missingpreds) {
-    ssnlist$preds <- sfpreds
+  ## ----------------------------------------------------
+  ## Import preds
+  ## ----------------------------------------------------
+  if (!is.null(predpts)) {
+    sfpreds <- vector(mode = "list", length = length(predpts))
+
+    for (m in seq_len(length(predpts))) {
+      tmp.preds <- get_sf_obj(predpts[m]) 
+
+      ## Check geometry type
+      if (sum(st_geometry_type(tmp.preds, by_geometry = TRUE) == "POINT") != nrow(tmp.preds)) {
+        stop(paste0(predpts[m], " do not have POINT geometry"))
+      }
+
+      ## Add network geometry column
+      tmp.preds<- create_netgeom(tmp.preds, type = "POINT",
+                               overwrite = overwrite)
+
+      sfpreds[[m]] <- tmp.preds
+      names(sfpreds)[m] <- p.names[m]
+      rm(tmp.preds)
+    }
+
   } else {
-    ssnlist$preds <- list()
+    sfpreds <- list()
   }
 
-  # Add the path
-  ## ssnlist$path <- dirname(edges)
-  ssnlist$path <- ssn_folder
-
-  # Set custom class
+  ## ---------------------------------------------------------
+  ## Create SSN object and return
+  ## ---------------------------------------------------------
+  ssnlist <- list(edges = sfedges, obs = sfsites, preds = sfpreds, path = path)
   class(ssnlist) <- "SSN"
 
   ## Create Binary ID database
   createBinaryID(ssnlist, overwrite = overwrite)
 
-  ## Warning when observation sites are not included if(include_obs ==
-  if (include_obs == FALSE) {
-    warning("SSN does not include observed sites, which are needed to fit models. If this was a mistake, set include_obs == TRUE")
+  ## Warning when observation sites are not included in SSN
+  if (is.logical(ssnlist$obs)) {
+    warning("SSN does not include observed sites, which are needed to fit models. If this was a mistake, run ssn_import() with obs correctly defined")
   }
 
-
-  # Return
+  ## Return
   return(ssnlist)
 }
diff --git a/R/ssn_import_predpts.R b/R/ssn_import_predpts.R
index a79e127..dedf27a 100644
--- a/R/ssn_import_predpts.R
+++ b/R/ssn_import_predpts.R
@@ -5,56 +5,41 @@
 #'   \code{SSN}, \code{ssn_lm}, or \code{ssn_glm}.
 #' @param x An object of class\code{SSN}, \code{ssn_lm}, or
 #'   \code{ssn_glm}.
-#' @param predpts Name of the prediction point shapefile to import in
-#'   character format, without the .shp extension.
-#' @param format_additive Logical indicating whether the columns containing
-#'   the addtive function values should be formated for
-#'   \code{SSN2}. Default = \code{FALSE}.
-#' @param names_additive Character vector of column names in observed and
-#'   prediction site datasets containing additive function
-#'   values. Must be defined if \code{format_additive = TRUE}. Default =
-#'   \code{NULL}.
+#' @param predpts Name of the prediction point dataset to import in
+#'   character format. See details.
 #'
-#' @details \command{ssn_import_predpts} imports a shapefile of
-#'   prediction points residing in the .ssn directory into an existing
+#' @details \command{ssn_import_predpts} imports one set of prediction
+#'   points residing in the .ssn directory into an existing
 #'   \code{SSN}, \code{ssn_lm}, or \code{ssn_glm} object. The
-#'   prediction dataset must reside in the ssn.object$path
+#'   prediction dataset must be in shapefile or geopackage format
+#'   (.shp or .gpkg, respectively) and reside in the ssn.object$path
 #'   directory. The path for an \code{SSN} object can be updated using
 #'   \command{ssn_update_path()} prior to importing prediction
-#'   datasets. Note that, the prediction dataset must contain the
+#'   datasets. The argument \code{predpts} accepts the name of the
+#'   prediction point dataset, with or without the file extension. If
+#'   it is passed as a named vector (of length 1), then the name
+#'   provided is used as the prediction dataset name in the \code{SSN}
+#'   object prediction sites list
+#'   (e.g. \code{names(ssn.obj$preds)}). Otherwise, the file basename
+#'   is used in the names attribute. See
+#'   \code{\link[SSN2]{ssn_import}} for a detailed description of the
+#'   prediction dataset format within the \code{SSN} class object.
+#'
+#'   When the prediction dataset is imported, a new column named
+#'   \code{netgeom} is created. If this column already exists it is
+#'   overwritten. Please see \code{\link{create_netgeom}} for a
+#'   detailed description of the \code{netgeom} column and the
+#'   information it contains.
+#'
+#'   The prediction dataset specified in \code{predpts} must contain the
 #'   spatial, topological and attribute information needed to make
-#'   predictions using an ssn_lm or ssn_glm object.  This information
-#'   can be generated using a number of proprietary and open source
-#'   software tools: \itemize{ \item{The Spatial Tools for the
-#'   Analysis of River Systems (STARS) tools for ArcGIS Desktop
-#'   versions 9.3x-10.8x (Peterson and Ver Hoef 2014). This custom
-#'   ArcGIS toolset is designed to work with existing streams data in
-#'   vector format.}  \item{The openSTARS package (Kattwinkel et
-#'   al. 2020) extends the functionality of the STARS toolset, which
-#'   makes use of R and GRASS GIS. It is open source and designed to
-#'   derive streams in raster format from a digital elevation model
-#'   (DEM).}  \item{The SSNbler package (currently in development as
-#'   of September 2023) is an open source version of the STARS
-#'   toolset, which makes use of the functionality found in the sf
-#'   package to process streams data in vector format.}  }
+#'   predictions using an ssn_lm or ssn_glm object. This information
+#'   is generated using the \code{SSNbler} package, which makes use of
+#'   the functionality found in the \code{sf} and \code{igraph}
+#'   packages to process streams data in vector format.
 #' @return an object of class \code{SSN}, \code{ssn_lm}, or
-#'   \code{ssn_glm} which contains the new prediction dataset. The
-#'   name of the prediction dataset in the preds list corresponds to
-#'   the basenames of the prediction site shapefiles (without the .shp
-#'   extension) specified in \code{predpts}. See
-#'   \code{\link[SSN2]{ssn_import}} for a detailed description of
-#'   the prediction dataset format within the \code{SSN} class object.
+#'   \code{ssn_glm} which contains the new prediction dataset.
 #'
-#' @references
-#' Kattwinkel, M., Szocs, E., Peterson, E., and Schafer,
-#'   R.B. (2020) Preparing GIS data for analysis of stream monitoring
-#'   data: The R package openSTARS. \emph{PLOS One} \bold{15(9)},
-#'   e0239237.
-#' Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
-#'   ArcGIS toolset used to calculate the spatial information needed
-#'   to fit spatial statistical stream network models to stream
-#'   network data. \emph{Journal of Statistical Software}
-#'   \bold{56(2)}, 1--17.
 #' @export
 #' @examples
 #' ## Create local temporary copy of MiddleFork04.ssn found in
@@ -66,40 +51,85 @@
 #'   overwrite = TRUE
 #' )
 #'
-#' ## Import pred1km prediction dataset into SSN object
-#' mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"))
-#' mf04p <- ssn_import_predpts(mf04p, predpts = "pred1km")
+#' ## Import pred1km prediction dataset into SSN object and assign the
+#' ## name preds1
+#' mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
+#'     overwrite = TRUE)
+#' mf04p <- ssn_import_predpts(mf04p, predpts = c(preds1 = "pred1km"))
 #' names(mf04p$preds)
 #'
-#' ## Import pred1km prediction dataset into a ssn_glm object
+#' ## Import CapeHorn prediction dataset into a ssn_glm object, using
+#' ## the default file basename as the name
 #' ssn_gmod <- ssn_glm(Summer_mn ~ netID, mf04p,
 #'   family = "Gamma",
 #'   tailup_type = "exponential", additive = "afvArea"
 #' )
 #' ssn_gmod <- ssn_import_predpts(ssn_gmod, predpts = "CapeHorn")
 #' names(ssn_gmod$ssn.object$preds)
-ssn_import_predpts <- function(x, predpts,
-                               format_additive = FALSE, names_additive = NULL) {
+#'
+ssn_import_predpts <- function(x, predpts) {
   obj.type <- class(x)
 
   old_wd <- getwd()
   on.exit(setwd(old_wd))
 
+  #################################################################
+  ## Check format of predpts
+  ################################################################
+
+  ## If names are provided, use them
+  if (is.vector(predpts) & !is.null(names(predpts))) {
+    p.names <- names(predpts)
+  }
+
+  ## If no names provided assign based on name of file without extension
+  if (is.vector(predpts) & is.null(names(predpts))) {
+    p.names <- NULL
+
+    for (d in 1:length(predpts)) {
+      shp.ext <- substr(
+        predpts[d], nchar(predpts[d]) - 3,
+        nchar(predpts[d])
+      ) == ".shp"
+      gpkg.ext <- substr(
+        predpts[d], nchar(predpts[d]) - 4,
+        nchar(predpts[d])
+      ) == ".gpkg"
+
+      p.names[d] <- ifelse(
+        shp.ext == TRUE, substr(predpts[d], 1, nchar(predpts[d]) - 4),
+        ifelse(gpkg.ext == TRUE,
+          substr(predpts[d], 1, nchar(predpts[d]) - 5),
+          ifelse(shp.ext == FALSE & gpkg.ext == FALSE,
+            predpts[d]
+          )
+        )
+      )
+    }
+  }
+
+  ## Remove path to predpts files, if included
+  predpts <- basename(predpts)
+
+  ################################################
+
+
   ## For fitted model objects- check if predpts already exists
   if (obj.type %in% c("ssn_lm", "ssn_glm")) {
     setwd(x$ssn.object$path)
-    count <- 0
-
-    if (length(x$ssn.object$preds) > 0) {
-      for (m in seq_len(length(x$ssn.object$preds))) {
-        if (names(x$ssn.object$preds[m]) == predpts) {
-          pred.num <- m
-          count <- count + 1
-        }
-      }
-    }
+    ## count <- 0
 
-    if (count > 0) {
+    ## if (length(x$ssn.object$preds) > 0) {
+    ##   for (m in seq_len(length(x$ssn.object$preds))) {
+    ##     if (names(x$ssn.object$preds[m]) == p.names) {
+    ##       pred.num <- m
+    ##       count <- count + 1
+    ##     }
+    ##   }
+    ## }
+
+    ## if (count > 0) {
+    if(p.names %in% names(x$ssn.object$preds)) {
       stop("Fitted model object already contains predpoints named ", predpts)
     }
   }
@@ -107,84 +137,46 @@ ssn_import_predpts <- function(x, predpts,
   ## For SSN objects - check if predpts already exits
   if (obj.type == "SSN") {
     setwd(x$path)
-    count <- 0
-
-    if (length(x$preds) > 0) {
-      for (m in seq_len(length(x$preds))) {
-        if (names(x$preds[m]) == predpts) {
-          pred.num <- m
-          count <- count + 1
-        }
-      }
-    }
+    ## count <- 0
+
+    ## if (length(x$preds) > 0) {
+    ##   for (m in seq_len(length(x$preds))) {
+    ##     if (names(x$preds[m]) == p.names) {
+    ##       pred.num <- m
+    ##       count <- count + 1
+    ##     }
+    ##   }
+    ## }
 
-    if (count > 0) {
+    ## if (count > 0) {
+    if(p.names %in% names(x$ssn.object$preds)) {
       stop("SSN already contains a prediction dataset named ", predpts)
     }
   }
 
-  if (file.exists(paste(predpts, ".shp", sep = "")) == 0) {
-    stop(paste(predpts, ".shp data is missing from ", old_wd,
-      " folder",
-      sep = ""
-    ))
-  }
-  ## Read in prediction point shapefile
-  predpoints <- st_read(paste0(predpts, ".shp"), quiet = TRUE)
+  ## Import predpts as sf object
+  predpoints <- get_sf_obj(predpts)
 
   ## Check geometry type
   if (sum(st_geometry_type(predpoints, by_geometry = TRUE) == "POINT") != nrow(predpoints)) {
     stop(paste0(predpts, " does not have POINT geometry"))
   }
 
-  if (format_additive == TRUE) {
-    if (is.null(names_additive)) {
-      stop("names_additive is required when format_additive = TRUE")
-    }
-    for (q in seq_len(length(names_additive))) {
-      if (!names_additive[q] %in% colnames(predpoints)) {
-        warning(paste0(names_additive[q], " is not found in ", predpts), call. = FALSE)
-      } else {
-        ## Extract afv column
-        tmp.col <- st_drop_geometry(predpoints[, names_additive[q]])
-        ## convert to character if column is numeric
-        if (!inherits(tmp.col[, 1], "numeric")) {
-          message(paste0(names_additive[q], " is not numeric. AFV values were not modified"))
-        } else {
-          tmp.col2 <- formatC(tmp.col[, 1],
-            digits = 254, format = "f",
-            drop0trailing = TRUE
-          )
-          predpoints[, names_additive[q]] <- tmp.col2
-        }
-      }
-    }
-  }
-
-  ## Add netgeom column
-  predpoints[, "netgeom"] <- paste0("SNETWORK (", paste(
-    predpoints$netID,
-    predpoints$rid,
-    predpoints$upDist,
-    predpoints$ratio,
-    predpoints$pid,
-    predpoints$locID
-  ),
-  ")",
-  sep = ""
-  )
+  ## Add network geometry column
+  predpoints<- create_netgeom(predpoints, type = "POINT",
+                             overwrite = TRUE)
 
   ## Put prediction points in SSN object
   if (obj.type == "SSN") {
     pred.num <- length(x$preds)
     x$preds[[pred.num + 1]] <- predpoints
-    names(x$preds)[[pred.num + 1]] <- predpts
+    names(x$preds)[[pred.num + 1]] <- p.names
   }
   ## Put prediction points in fitted model object
   if (obj.type %in% c("ssn_glm", "ssn_lm")) {
     pred.num <- length(x$ssn.object$preds)
     x$ssn.object$preds[[pred.num + 1]] <- predpoints
-    names(x$ssn.object$preds)[[pred.num + 1]] <- predpts
+    names(x$ssn.object$preds)[[pred.num + 1]] <- p.names
   }
   return(x)
 }
diff --git a/R/ssn_names.R b/R/ssn_names.R
new file mode 100644
index 0000000..dbaf0ed
--- /dev/null
+++ b/R/ssn_names.R
@@ -0,0 +1,44 @@
+#' Return names of data in an SSN object
+#'
+#' @description Extract and print names from the \code{edges}, \code{sites}
+#' and \code{preds} elements of an SSN object.
+#'
+#' @param ssn.object An \code{SSN} object.
+#'
+#' @return Print variable names to console
+#'
+#' @name ssn_names
+#' @export
+ssn_names <- function(ssn.object) {
+  if (identical(ssn.object$obs, NA)) {
+    no <- 0
+    nameso <- NULL
+  } else {
+    # is obs present
+    no <- 1
+    nameso <- names(ssn.object$obs)
+  }
+  np <- length(ssn.object$preds)
+  nt <- no + np
+  if (nt == 0) {
+    return(cat("There are no observed or prediction data and hence, no variables names."))
+  }
+
+  namesList <- vector("list", nt)
+  if (no == 1) {
+    namesList[[1]] <- nameso
+    names4List <- "obs"
+  } else {
+    names4List <- NULL
+  }
+  if (np > 0) {
+    prednames <- names(ssn.object$preds)
+    for (i in seq_len(np)) {
+      namespi <- names(ssn.object$preds[[i]])
+      names4List <- c(names4List, prednames[i])
+      namesList[[i + no]] <- namespi
+    }
+  }
+  names(namesList) <- names4List
+  print(namesList)
+}
diff --git a/R/ssn_split_predpts.R b/R/ssn_split_predpts.R
index cb52699..e3f753c 100644
--- a/R/ssn_split_predpts.R
+++ b/R/ssn_split_predpts.R
@@ -9,7 +9,7 @@
 #'
 #' @param ssn An \code{SSN} object.
 #' @param predpts A character string representing the name of the
-#'   prediction dataset
+#'   prediction dataset.
 #' @param size_predpts numeric value representing the size of the new
 #'   prediction sets. The existing prediction set is split equally to
 #'   produce multiple prediction sets of this size
@@ -28,20 +28,20 @@
 #'   levels should be dropped in the \code{by} column when the new
 #'   prediction dataset(s) are created. Default is \code{FALSE}
 #' @param overwrite logical indicating whether the new prediction
-#'   dataset shapefile should be deleted in the .ssn directory if it
+#'   dataset geopackage should be deleted in the .ssn directory if it
 #'   already exists. Default = \code{FALSE}
 #'
 #' @details Three methods have been provided to split prediction sets:
-#'   \code{size_predpts}, \code{by}, and \code{subset}. The
-#'   \code{size_predpts} method is used to split the existing prediction
-#'   set into multiple equally-sized prediction sets. Note that the
+#'   size, by, and subset. The
+#'   size method is used to split the existing prediction
+#'   set into multiple equally-sized prediction sets using the \code{size_predpts} argument. Note that the
 #'   final prediction set may be smaller in size than the others if
 #'   the total number of predictions is not evenly divisible by
-#'   \code{size_predpts}. The \code{by} method is used if the prediction
+#'   \code{size_predpts}. The by method is used if the prediction
 #'   set is to be split into multiple new prediction sets based on an
 #'   existing column of type factor, integer, character, or
-#'   logical. The \code{subset} method is used to create one new
-#'   prediction set based on a logical expression.
+#'   logical specified using the argument \code{by}. The subset method is used to create one new
+#'   prediction set based on a logical expression defined in \code{subset}.
 #'
 #'   When more than one prediction dataset is created the prediction
 #'   dataset names will be appended with a hyphen and prediction
@@ -61,7 +61,7 @@
 #'   predictions can be made.
 #'
 #' @return returns the \code{SSN} specified in \code{ssn}, with one or more new prediction
-#'   sets. Shapefiles of the new prediction sets are written to the
+#'   sets. Geopackages of the new prediction sets are written to the
 #'   .ssn directory designated in ssn$path.
 #'
 #' @name ssn_split_predpts
@@ -70,11 +70,11 @@
 #' ## Import SSN object
 #' copy_lsn_to_temp() ## Only needed for this example
 #' ssn <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-#'   predpts = c("pred1km.shp", "Knapp", "CapeHorn"),
+#'   predpts = c("pred1km", "CapeHorn"),
 #'   overwrite = TRUE
 #' )
 #'
-#' ## Split predictions into size_predpts 200
+#' ## Split predictions based on 'size' method
 #' ssn1 <- ssn_split_predpts(ssn, "CapeHorn",
 #'   size_predpts = 200,
 #'   keep = FALSE, overwrite = TRUE
@@ -82,7 +82,7 @@
 #' names(ssn1$preds)
 #' nrow(ssn1$preds[["CapeHorn-1"]])
 #'
-#' ## Split predictions using by method
+#' ## Split predictions using 'by' method
 #' ssn$preds$pred1km$net.fac <- as.factor(ssn$preds$pred1km$netID)
 #' ssn2 <- ssn_split_predpts(ssn, "pred1km",
 #'   by = "net.fac",
@@ -90,7 +90,7 @@
 #' )
 #' names(ssn2$preds)
 #'
-#' ## Split predictions using subset method
+#' ## Split predictions using 'subset' method
 #' ssn3 <- ssn_split_predpts(ssn, "pred1km",
 #'   subset = ratio > 0.5,
 #'   id_predpts = "RATIO_05", overwrite = TRUE
@@ -178,7 +178,7 @@ ssn_split_predpts <- function(ssn, predpts, size_predpts, by,
       ## Recreate netgeom
       ind <- colnames(tmp) == "netgeom"
       tmp <- tmp[, !ind]
-      tmp <- create_netgeom(tmp, "point")
+      tmp <- create_netgeom(tmp, "POINT", overwrite = TRUE)
 
       ## Replace predpts with updated data.frame
       ssn$preds[[predpts]] <- tmp
@@ -215,18 +215,18 @@ ssn_split_predpts <- function(ssn, predpts, size_predpts, by,
 
 
         ## write subset of prediction points to file
-        fe.old <- file.exists(paste0(ssn$path, "/", id_predpts2, ".shp"))
+        fe.old <- file.exists(paste0(ssn$path, "/", id_predpts2, ".gpkg"))
         if (overwrite == TRUE & fe.old == TRUE) {
-          st_delete(paste0(ssn$path, "/", id_predpts2, ".shp"),
+          st_delete(paste0(ssn$path, "/", id_predpts2, ".gpkg"),
             quiet = TRUE
           )
         }
         if (overwrite == FALSE & fe.old == TRUE) {
-          stop(paste0("overwrite = FALSE and ", id_predpts2, ".shp already exists"))
+          stop(paste0("overwrite = FALSE and ", id_predpts2, ".gpkg already exists"))
         }
 
-        ## Write shapefile
-        st_write(sub.data, paste0(id_predpts2, ".shp"), quiet = TRUE)
+        ## Write geopackage
+        st_write(sub.data, paste0(id_predpts2, ".gpkg"), quiet = TRUE)
 
         ## Add to existing ssn
         new.index <- length(ssn$preds) + 1
@@ -253,18 +253,18 @@ ssn_split_predpts <- function(ssn, predpts, size_predpts, by,
       sub.data <- ssn$preds[[predpts]][values, ]
 
       ## write subset of prediction points to file
-      fe.old <- file.exists(paste0(ssn$path, "/", id_predpts, ".shp"))
+      fe.old <- file.exists(paste0(ssn$path, "/", id_predpts, ".gpkg"))
       if (overwrite == TRUE & fe.old == TRUE) {
-        st_delete(paste0(ssn$path, "/", id_predpts, ".shp"),
+        st_delete(paste0(ssn$path, "/", id_predpts, ".gpkg"),
           quiet = TRUE
         )
       }
       if (overwrite == FALSE & fe.old == TRUE) {
-        stop(paste0("overwrite = FALSE and ", id_predpts, ".shp already exists"))
+        stop(paste0("overwrite = FALSE and ", id_predpts, ".gpkg already exists"))
       }
 
       ## write subset of prediction points to file
-      st_write(sub.data, paste0(id_predpts, ".shp"), quiet = TRUE)
+      st_write(sub.data, paste0(id_predpts, ".gpkg"), quiet = TRUE)
 
       ## Add to existing ssn
       new.index <- length(ssn$preds) + 1
@@ -306,18 +306,18 @@ ssn_split_predpts <- function(ssn, predpts, size_predpts, by,
         }
 
         ## write subset of prediction points to file
-        fe.old <- file.exists(paste0(ssn$path, "/", id_predpts, ".shp"))
+        fe.old <- file.exists(paste0(ssn$path, "/", id_predpts, ".gpkg"))
         if (overwrite == TRUE & fe.old == TRUE) {
-          st_delete(paste0(ssn$path, "/", id_predpts, ".shp"),
+          st_delete(paste0(ssn$path, "/", id_predpts, ".gpkg"),
             quiet = TRUE
           )
         }
         if (overwrite == FALSE & fe.old == TRUE) {
-          stop(paste0("overwrite = FALSE and ", id_predpts, ".shp already exists"))
+          stop(paste0("overwrite = FALSE and ", id_predpts, ".gpkg already exists"))
         }
 
         ## write subset of prediction points to file
-        st_write(sub.data, paste0(id_predpts, ".shp"), quiet = TRUE)
+        st_write(sub.data, paste0(id_predpts, ".gpkg"), quiet = TRUE)
 
         ## Add to existing ssn
         new.index <- length(ssn$preds) + 1
diff --git a/R/ssn_subset.R b/R/ssn_subset.R
index 4e23b8e..5a7b4aa 100644
--- a/R/ssn_subset.R
+++ b/R/ssn_subset.R
@@ -45,7 +45,7 @@
 #' ## Import SSN object
 #' copy_lsn_to_temp() ## Only needed for this example
 #' mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-#'   predpts = c("pred1km.shp", "Knapp"),
+#'   predpts = "pred1km",
 #'   overwrite = TRUE
 #' )
 #'
@@ -63,6 +63,12 @@
 #'   overwrite = TRUE
 #' )
 ssn_subset <- function(ssn, path, subset, clip = FALSE, overwrite = FALSE) {
+  ## Add .ssn extension if necessary
+  if (substr(path, nchar(path) - 3, nchar(path)) != ".ssn") {
+    print(paste0("path must include .ssn extension. ", path, " updated to ", paste0(path, ".ssn")))
+    path <- paste0(path, ".ssn")
+  }
+
   file <- path
 
   suppressWarnings({
@@ -140,8 +146,8 @@ ssn_subset <- function(ssn, path, subset, clip = FALSE, overwrite = FALSE) {
       rm(ind, ind2)
     }
 
-    ## Write sites shapefile
-    st_write(ssn.tmp$obs, paste0(file, "/sites.shp"), quiet = TRUE)
+    ## Write sites to file
+    st_write(ssn.tmp$obs, paste0(file, "/sites.gpkg"), quiet = TRUE)
 
 
     if (clip == FALSE) {
@@ -172,7 +178,6 @@ ssn_subset <- function(ssn, path, subset, clip = FALSE, overwrite = FALSE) {
 
       ## Subset edges
       ind.edges <- eval(substitute(subset), ssn.tmp$edges)
-      ## ind.edges<- eval(substitute(netID == 2), ssn.tmp$edges)
       ind.na <- is.na(ind.edges)
       ind.edges[ind.na] <- FALSE
       rm(ind.na)
@@ -201,7 +206,7 @@ ssn_subset <- function(ssn, path, subset, clip = FALSE, overwrite = FALSE) {
       edges.sub <- ssn.tmp$edges[ind.edges, ]
 
       ## Save subset of edges
-      st_write(edges.sub, paste0(ssn.tmp$path, "/edges.shp"), quiet = TRUE)
+      st_write(edges.sub, paste0(ssn.tmp$path, "/edges.gpkg"), quiet = TRUE)
 
 
       ## Subset prediction points
@@ -220,7 +225,6 @@ ssn_subset <- function(ssn, path, subset, clip = FALSE, overwrite = FALSE) {
           }
 
           ind.preds <- eval(substitute(subset), ssn.tmp$preds[[pred.name]])
-          ## ind.preds <- eval(substitute(netID == 2), ssn.tmp$preds[[pred.name]])
           ind.na <- is.na(ind.preds)
           ind.preds[ind.na] <- FALSE
           rm(ind.na)
@@ -244,7 +248,7 @@ ssn_subset <- function(ssn, path, subset, clip = FALSE, overwrite = FALSE) {
             }
             ## Subset predictions
             preds.sub <- ssn.tmp$preds[[pred.name]][ind.preds, ]
-            st_write(preds.sub, paste0(ssn.tmp$path, "/", pred.name, ".shp"),
+            st_write(preds.sub, paste0(ssn.tmp$path, "/", pred.name, ".gpkg"),
               quiet = TRUE
             )
             rm(preds.sub)
@@ -260,7 +264,7 @@ ssn_subset <- function(ssn, path, subset, clip = FALSE, overwrite = FALSE) {
       ind.dup <- !duplicated(edges.sub$netID)
       netID.list <- edges.sub$netID[ind.dup]
 
-      # copy netID files
+      ## copy netID files
       for (i in seq_len(length(netID.list))) {
         fn.old <- file.path(ssn$path, paste("netID", netID.list[i], ".dat", sep = ""))
         fn.new <- file.path(ssn.tmp$path, paste("netID", netID.list[i], ".dat", sep = ""))
diff --git a/R/ssn_write.R b/R/ssn_write.R
index fbf9f58..e0c2d25 100644
--- a/R/ssn_write.R
+++ b/R/ssn_write.R
@@ -14,7 +14,9 @@
 #'
 #' @return{ssn_write} creates an .ssn directory that contains the
 #'   spatial, topological, and attribute information stored in the
-#'   original \code{SSN} object. When \code{import = TRUE}, the
+#'   original \code{SSN} object. Spatial datasets found in the
+#'   \code{SSN} object (e.g. edges, obs, and prediction sites) are
+#'   saved in GeoPackage format. When \code{import = TRUE}, the
 #'   \code{SSN} object is imported and returned.
 #'
 #' @export
@@ -24,12 +26,15 @@
 #' copy_lsn_to_temp()
 #' ## Import SSN object with prediction sites
 #' mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-#'   predpts = c("pred1km.shp"),
+#'   predpts = "pred1km",
 #'   overwrite = TRUE
 #' )
 #'
 #' ## Write SSN to new .ssn directory
-#' ssn_write(mf04p, path = paste0(tempdir(), "/tempSSN.ssn"))
+#' ssn_write(mf04p,
+#'   path = paste0(tempdir(), "/tempSSN.ssn"),
+#'   overwrite = TRUE
+#' )
 #'
 #' ## Write SSN to .ssn directory and return SSN object
 #' tempSSN <- ssn_write(mf04p, path = paste0(
@@ -38,33 +43,51 @@
 #' ), overwrite = TRUE, import = TRUE)
 ssn_write <- function(ssn, path, overwrite = FALSE,
                       copy_dist = FALSE, import = FALSE) {
-  file <- path
+  ## Add .ssn extension if necessary
+  if (substr(path, nchar(path) - 3, nchar(path)) != ".ssn") {
+    print(paste0(
+      "path must include .ssn extension. ",
+      path, " updated to ", paste0(path, ".ssn")
+    ))
+    path <- paste0(path, ".ssn")
+  }
 
   suppressWarnings({
-    if (!file.exists(file)) {
-      dir.create(file)
+    if (!file.exists(path)) {
+      dir.create(path)
     } else {
-      if (overwrite == FALSE) stop("file exists and overwrite = FALSE")
+      if (overwrite == FALSE) stop(paste0(path, " exists and overwrite = FALSE"))
       if (overwrite == TRUE) {
-        unlink(file, recursive = TRUE)
-        dir.create(file)
+        unlink(path, recursive = TRUE)
+        dir.create(path)
       }
     }
 
     old_wd <- getwd()
     on.exit(setwd(old_wd))
-    setwd(file)
+    setwd(path)
 
-    ## Get list of files NOT associated with shapefiles
+    #######################################################################
+    ## Get vector of filenames not associated with shapefiles or geopackages
     ssn.tmp <- ssn
     pred.len <- length(ssn.tmp$preds)
 
     ssn.tmp$path <- getwd()
 
     ssn.files <- list.files(ssn$path)
+
+    ## Remove binaryID.db from the vector b/c need recreate it on import
+    ## anyway
+    if(import == TRUE) {
+      ind.bid <- ssn.files == "binaryID.db"
+      ssn.files<- ssn.files[-c(which(ind.bid))]
+    }
+
+    ind.gpkg <- substr(ssn.files, nchar(ssn.files) - 4, nchar(ssn.files)) == ".gpkg"
     ind.shp <- substr(ssn.files, nchar(ssn.files) - 3, nchar(ssn.files)) == ".shp"
 
-    sub.list <- substr(ssn.files[ind.shp], 1, nchar(ssn.files[ind.shp]) - 4)
+    sub.list <- substr(ssn.files[ind.gpkg], 1, nchar(ssn.files[ind.gpkg]) - 5)
+    sub.list <- c(sub.list, substr(ssn.files[ind.shp], 1, nchar(ssn.files[ind.shp]) - 4))
 
     ind.list <- vector(mode = "logical", length = length(ssn.files))
 
@@ -72,7 +95,8 @@ ssn_write <- function(ssn, path, overwrite = FALSE,
       tmp <- unlist(strsplit(ssn.files[j], "[.]"))
       if ((tmp[1] %in% sub.list) == TRUE) ind.list[j] <- TRUE
     }
-    ssn.files <- ssn.files[!ind.list]
+    
+    ssn.files <- ssn.files[!ind.list]  
 
     ## Copy files to new .ssn directory
     for (i in seq_len(length(ssn.files))) {
@@ -91,11 +115,11 @@ ssn_write <- function(ssn, path, overwrite = FALSE,
 
     ## Copy observed sites if they exist
     if (class(ssn.tmp$obs)[1] == c("sf")) {
-      st_write(ssn$obs, paste0(ssn.tmp$path, "/sites.shp"), quiet = TRUE)
+      st_write(ssn$obs, paste0(ssn.tmp$path, "/sites.gpkg"), quiet = TRUE)
     }
 
     ## Copy edges
-    st_write(ssn$edges, paste0(ssn.tmp$path, "/edges.shp"), quiet = TRUE)
+    st_write(ssn$edges, paste0(ssn.tmp$path, "/edges.gpkg"), quiet = TRUE)
 
     ## Copy prediction sites
     if (pred.len > 0) {
@@ -108,7 +132,7 @@ ssn_write <- function(ssn, path, overwrite = FALSE,
         pred.name <- pred.name.vec[i]
         st_write(ssn$preds[[pred.name]], paste0(
           ssn.tmp$path, "/",
-          pred.name, ".shp"
+          pred.name, ".gpkg"
         ),
         quiet = TRUE
         )
@@ -118,12 +142,12 @@ ssn_write <- function(ssn, path, overwrite = FALSE,
 
     ## Import SSN without prediction sites
     if (import == TRUE & pred.len == 0) {
-      ssn.tmp2 <- ssn_import(ssn.tmp$path, overwrite = FALSE)
+      ssn.tmp <- ssn_import(ssn.tmp$path, overwrite = TRUE)
       return(ssn.tmp2)
     }
     ## Import SSN with all prediction sites
     if (import == TRUE & pred.len > 0) {
-      ssn.tmp2 <- ssn_import(ssn.tmp$path, overwrite = FALSE)
+      ssn.tmp2 <- ssn_import(ssn.tmp$path, overwrite = TRUE)
       for (j in seq_len(pred.len)) {
         ssn.tmp2 <- ssn_import_predpts(ssn.tmp2, pred.name.vec[j])
       }
diff --git a/R/tidy.R b/R/tidy.R
index f6b2744..1460542 100644
--- a/R/tidy.R
+++ b/R/tidy.R
@@ -42,7 +42,6 @@
 #' tidy(ssn_mod)
 tidy.ssn_lm <- function(x, conf.int = FALSE,
                         conf.level = 0.95, effects = "fixed", ...) {
-
   if (conf.int && (conf.level < 0 || conf.level > 1)) {
     stop("conf.level must be between 0 and 1.", call. = FALSE)
   }
@@ -165,7 +164,6 @@ tidy.ssn_lm <- function(x, conf.int = FALSE,
 #' @export
 tidy.ssn_glm <- function(x, conf.int = FALSE,
                          conf.level = 0.95, effects = "fixed", ...) {
-
   if (conf.int && (conf.level < 0 || conf.level > 1)) {
     stop("conf.level must be between 0 and 1.", call. = FALSE)
   }
diff --git a/R/transform_anis.R b/R/transform_anis.R
index cbc7fd6..7aa6b29 100644
--- a/R/transform_anis.R
+++ b/R/transform_anis.R
@@ -1,3 +1,11 @@
+#' Perform anisotropy transformation for Euclidean distance
+#'
+#' @param xcoord_val x-coordinate
+#' @param ycoord_val y-coordinate
+#' @param rotate Anisotropy rotation parameter
+#' @param scale Anisotropy scale parameter
+#'
+#' @noRd
 transform_anis <- function(xcoord_val, ycoord_val, rotate, scale) {
   rotate_clockwise <- matrix(c(cos(rotate), sin(rotate), -sin(rotate), cos(rotate)), nrow = 2, ncol = 2, byrow = TRUE)
   scale_yaxis <- matrix(c(1, 0, 0, 1 / scale), nrow = 2, ncol = 2, byrow = TRUE)
@@ -6,6 +14,14 @@ transform_anis <- function(xcoord_val, ycoord_val, rotate, scale) {
   list(xcoord_val = new_coords[1, ], ycoord_val = new_coords[2, ])
 }
 
+#' Perform inverse anisotropy transformation for Euclidean distance
+#'
+#' @param xcoord_val x-coordinate
+#' @param ycoord_val y-coordinate
+#' @param rotate Anisotropy rotation parameter
+#' @param scale Anisotropy scale parameter
+#'
+#' @noRd
 transform_anis_inv <- function(xcoord_val, ycoord_val, rotate, scale) {
   rotate_clockwise_inv <- matrix(c(cos(rotate), -sin(rotate), sin(rotate), cos(rotate)), nrow = 2, ncol = 2, byrow = TRUE)
   scale_yaxis_inv <- matrix(c(1, 0, 0, scale), nrow = 2, ncol = 2, byrow = TRUE)
diff --git a/R/use_gloglik.R b/R/use_gloglik.R
index ccba71f..5dde5bd 100644
--- a/R/use_gloglik.R
+++ b/R/use_gloglik.R
@@ -1,3 +1,11 @@
+#' Get covariance parameter output using log likelihood
+#'
+#' @param initial_object Initial value object
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#' @param optim_dotlist Additional optim arguments
+#'
+#' @noRd
 use_gloglik <- function(initial_object, data_object, estmethod, optim_dotlist) {
   # take original parameter values and put them on optim scale
   orig2optim_object <- orig2optim(initial_object)
@@ -73,6 +81,13 @@ use_gloglik <- function(initial_object, data_object, estmethod, optim_dotlist) {
 
 
 
+#' Get covariance parameter output from log likelihood when all parameters are known
+#'
+#' @param initial_object Initial value object
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#'
+#' @noRd
 use_gloglik_known <- function(initial_object, data_object, estmethod) {
   # all parameters known
 
diff --git a/R/use_laploglik.R b/R/use_laploglik.R
index 82d06bb..f71061f 100644
--- a/R/use_laploglik.R
+++ b/R/use_laploglik.R
@@ -1,3 +1,11 @@
+#' Get covariance parameter output using Laplace log likelihood (for glms)
+#'
+#' @param initial_object Initial value object
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#' @param optim_dotlist Additional optim arguments
+#'
+#' @noRd
 use_laploglik <- function(initial_object, data_object, estmethod, optim_dotlist) {
   orig2optim_object <- orig2optim_glm(initial_object)
 
@@ -67,7 +75,13 @@ use_laploglik <- function(initial_object, data_object, estmethod, optim_dotlist)
 
 
 
-
+#' Get covariance parameter output from Laplace log likelihood (for glms) when all parameters are known
+#'
+#' @param initial_object Initial value object
+#' @param data_object Data object
+#' @param estmethod Estimation method
+#'
+#' @noRd
 use_laploglik_known <- function(initial_object, data_object, estmethod) {
   params_object <- get_params_object_glm_known(initial_object)
 
diff --git a/R/utils.R b/R/utils.R
index e78bb5e..30af5aa 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -40,7 +40,11 @@ spmodel::randcov_params
 #' @export
 spmodel::varcomp
 
-# logit function
+#' Logit function
+#'
+#' @param x Vector to take logit of
+#'
+#' @noRd
 logit <- function(x) {
   if (x < 0 | x > 1) {
     stop("logit argument must be between zero and one", call. = FALSE)
@@ -48,17 +52,28 @@ logit <- function(x) {
   log(x / (1 - x))
 }
 
-# expit function
+#' Inverse logit (expit) function
+#'
+#' @param x Vector to take inverse logit of
+#'
+#' @noRd
 expit <- function(x) {
   1 / (1 + exp(-x))
 }
 
-# remove type class
+#' Helper to remove type class prefix from certain class strings
+#'
+#' @param class_string
+#'
+#' @noRd
 remove_covtype <- function(class_string) {
   sub("^[^_]*_", "", class_string)
 }
 
-# CRAN release questions
+
+#' CRAN release questions
+#'
+#' @noRd
 release_questions <- function() {
   c(
     "Have you changed version numbers in DESCRIPTION, CITATION, and README?",
diff --git a/README.md b/README.md
index f515ffc..3c68258 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,7 @@
 [![cran checks](https://badges.cranchecks.info/worst/SSN2.svg)](https://cran.r-project.org/web/checks/check_results_SSN2.html)
 [![R-CMD-check](https://github.com/USEPA/SSN2/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/USEPA/SSN2/actions/workflows/R-CMD-check.yaml)
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![status](https://joss.theoj.org/papers/66fd932526762f8ccd8bd9c3954e0e3d/status.svg)](https://joss.theoj.org/papers/66fd932526762f8ccd8bd9c3954e0e3d)
 <!-- badges: end -->
 
 # SSN2: Spatial Modeling on Stream Networks
@@ -24,7 +25,7 @@ citation(package = "SSN2")
 #> 
 #> To cite SSN2 in publications use:
 #> 
-#>   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.1.0
+#>   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.2.0
 #> 
 #> A BibTeX entry for LaTeX users is
 #> 
@@ -32,13 +33,13 @@ citation(package = "SSN2")
 #>     title = {{SSN2}: Spatial Modeling on Stream Networks in {R}},
 #>     author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak},
 #>     year = {2023},
-#>     note = {{R} package version 0.1.0},
+#>     note = {{R} package version 0.2.0},
 #>   }
 ```
 
 ## Statement of Need
 
-Streams provide vital aquatic services that sustain wildlife, provide drinking and irrigation water, and support recreational and cultural activities.  Data are often collected at various locations on a stream network and used to characterize some scientific phenomenon in the stream. Spatial stream network (SSN) models use a spatial statistical modeling framework to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume. SSN models relate a response variable to one or more explanatory variables, a spatially independent error term (i.e., nugget), and up to three spatially dependent error terms: tail-down errors, tail-up errors, and Euclidean errors. Tail-down errors restrict spatial dependence to flow-connected sites (i.e., water flows from an upstream to a downstream site) and incorporate spatial weights (i.e., additive function) to describe the branching network between them. Tail-up errors describe spatial dependence between both flow-connected and flow-unconnected (i.e., sites that share a common downstream junction but not flow) sites, but spatial weights are not required. Euclidean errors describe spatial dependence between sites based on Euclidean distance and are governed by factors not confined to the stream network like regional geology. 
+Streams provide vital aquatic services that sustain wildlife, provide drinking and irrigation water, and support recreational and cultural activities.  Data are often collected at various locations on a stream network and used to characterize some scientific phenomenon in the stream. Spatial stream network (SSN) models use a spatial statistical modeling framework to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume. SSN models relate a response variable to one or more explanatory variables, a spatially independent error term (i.e., nugget), and up to three spatially dependent error terms: tail-down errors, tail-up errors, and Euclidean errors. Tail-down errors restrict spatial dependence to flow-connected sites (i.e., water flows from an upstream to a downstream site) and incorporate spatial weights (i.e., additive function) to describe the branching network between them. Tail-up errors describe spatial dependence between both flow-connected and flow-unconnected (i.e., sites that share a common downstream junction but not flow) sites, but spatial weights are not required. Euclidean errors describe spatial dependence between sites based on Euclidean distance and are governed by factors not confined to the stream network like regional geology. The `SSN2` **R** package is designed to help users fit SSN models to their stream network data.
 
 
 ## Installation Instructions
@@ -73,6 +74,18 @@ remotes::install_github("USEPA/SSN2", ref = "develop")
 library(SSN2)
 ```
 
+## Contributing to `SSN2`
+
+We encourage users to report bugs and/or contribute to `SSN2`. For more detail on how to do this, please see our contributing guide (`CONTRIBUTING.md`).
+
+## Getting Help
+
+There are several ways to get help with `SSN2`:
+
+1. Open a GitHub issue [link here](https://github.com/USEPA/SSN2/issues).
+2. Email the SSN support team (support@spatialstreamnetworks.com or Dumelle.Michael@epa.gov)
+3. Post on a support website like Stack Overflow or Cross Validated. 
+
 ## Example Usage
 
 Below we provide a brief example showing how to use `SSN2`. For a thorough introduction to the software, see our introductory vignette [linked here](https://usepa.github.io/SSN2/articles/introduction.html). For a list of all functions available in `SSN2`, see our function reference [linked here](https://usepa.github.io/SSN2/reference/index.html).
@@ -199,14 +212,37 @@ head(preds)
 #> 6 15.12783 14.76358 15.49208
 ```
 
-## Contributing to `SSN2`
+## Imported Packages
 
-We encourage users to propose changes to `SSN2`. Please see our contributing guide (`CONTRIBUTING.md`) for more detail.
+`SSN2` imports the following **R** packages:
 
-## EPA Disclaimer
+* generics: For exporting generic functions.
+* graphics: For visualizations (e.g., `plot()`).
+* Matrix: For efficient matrix manipulations.
+* RSQlite: For various functions that read and write (e.g., `ssn_create_distmat()`).
+* sf: For handling spatial data.
+* spmodel: For various modeling functions (e.g., `randcov_initial()`) and generic functions (e.g., `loocv()`).
+* stats: For various modeling functions (e.g., `confint()`).
+* tibble: For creating tibbles as output for various functions (e.g., `tidy()`).
+* utils: For various utility functions.
+* withr: For path handling while reading and writing.
 
-The United States Environmental Protection Agency (EPA) GitHub project code is provided on an "as is" basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.
+## Suggested Packages
+
+`SSN2` suggests the following **R** packages:
+
+* ggplot2: For vignette visualizations.
+* knitr: For vignette building.
+* rmarkdown: For vignette building.
+* sp: For making `SSN` objects from the `SSN` **R** package compatible with `SSN2`.
+* statmod: For modeling and simulation of inverse Gaussian data.
+* testthat: For unit testing.
 
 ## License
 
 This project is licensed under the GNU General Public License, [GPL-3](https://cran.r-project.org/web/licenses/GPL-3).
+
+## EPA Disclaimer
+
+The United States Environmental Protection Agency (EPA) GitHub project code is provided on an "as is" basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.
+
diff --git a/data/mf04p.rda b/data/mf04p.rda
index 5b6a052..288748b 100644
Binary files a/data/mf04p.rda and b/data/mf04p.rda differ
diff --git a/docs/404.html b/docs/404.html
index ae0e8d9..dbe33f7 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -7,66 +7,53 @@
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Page not found (404) • SSN2</title>
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a>
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-<li class="nav-item">
-  <a class="nav-link" href="news/index.html">Changelog</a>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
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-<form class="form-inline my-2 my-lg-0" role="search">
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-</form>
-
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+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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-      <img src="" class="logo" alt=""><h1>Page not found (404)</h1>
-      
+
+      <h1>Page not found (404)</h1>
+
     </div>
 
 Content not found. Please use links in the navbar.
@@ -76,21 +63,19 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html
index a2c5ec4..e79a6c5 100644
--- a/docs/CONTRIBUTING.html
+++ b/docs/CONTRIBUTING.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Contributing to SSN2 development • SSN2</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Contributing to SSN2 development"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="pkgdown-footer-right">
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 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>GNU General Public License</h1>
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@@ -220,7 +206,7 @@ <h3 id="id_17-interpretation-of-sections-15-and-16">17. Interpretation of Sectio
 <h2 id="how-to-apply-these-terms-to-your-new-programs">How to Apply These Terms to Your New Programs<a class="anchor" aria-label="anchor" href="#how-to-apply-these-terms-to-your-new-programs"></a></h2>
 <p>If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.</p>
 <p>To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="sc">&lt;</span>one line to give the program<span class="st">'s name and a brief idea of what it does.&gt;</span></span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="sc">&lt;</span>one line to give the program<span class="st">'s name and a brief idea of what it does.&gt;</span></span></code></pre></div>
 <p>Copyright (C) <year><name of author></name></year></p>
 <p>This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.</p>
 <p>This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.</p>
@@ -234,23 +220,23 @@ <h2 id="how-to-apply-these-terms-to-your-new-programs">How to Apply These Terms
 </div>
 </div>
 
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 <div class="pkgdown-footer-right">
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diff --git a/docs/articles/index.html b/docs/articles/index.html
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@@ -71,7 +55,8 @@
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>An Introduction to Spatial Stream Network Modeling in R Using SSN2</h1>
+
+      <h1>An Introduction to Spatial Stream Network Modeling in R Using SSN2</h1>
                         <h4 data-toc-skip class="author">Michael
 Dumelle, Erin Peterson, Jay M. Ver Hoef, Alan Pearse, and Dan Isaak</h4>
             
@@ -91,7 +76,7 @@ <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#backg
 flow, and differences in flow volume throughout the network <span class="citation">(Erin E. Peterson et al. 2013)</span>. In addition,
 stream networks are embedded within a spatial environment, which can
 also influence observations on the stream network. Traditional spatial
-statistical models, which are based solely on Eucliean distance, often
+statistical models, which are based solely on Euclidean distance, often
 fail to adequately describe these unique and complex spatial
 dependencies.</p>
 <p>Spatial stream network models are based on a moving-average
@@ -112,8 +97,8 @@ <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#backg
 and Ver Hoef 2010)</span>. As a result, non-zero covariances are
 restricted to flow-connected sites in a tail-up model. In a tail-down
 model, the moving average function points in the downstream direction.
-In contrast to the tail-up model, tail-down models describe both
-flow-connected and flow-unconnected relationships, although covariances
+In contrast to the tail-up model, tail-down models allow for both
+flow-connected and flow-unconnected autocorrelation, autocorrelation
 will always be equal or stronger for flow-unconnected sites than
 flow-connected sites separated by equal stream distances <span class="citation">(J. M. Ver Hoef and Peterson 2010)</span>. In the
 tail-down model, covariance is a function of stream distance and weights
@@ -132,35 +117,40 @@ <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#backg
 summarize spatial stream network models and make predictions at
 unobserved locations (Kriging). <code>SSN2</code> is an updated version
 of the <code>SSN</code> <strong>R</strong> package <span class="citation">(J. Ver Hoef et al. 2014)</span>. Why did we create
-<code>SSN2</code> to replace <code>SSN</code>? There are two main
-reasons:</p>
+<code>SSN2</code> to replace the <code>SSN</code> <strong>R</strong>
+package? There are two main reasons:</p>
 <ol style="list-style-type: decimal">
 <li>
-<p><code>SSN</code> depends on the <code>rgdal</code> <span class="citation">(Bivand, Keitt, and Rowlingson 2021)</span>,
-<code>rgeos</code> <span class="citation">(Bivand and Rundel
-2020)</span>, and <code>maptools</code> <span class="citation">(Bivand
-and Lewin-Koh 2021)</span> <strong>R</strong> packages, which are being
-retired in October, 2023. Their functionality has been replaced and
-modernized by the <code>sf</code> package <span class="citation">(Pebesma 2018)</span>. <code>SSN2</code> depends on
+<p>The <code>SSN</code> <strong>R</strong> package depends on the
+<code>rgdal</code> <span class="citation">(Bivand, Keitt, and Rowlingson
+2021)</span>, <code>rgeos</code> <span class="citation">(Bivand and
+Rundel 2020)</span>, and <code>maptools</code> <span class="citation">(Bivand and Lewin-Koh 2021)</span> <strong>R</strong>
+packages, which were retired in October, 2023. Their functionality has
+been replaced and modernized by the <code>sf</code> package <span class="citation">(Pebesma 2018)</span>. <code>SSN2</code> depends on
 <code>sf</code> instead of <code>rgdal</code>, <code>rgeos</code>, and
 <code>maptools</code>, reflecting this broader change regarding handling
 spatial data in <strong>R</strong>.</p>
 <ul>
 <li>See <span class="citation">Nowosad (2023)</span> for more
 information regarding the retirement of <code>rgdal</code>,
-<code>rgeos</code>, and <code>maptools</code>, available at <a href="https://geocompx.org//post/2023/rgdal-retirement" class="external-link uri">https://geocompx.org//post/2023/rgdal-retirement</a> .</li>
+<code>rgeos</code>, and <code>maptools</code>, available at <a href="https://geocompx.org//post/2023/rgdal-retirement" class="external-link">this
+link</a>.</li>
 </ul>
 </li>
-<li><p>There are features we added to <code>SSN2</code> that would have
-been difficult to implement in <code>SSN</code> without a massive
-restructuring of <code>SSN</code>’s foundation, so we created a new
-package. For example, the <code>SSN</code> objects in <code>SSN2</code>
-are S3 objects but the <code>SSN</code> objects in <code>SSN</code> were
-S4 objects. Additionally, many functions were rewritten and/or
-repurposed in <code>SSN2</code> to use generic functions (e.g., block
-prediction in <code>SSN2</code> is performed using
-<code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> while in <code>SSN</code> it was performed using
-<code>BlockPredict()</code>).</p></li>
+<li><p>There are features added to <code>SSN2</code> that would have
+been difficult to implement in the <code>SSN</code> <strong>R</strong>
+package without a massive restructuring of its foundation, so a new
+package was created. For example, the <code>SSN</code> objects in
+<code>SSN2</code> are S3 objects but the <code>SSN</code> objects in the
+<code>SSN</code> <strong>R</strong> package were S4 objects.
+Additionally, many functions were rewritten and/or repurposed in
+<code>SSN2</code> to use generic functions (e.g., block prediction in
+<code>SSN2</code> is performed using <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> while in the
+<code>SSN</code> <strong>R</strong> package it was performed using
+<code>BlockPredict()</code>). Moreover, <code>SSN2</code> leverages many
+of the tools in the <code>spmodel</code> <strong>R</strong> package for
+spatial statistical modeling <span class="citation">(Dumelle, Higham,
+and Ver Hoef 2023)</span>.</p></li>
 </ol>
 <p>This vignette provides an overview of basic features in
 <code>SSN2</code>. We load <code>SSN2</code> by running</p>
@@ -172,11 +162,10 @@ <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#backg
 running</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/citation.html" class="external-link">citation</a></span><span class="op">(</span>package <span class="op">=</span> <span class="st">"SSN2"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; To cite SSN2 in publications use:</span></span>
+<pre><code><span><span class="co">#&gt; To cite SSN2 in publications use:</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt;   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2:</span></span>
-<span><span class="co">#&gt;   Spatial Modeling on Stream Networks in R. R package version 0.1.0</span></span>
+<span><span class="co">#&gt;   Spatial Modeling on Stream Networks in R. R package version 0.2.0</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; A BibTeX entry for LaTeX users is</span></span>
 <span><span class="co">#&gt; </span></span>
@@ -184,32 +173,38 @@ <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#backg
 <span><span class="co">#&gt;     title = {{SSN2}: Spatial Modeling on Stream Networks in {R}},</span></span>
 <span><span class="co">#&gt;     author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak},</span></span>
 <span><span class="co">#&gt;     year = {2023},</span></span>
-<span><span class="co">#&gt;     note = {{R} package version 0.1.0},</span></span>
+<span><span class="co">#&gt;     note = {{R} package version 0.2.0},</span></span>
 <span><span class="co">#&gt;   }</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="input-data">Input Data<a class="anchor" aria-label="anchor" href="#input-data"></a>
 </h2>
-<p>Spatial input data must be pre-processed before it can be used to fit
-spatial stream network models in the <code>SSN2</code> package. This
-information is generated using the Spatial Tools for the Analysis of
-River Systems (STARS) toolset for ArcGIS Desktop versions 9.3x-10.8x
-<span class="citation">(E. Peterson and Ver Hoef 2014)</span>. Note that
-<code>STARS</code> is designed to work with existing streams data in
-vector format. The <code>openSTARS</code> R package <span class="citation">(Kattwinkel and Szöcs 2022)</span> provides an open
-source alternative to the STARS toolset, which depends on GRASS GIS. In
-contrast to <code>STARS</code>, <code>openSTARS</code> is designed to
-derive streams in raster format from a digital elevation model (DEM).
-Both tools output a non-proprietary <code>.ssn</code> folder (i.e.,
-directory) which contains all of the spatial, topological and attribute
-information needed to fit a spatial stream network model using
-<code>SSN2</code>. This includes:</p>
+<p>The streams, observation, and prediction datasets must be
+pre-processed prior to fitting SSN models and making predictions at
+unobserved locations using <code>SSN2</code>. Previously, the STARS
+toolset for ArcGIS Desktop versions 9.3x - 10.8x <span class="citation">(E. Peterson and Ver Hoef 2014)</span> or the
+<code>openSTARS</code> <strong>R</strong> package <span class="citation">(<strong>kattwinkel2020preparing?</strong>)</span> were
+used to generate spatial information required for model fitting and
+prediction. However, both software packages have recently been retired
+and are replaced by the <code>SSNbler</code> <strong>R</strong> package
+<span class="citation">(Erin E. Peterson et al. 2024)</span>, which is a
+new, <strong>R</strong>-based version of the STARS tools.
+<code>SSNbler</code> is currently available on GitHub (<a href="https://github.com/pet221/SSNbler/" class="external-link">link here</a>), will soon be
+available on CRAN, and contains several useful resources that guide
+users through these pre-processing steps. Pre-processing using either
+<code>SSNbler</code>, STARS, or <code>openSTARS</code> ends with the
+creatino of a <code>.ssn</code> folder, which is non-proprietary. Files
+residing in the <code>.ssn</code> folder are read into R using
+<code><a href="../reference/ssn_import.html">ssn_import()</a></code> from <code>SSN2</code> and palced into a list
+structure called an SSN object, which contains all the spatial,
+topological, and attribute information needed to leverage the modeling
+tools in <code>SSN2</code>. This includes:</p>
 <ol style="list-style-type: decimal">
-<li>edges: a shapefile of lines representing the linear geometry of the
+<li>edges: a geopackage of lines representing the linear geometry of the
 stream network(s).</li>
-<li>sites: a shapefile of site locations where observed data were
+<li>sites: a geopackage of site locations where observed data were
 collected on the stream network.</li>
-<li>prediction sites: one or more shapefiles of locations where
+<li>prediction sites: one or more geopackages of locations where
 predictions will be made. Optional.</li>
 <li>netIDx.dat for each stream network: a text file containing
 topological relationships for the line segments in edges, by
@@ -226,7 +221,9 @@ <h2 id="ssn-objects-in-ssn2">
 <ol style="list-style-type: decimal">
 <li>
 <code>edges</code>: An <code>sf</code> object that contains the
-edges with <code>LINESTRING</code> geometry.</li>
+edges with <code>LINESTRING</code> geometry (representing the linear
+geometry of the stream network/s). The endpoints of the edges are called
+nodes.</li>
 <li>
 <code>obs</code>: An <code>sf</code> object that contains the
 observed data with <code>POINT</code> geometry.</li>
@@ -252,36 +249,37 @@ <h2 id="ssn-objects-in-ssn2">
 <p><code>"SNETWORK (netID rid upDist ratio pid locID)"</code>,</p>
 <p><strong>The data used to define the <code>netgeom</code> column are
 found in the edges,</strong> <strong>observed sites, and prediction
-sites shapefiles, which are created</strong> <strong>using the
-<code>STARS</code> or <code>openSTARS</code> software.</strong> For
-edges, this includes a unique network identifier (<code>netID</code>)
-and reach (i.e., edge) identifier (<code>rid</code>), as well as the
-distance between the most downstream location on the stream network
-(i.e., stream outlet) to the upstream node of each edge segment, when
-movement is restricted to the stream network (<code>upDist</code>). The
-<code>netgeom</code> column for sites also contains the
-<code>netID</code> and <code>rid</code> for the edge on which the site
-resides. The point identifier (<code>pid</code>) is unique to each
-measurement, while the location identifier (<code>locID</code>) is
-unique to each spatial location. Note that a <code>locID</code> may have
-multiple <code>pid</code>s associated with it if there are repeated
-measurements in the observed data or multiple predictions are made at
-the same location. The <code>upDist</code> value for each site
-represents the stream distance between the stream outlet and the site
-location. Finally, the <code>ratio</code> is used to describe the
-relative position of a site on its associated edge segment. It is the
-proportional distance from the most downstream node of the edge segment
-to the site location. For example, <code>ratio</code> at a site is close
-to zero when the site is close to the most downstream node of the edge
-segment, and <code>ratio</code> at a site is close to one when the site
-is far from the most downstream node of the edge segment. Together these
-key pieces of data are used to describe which network and edge each site
-resides on, as well as where exactly the site is on each line segment.
-It may at first seem redundant to combine and store multiple numeric
-columns as text in the <code>netgeom</code> column. However, these data
-dictate how the observed and prediction sites relate to one another in
-topological space, which impacts parameter estimates and predicted
-values generated from fitted models. Storing these data as text in the
+sites geopackages, which are created</strong> <strong>using the
+<code>SSNbler</code> (recommended), <code>STARS</code>, or
+<code>openSTARS</code> software.</strong> For edges, this includes a
+unique network identifier (<code>netID</code>) and reach (i.e., edge)
+identifier (<code>rid</code>), as well as the distance between the most
+downstream location on the stream network (i.e., stream outlet) to the
+upstream node of each edge segment, when movement is restricted to the
+stream network (<code>upDist</code>). The <code>netgeom</code> column
+for sites also contains the <code>netID</code> and <code>rid</code> for
+the edge on which the site resides. The point identifier
+(<code>pid</code>) is unique to each measurement, while the location
+identifier (<code>locID</code>) is unique to each spatial location. Note
+that a <code>locID</code> may have multiple <code>pid</code>s associated
+with it if there are repeated measurements in the observed data or
+multiple predictions are made at the same location. The
+<code>upDist</code> value for each site represents the stream distance
+between the stream outlet and the site location. Finally, the
+<code>ratio</code> is used to describe the relative position of a site
+on its associated edge segment. It is the proportional distance from the
+most downstream node of the edge segment to the site location. For
+example, <code>ratio</code> at a site is close to zero when the site is
+close to the most downstream node of the edge segment, and
+<code>ratio</code> at a site is close to one when the site is far from
+the most downstream node of the edge segment. Together these key pieces
+of data are used to describe which network and edge each site resides
+on, as well as where exactly the site is on each line segment. It may at
+first seem redundant to combine and store multiple numeric columns as
+text in the <code>netgeom</code> column. However, these data dictate how
+the observed and prediction sites relate to one another in topological
+space, which impacts parameter estimates and predicted values generated
+from fitted models. Storing these data as text in the
 <code>netgeom</code> column significantly reduces the chance that these
 values are accidentally (and unknowingly) altered by a user.</p>
 <div class="section level3">
@@ -302,14 +300,14 @@ <h3 id="the-middle-fork-data">The Middle Fork Data<a class="anchor" aria-label="
 can be stored:</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/copy_lsn_to_temp.html">copy_lsn_to_temp</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="va">path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html" class="external-link">file.path</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"MiddleFork04.ssn"</span><span class="op">)</span></span></code></pre></div>
 <p>After specifying <code>path</code> (using <code><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file()</a></code>
 or <code><a href="../reference/copy_lsn_to_temp.html">copy_lsn_to_temp()</a></code>), we import the stream reaches,
 observed sites, and prediction sites:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/ssn_import.html">ssn_import</a></span><span class="op">(</span></span>
 <span>  path <span class="op">=</span> <span class="va">path</span>,</span>
-<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span>, <span class="st">"Knapp"</span><span class="op">)</span>,</span>
+<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span><span class="op">)</span>,</span>
 <span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <p>We summarise the <code>mf04p</code> data by running</p>
@@ -318,39 +316,33 @@ <h3 id="the-middle-fork-data">The Middle Fork Data<a class="anchor" aria-label="
 <pre><code><span><span class="co">#&gt; Object of class SSN</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Object includes observations on 26 variables across 45 sites within the bounding box</span></span>
-<span><span class="co">#&gt;     xmin     ymin     xmax     ymax </span></span>
-<span><span class="co">#&gt; -1531385  2521181 -1498448  2540274 </span></span>
+<span><span class="co">#&gt;       xmin       ymin       xmax       ymax </span></span>
+<span><span class="co">#&gt; -1531384.6   914394.3 -1498447.7   933487.5 </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Object also includes 3 sets of prediction points with a total of 2102 locations</span></span>
+<span><span class="co">#&gt; Object also includes 2 sets of prediction points with a total of 829 locations</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Variable names are (found using names(object)):</span></span>
+<span><span class="co">#&gt; Variable names are (found using ssn_names(object)):</span></span>
 <span><span class="co">#&gt; $obs</span></span>
-<span><span class="co">#&gt;  [1] "STREAMNAME" "COMID"      "CDRAINAG"   "AREAWTMAP"  "SLOPE"     </span></span>
-<span><span class="co">#&gt;  [6] "ELEV_DEM"   "Source"     "Summer_mn"  "MaxOver20"  "C16"       </span></span>
-<span><span class="co">#&gt; [11] "C20"        "C24"        "FlowCMS"    "AirMEANc"   "AirMWMTc"  </span></span>
-<span><span class="co">#&gt; [16] "NEAR_X"     "NEAR_Y"     "rid"        "ratio"      "afvArea"   </span></span>
-<span><span class="co">#&gt; [21] "upDist"     "locID"      "netID"      "pid"        "geometry"  </span></span>
-<span><span class="co">#&gt; [26] "netgeom"   </span></span>
+<span><span class="co">#&gt;  [1] "rid"        "pid"        "STREAMNAME" "COMID"      "AREAWTMAP" </span></span>
+<span><span class="co">#&gt;  [6] "SLOPE"      "ELEV_DEM"   "Source"     "Summer_mn"  "MaxOver20" </span></span>
+<span><span class="co">#&gt; [11] "C16"        "C20"        "C24"        "FlowCMS"    "AirMEANc"  </span></span>
+<span><span class="co">#&gt; [16] "AirMWMTc"   "rcaAreaKm2" "h2oAreaKm2" "ratio"      "snapdist"  </span></span>
+<span><span class="co">#&gt; [21] "upDist"     "afvArea"    "locID"      "netID"      "netgeom"   </span></span>
+<span><span class="co">#&gt; [26] "geometry"  </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $pred1km</span></span>
-<span><span class="co">#&gt;  [1] "COMID"     "GNIS_NAME" "CDRAINAG"  "AREAWTMAP" "SLOPE"     "ELEV_DEM" </span></span>
-<span><span class="co">#&gt;  [7] "FlowCMS"   "AirMEANc"  "AirMWMTc"  "NEAR_X"    "NEAR_Y"    "rid"      </span></span>
-<span><span class="co">#&gt; [13] "ratio"     "afvArea"   "upDist"    "locID"     "netID"     "pid"      </span></span>
-<span><span class="co">#&gt; [19] "geometry"  "netgeom"  </span></span>
+<span><span class="co">#&gt;  [1] "rid"        "pid"        "COMID"      "AREAWTMAP"  "SLOPE"     </span></span>
+<span><span class="co">#&gt;  [6] "ELEV_DEM"   "FlowCMS"    "AirMEANc"   "AirMWMTc"   "rcaAreaKm2"</span></span>
+<span><span class="co">#&gt; [11] "h2oAreaKm2" "ratio"      "snapdist"   "upDist"     "afvArea"   </span></span>
+<span><span class="co">#&gt; [16] "locID"      "netID"      "netgeom"    "geom"      </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $CapeHorn</span></span>
-<span><span class="co">#&gt;  [1] "COMID"     "GNIS_NAME" "CDRAINAG"  "AREAWTMAP" "SLOPE"     "ELEV_DEM" </span></span>
-<span><span class="co">#&gt;  [7] "FlowCMS"   "AirMEANc"  "AirMWMTc"  "NEAR_X"    "NEAR_Y"    "rid"      </span></span>
-<span><span class="co">#&gt; [13] "ratio"     "afvArea"   "upDist"    "locID"     "netID"     "pid"      </span></span>
-<span><span class="co">#&gt; [19] "geometry"  "netgeom"  </span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; $Knapp</span></span>
-<span><span class="co">#&gt;  [1] "COMID"     "GNIS_NAME" "CDRAINAG"  "AREAWTMAP" "SLOPE"     "ELEV_DEM" </span></span>
-<span><span class="co">#&gt;  [7] "FlowCMS"   "AirMEANc"  "AirMWMTc"  "NEAR_X"    "NEAR_Y"    "rid"      </span></span>
-<span><span class="co">#&gt; [13] "ratio"     "afvArea"   "upDist"    "locID"     "netID"     "pid"      </span></span>
-<span><span class="co">#&gt; [19] "geometry"  "netgeom"</span></span></code></pre>
+<span><span class="co">#&gt;  [1] "rid"        "pid"        "COMID"      "AREAWTMAP"  "SLOPE"     </span></span>
+<span><span class="co">#&gt;  [6] "ELEV_DEM"   "FlowCMS"    "AirMEANc"   "AirMWMTc"   "rcaAreaKm2"</span></span>
+<span><span class="co">#&gt; [11] "h2oAreaKm2" "ratio"      "snapdist"   "upDist"     "afvArea"   </span></span>
+<span><span class="co">#&gt; [16] "locID"      "netID"      "netgeom"    "geom"</span></span></code></pre>
 <p>We see that <code>mf04p</code> contains 45 observation sites and a
-total of 2102 prediction sites stored in three different prediction
+total of 829 prediction sites stored in two different prediction
 datasets. We will explore several of these variables throughout the rest
 of the vignette:</p>
 <ul>
@@ -386,7 +378,7 @@ <h3 id="the-middle-fork-data">The Middle Fork Data<a class="anchor" aria-label="
 print the names of the prediction datasets to the console</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">mf04p</span><span class="op">$</span><span class="va">preds</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; [1] "pred1km"  "CapeHorn" "Knapp"</span></span></code></pre>
+<pre><code><span><span class="co">#&gt; [1] "pred1km"  "CapeHorn"</span></span></code></pre>
 <p>We view the Middle Fork stream network, overlay the observed sites
 where data were collected using brown circles, and overlay the
 <code>pred1km</code> prediction locations using smaller, blue triangles
@@ -405,7 +397,7 @@ <h3 id="the-middle-fork-data">The Middle Fork Data<a class="anchor" aria-label="
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ssn_create_distmat.html">ssn_create_distmat</a></span><span class="op">(</span></span>
 <span>  ssn.object <span class="op">=</span> <span class="va">mf04p</span>,</span>
-<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span>, <span class="st">"Knapp"</span><span class="op">)</span>,</span>
+<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span><span class="op">)</span>,</span>
 <span>  among_predpts <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
 <span><span class="op">)</span></span></code></pre></div>
@@ -434,26 +426,44 @@ <h3 id="sec:lm">Linear SSN Models<a class="anchor" aria-label="anchor" href="#se
 linear models are often used when the response variable is binary, a
 count, or highly skewed.</p>
 <p>Linear spatial stream network models for a quantitative response
-vector <span class="math inline">\(\mathbf{y}\)</span> have spatially
-dependent random errors and are often parameterized as</p>
-<span class="math display">\[\begin{equation*}
-\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} +
-\boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon},
-\end{equation*}\]</span>
-<p>where <span class="math inline">\(\mathbf{X}\)</span> is a matrix of
-explanatory variables (usually including a column of 1’s for an
-intercept), <span class="math inline">\(\boldsymbol{\beta}\)</span> is a
-vector of fixed effects that describe the average impact of <span class="math inline">\(\mathbf{X}\)</span> on <span class="math inline">\(\mathbf{y}\)</span>, <span class="math inline">\(\boldsymbol{\tau}_{tu}\)</span> is a vector of
-spatially dependent (correlated) tail-up random errors, <span class="math inline">\(\boldsymbol{\tau}_{td}\)</span> is a vector of
-spatially dependent (correlated) tail-down random errors, <span class="math inline">\(\boldsymbol{\tau}_{eu}\)</span> is a vector of
-spatially dependent (correlated) Euclidean random errors, and <span class="math inline">\(\boldsymbol{\epsilon}\)</span> is a vector of
-spatially independent (uncorrelated) random errors. The spatial
-dependence of each <span class="math inline">\(\boldsymbol{\tau}\)</span> term is explicitly
-specified using a spatial covariance function that incorporates the
-variance of the respective <span class="math inline">\(\boldsymbol{\tau}\)</span> term, often called a
-partial sill, and a range parameter that controls the behavior of the
-respective spatial covariance. The variance of <span class="math inline">\(\boldsymbol{\epsilon}\)</span> is often called the
-nugget (or nugget effect).</p>
+vector
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐲</mi><annotation encoding="application/x-tex">\mathbf{y}</annotation></semantics></math>
+have spatially dependent random errors and are often parameterized
+as</p>
+<math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐲</mi><mo>=</mo><mi>𝐗</mi><mi>𝛃</mi><mo>+</mo><msub><mi>𝛕</mi><mrow><mi>t</mi><mi>u</mi></mrow></msub><mo>+</mo><msub><mi>𝛕</mi><mrow><mi>t</mi><mi>d</mi></mrow></msub><mo>+</mo><msub><mi>𝛕</mi><mrow><mi>e</mi><mi>u</mi></mrow></msub><mo>+</mo><mi>𝛜</mi><mo>,</mo></mrow><annotation encoding="application/x-tex">\begin{equation*}
+\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon},
+\end{equation*}</annotation></semantics></math><p>where
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐗</mi><annotation encoding="application/x-tex">\mathbf{X}</annotation></semantics></math>
+is a matrix of explanatory variables (usually including a column of 1’s
+for an intercept),
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝛃</mi><annotation encoding="application/x-tex">\boldsymbol{\beta}</annotation></semantics></math>
+is a vector of fixed effects that describe the average impact of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐗</mi><annotation encoding="application/x-tex">\mathbf{X}</annotation></semantics></math>
+on
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐲</mi><annotation encoding="application/x-tex">\mathbf{y}</annotation></semantics></math>,
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>𝛕</mi><mrow><mi>t</mi><mi>u</mi></mrow></msub><annotation encoding="application/x-tex">\boldsymbol{\tau}_{tu}</annotation></semantics></math>
+is a vector of spatially dependent (autocorrelated) tail-up random
+errors,
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>𝛕</mi><mrow><mi>t</mi><mi>d</mi></mrow></msub><annotation encoding="application/x-tex">\boldsymbol{\tau}_{td}</annotation></semantics></math>
+is a vector of spatially dependent (autocorrelated) tail-down random
+errors,
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>𝛕</mi><mrow><mi>e</mi><mi>u</mi></mrow></msub><annotation encoding="application/x-tex">\boldsymbol{\tau}_{eu}</annotation></semantics></math>
+is a vector of spatially dependent (autocorrelated) Euclidean random
+errors, and
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝛜</mi><annotation encoding="application/x-tex">\boldsymbol{\epsilon}</annotation></semantics></math>
+is a vector of spatially independent (uncorrelated) random errors. The
+spatial dependence of each
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝛕</mi><annotation encoding="application/x-tex">\boldsymbol{\tau}</annotation></semantics></math>
+term is explicitly specified using a spatial covariance function that
+incorporates the variance of the respective
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝛕</mi><annotation encoding="application/x-tex">\boldsymbol{\tau}</annotation></semantics></math>
+term, often called a partial sill, and a range parameter that controls
+the behavior of the respective spatial covariance. The variance of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝛜</mi><annotation encoding="application/x-tex">\boldsymbol{\epsilon}</annotation></semantics></math>
+is often called the nugget (or nugget effect). Sometimes several
+unconnected networks are modeled simultaneously. For two observations on
+separate networks, tail-up and tail-down random errors are uncorrelated
+but Euclidean random errors can be autocorrelated.</p>
 <p>Suppose we are interested in studying summer mean temperature
 (<code>Summer_mn</code>) on the stream network. We can visualize the
 distribution of summer mean temperature (overlain onto the stream
@@ -462,7 +472,7 @@ <h3 id="sec:lm">Linear SSN Models<a class="anchor" aria-label="anchor" href="#se
 <code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">mf04p</span><span class="op">$</span><span class="va">edges</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">mf04p</span><span class="op">$</span><span class="va">obs</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>color <span class="op">=</span> <span class="va">Summer_mn</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_color_viridis_c</a></span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.5</span>, <span class="fl">17</span><span class="op">)</span>, option <span class="op">=</span> <span class="st">"H"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_color_viridis_c</a></span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">17</span><span class="op">)</span>, option <span class="op">=</span> <span class="st">"H"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_bw</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p><img src="introduction_files/figure-html/unnamed-chunk-11-1.png" width="700"></p>
 <p>The <code><a href="../reference/ssn_lm.html">ssn_lm()</a></code> function is used to fit linear spatial
@@ -570,10 +580,10 @@ <h3 id="sec:lm">Linear SSN Models<a class="anchor" aria-label="anchor" href="#se
 distance, which suggests that the model will benefit from at least a
 tail-up component. The takeaway for flow-unconnected and Euclidean
 semivariances is less clear – they seem to generally increase with
-distance but there are some low distances with high semivariances. This
-suggests that tail-down and Euclidean components may not be too
-impactful on the model fit. We investigate this next while we fit a
-model with all three components: tail-up, tail-down, and Euclidean.</p>
+distance but there are some short distances with high semivariances. We
+more empirically investigate the impact of each dependence structure
+next while we fit a model with all three components: tail-up, tail-down,
+and Euclidean.</p>
 <p>We fit a spatial stream network model regressing summer mean stream
 temperature on elevation and watershed-averaged precipitation using an
 exponential tail-up covariance function with additive weights created
@@ -614,28 +624,28 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <span><span class="co">#&gt;     euclid_type = "gaussian", additive = "afvArea")</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Residuals:</span></span>
-<span><span class="co">#&gt;     Min      1Q  Median      3Q     Max </span></span>
-<span><span class="co">#&gt; -3.6393 -2.0646 -0.5952  0.2143  0.7497 </span></span>
+<span><span class="co">#&gt;      Min       1Q   Median       3Q      Max </span></span>
+<span><span class="co">#&gt; -2.73430 -1.43161 -0.04368  0.83251  1.39377 </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (fixed):</span></span>
 <span><span class="co">#&gt;              Estimate Std. Error z value Pr(&gt;|z|)    </span></span>
-<span><span class="co">#&gt; (Intercept) 76.195041   7.871574   9.680  &lt; 2e-16 ***</span></span>
-<span><span class="co">#&gt; ELEV_DEM    -0.026905   0.003646  -7.379  1.6e-13 ***</span></span>
-<span><span class="co">#&gt; AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *  </span></span>
+<span><span class="co">#&gt; (Intercept) 78.214857  12.189379   6.417 1.39e-10 ***</span></span>
+<span><span class="co">#&gt; ELEV_DEM    -0.028758   0.005808  -4.952 7.35e-07 ***</span></span>
+<span><span class="co">#&gt; AREAWTMAP   -0.008067   0.004125  -1.955   0.0505 .  </span></span>
 <span><span class="co">#&gt; ---</span></span>
 <span><span class="co">#&gt; Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Pseudo R-squared: 0.6124</span></span>
+<span><span class="co">#&gt; Pseudo R-squared: 0.4157</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (covariance):</span></span>
 <span><span class="co">#&gt;               Effect     Parameter   Estimate</span></span>
-<span><span class="co">#&gt;   tailup exponential  de (parsill)  3.800e+00</span></span>
-<span><span class="co">#&gt;   tailup exponential         range  4.194e+06</span></span>
-<span><span class="co">#&gt;   taildown spherical  de (parsill)  4.480e-01</span></span>
-<span><span class="co">#&gt;   taildown spherical         range  1.647e+05</span></span>
-<span><span class="co">#&gt;      euclid gaussian  de (parsill)  1.509e-02</span></span>
-<span><span class="co">#&gt;      euclid gaussian         range  4.496e+03</span></span>
-<span><span class="co">#&gt;               nugget        nugget  2.087e-02</span></span></code></pre>
+<span><span class="co">#&gt;   tailup exponential  de (parsill)  1.348e+00</span></span>
+<span><span class="co">#&gt;   tailup exponential         range  8.987e+05</span></span>
+<span><span class="co">#&gt;   taildown spherical  de (parsill)  2.647e+00</span></span>
+<span><span class="co">#&gt;   taildown spherical         range  1.960e+05</span></span>
+<span><span class="co">#&gt;      euclid gaussian  de (parsill)  1.092e-04</span></span>
+<span><span class="co">#&gt;      euclid gaussian         range  1.805e+05</span></span>
+<span><span class="co">#&gt;               nugget        nugget  1.660e-02</span></span></code></pre>
 <p>Similar to summaries of <code><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm()</a></code> objects, summaries of
 <code><a href="../reference/ssn_lm.html">ssn_lm()</a></code> objects include the original function call,
 residuals, and a coefficients table of fixed effects. The
@@ -646,17 +656,22 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <code>AREAWTMAP</code> represents the decrease in average summer mean
 stream temperature with a one unit (mm) increase in precipitation. There
 is strong evidence that average summer mean stream temperature decreases
-with elevation (<span class="math inline">\(p\)</span>-value <span class="math inline">\(&lt; 0.001\)</span>), while there is moderate
-evidence that average summer mean stream temperature decreases with
-precipitation (<span class="math inline">\(p\)</span>-value <span class="math inline">\(\approx\)</span> 0.05). A pseudo r-squared is also
-returned, which quantifies the proportion of variability explained by
-the fixed effects. The coefficients table of covariance parameters
-describes the model’s dependence. The larger the <code>de</code>
-parameter, the more variability in the process is attributed to the
-relevant effect. Here, most of the model’s random variability comes from
-the tail-up portion of the model. The larger the <code>range</code>
-parameter, the more correlated nearby observations are with respect to
-the relevant effect.</p>
+with elevation
+(<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>p</mi><annotation encoding="application/x-tex">p</annotation></semantics></math>-value
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>&lt;</mo><mn>0.001</mn></mrow><annotation encoding="application/x-tex">&lt; 0.001</annotation></semantics></math>),
+while there is moderate evidence that average summer mean stream
+temperature decreases with precipitation
+(<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>p</mi><annotation encoding="application/x-tex">p</annotation></semantics></math>-value
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mo>≈</mo><annotation encoding="application/x-tex">\approx</annotation></semantics></math>
+0.05). A pseudo r-squared is also returned, which quantifies the
+proportion of variability explained by the fixed effects. The
+coefficients table of covariance parameters describes the model’s
+dependence. The larger the <code>de</code> parameter, the more
+variability in the process is attributed to the relevant effect. Here,
+most of the model’s random variability comes from the tail-up and
+tail-down portions of the model. The larger the <code>range</code>
+parameter, the more autocorrelated nearby observations are with respect
+to the relevant effect.</p>
 <p>We directly compare the sources of variability in the model using the
 <code>varcomp</code> function:</p>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
@@ -664,15 +679,16 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <pre><code><span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 5 × 2</span></span></span>
 <span><span class="co">#&gt;   varcomp            proportion</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Covariates (PR-sq)    0.612  </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> tailup_de             0.344  </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> taildown_de           0.040<span style="text-decoration: underline;">5</span> </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> euclid_de             0.001<span style="text-decoration: underline;">37</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> nugget                0.001<span style="text-decoration: underline;">89</span></span></span></code></pre>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Covariates (PR-sq)  0.416    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> tailup_de           0.196    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> taildown_de         0.386    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> euclid_de           0.000<span style="text-decoration: underline;">015</span>9</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> nugget              0.002<span style="text-decoration: underline;">42</span></span></span></code></pre>
 <p>Most of the variability in summer mean stream temperature is
 explained by the fixed effects of elevation and precipitation
-(<code>Covariates (PR-sq)</code>) as well as the tail-up component. Note
-that the values in the <code>proportion</code> column sum to one.</p>
+(<code>Covariates (PR-sq)</code>), the tail-up component, and the
+tail-down component. Note that the values in the <code>proportion</code>
+column sum to one.</p>
 <p>In the remainder of this subsection, we describe the broom <span class="citation">(Robinson, Hayes, and Couch 2021)</span> functions
 <code><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy()</a></code>, <code><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">glance()</a></code> and <code><a href="https://generics.r-lib.org/reference/augment.html" class="external-link">augment()</a></code>.
 <code><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy()</a></code> tidies coefficient output in a convenient
@@ -684,25 +700,26 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy</a></span><span class="op">(</span><span class="va">ssn_mod</span>, conf.int <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3 × 7</span></span></span>
-<span><span class="co">#&gt;   term        estimate std.error statistic  p.value conf.low conf.high</span></span>
-<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> (Intercept) 76.2       7.87         9.68 0   <span style="color: #949494;"> </span>     60.8    91.6     </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> AREAWTMAP   -<span style="color: #BB0000;">0.009</span><span style="color: #BB0000; text-decoration: underline;">10</span>   0.004<span style="text-decoration: underline;">46</span>     -<span style="color: #BB0000;">2.04</span> 4.14<span style="color: #949494;">e</span><span style="color: #BB0000;">- 2</span>  -<span style="color: #BB0000;">0.017</span><span style="color: #BB0000; text-decoration: underline;">8</span> -<span style="color: #BB0000;">0.000</span><span style="color: #BB0000; text-decoration: underline;">356</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> ELEV_DEM    -<span style="color: #BB0000;">0.026</span><span style="color: #BB0000; text-decoration: underline;">9</span>    0.003<span style="text-decoration: underline;">65</span>     -<span style="color: #BB0000;">7.38</span> 1.60<span style="color: #949494;">e</span><span style="color: #BB0000;">-13</span>  -<span style="color: #BB0000;">0.034</span><span style="color: #BB0000; text-decoration: underline;">1</span> -<span style="color: #BB0000;">0.019</span><span style="color: #BB0000; text-decoration: underline;">8</span></span></span></code></pre>
+<span><span class="co">#&gt;   term        estimate std.error statistic  p.value conf.low   conf.high</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> (Intercept) 78.2      12.2          6.42 1.39<span style="color: #949494;">e</span><span style="color: #BB0000;">-10</span>  54.3    102.       </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> AREAWTMAP   -<span style="color: #BB0000;">0.008</span><span style="color: #BB0000; text-decoration: underline;">07</span>   0.004<span style="text-decoration: underline;">13</span>     -<span style="color: #BB0000;">1.96</span> 5.05<span style="color: #949494;">e</span><span style="color: #BB0000;">- 2</span>  -<span style="color: #BB0000;">0.016</span><span style="color: #BB0000; text-decoration: underline;">2</span>   0.000<span style="text-decoration: underline;">018</span>7</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> ELEV_DEM    -<span style="color: #BB0000;">0.028</span><span style="color: #BB0000; text-decoration: underline;">8</span>    0.005<span style="text-decoration: underline;">81</span>     -<span style="color: #BB0000;">4.95</span> 7.35<span style="color: #949494;">e</span><span style="color: #BB0000;">- 7</span>  -<span style="color: #BB0000;">0.040</span><span style="color: #BB0000; text-decoration: underline;">1</span>  -<span style="color: #BB0000;">0.017</span><span style="color: #BB0000; text-decoration: underline;">4</span></span></span></code></pre>
 <p>We glance at the model-fit statistics by running</p>
 <div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">glance</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 1 × 9</span></span></span>
 <span><span class="co">#&gt;       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span>    45     3     7  59.3  73.3  76.3  -<span style="color: #BB0000;">29.6</span>     41.9            0.612</span></span></code></pre>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span>    45     3     7  76.6  90.6  93.7  -<span style="color: #BB0000;">38.3</span>     41.8            0.416</span></span></code></pre>
 <p>The columns of this <code>tibble</code> represent:</p>
 <ul>
 <li>
 <code>n</code>: The sample size.</li>
 <li>
 <code>p</code>: The number of fixed effects (linearly independent
-columns in <span class="math inline">\(\mathbf{X}\)</span>).</li>
+columns in
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐗</mi><annotation encoding="application/x-tex">\mathbf{X}</annotation></semantics></math>).</li>
 <li>
 <code>npar</code>: The number of estimated covariance
 parameters.</li>
@@ -734,7 +751,7 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <pre><code><span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 10</span></span></span>
 <span><span class="co">#&gt;   model        n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> ssn_mod     45     3     7  59.3  73.3  76.3  -<span style="color: #BB0000;">29.6</span>     41.9            0.612</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> ssn_mod     45     3     7  76.6  90.6  93.7  -<span style="color: #BB0000;">38.3</span>     41.8            0.416</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> ssn_mod2    45     3     3 130.  136.  137.   -<span style="color: #BB0000;">65.1</span>     41.9            0.180</span></span></code></pre>
 <p>Often AIC and AICc are used for model selection, as they balance
 model fit and model simplicity. The lower AIC and AICc for the original
@@ -773,8 +790,8 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <pre><code><span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 10</span></span></span>
 <span><span class="co">#&gt;   model       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> ml_mod     45     3     7  39.3  59.3  65.8  -<span style="color: #BB0000;">19.6</span>     44.6           0.609 </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> ml_mod2    45     2     7  56.1  74.1  79.3  -<span style="color: #BB0000;">28.1</span>     42.0           0.034<span style="text-decoration: underline;">4</span></span></span></code></pre>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> ml_mod     45     3     7  60.2  80.2  86.7  -<span style="color: #BB0000;">30.1</span>     45.4           0.461 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> ml_mod2    45     2     7  72.9  90.9  96.0  -<span style="color: #BB0000;">36.4</span>     44.9           0.023<span style="text-decoration: underline;">7</span></span></span></code></pre>
 <p>Elevation seems important to model fit, as evidenced by the lower
 AIC. <span class="citation">Erin E. Peterson and Ver Hoef (2010)</span>
 describe a two-step model procedure for model selection based on AIC
@@ -797,17 +814,15 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">loocv_mod</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span>
 <span><span class="va">loocv_mod</span><span class="op">$</span><span class="va">RMSPE</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; [1] 0.4365597</span></span></code></pre>
+<pre><code><span><span class="co">#&gt; [1] 0.4800386</span></span></code></pre>
 <div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">loocv_mod2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv</a></span><span class="op">(</span><span class="va">ssn_mod2</span><span class="op">)</span></span>
 <span><span class="va">loocv_mod2</span><span class="op">$</span><span class="va">RMSPE</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; [1] 0.8150283</span></span></code></pre>
+<pre><code><span><span class="co">#&gt; [1] 0.8150308</span></span></code></pre>
 <p><code>ssn_mod</code> is the better model with respect to
 <code>AIC</code>, <code>AICc</code>, and <code>RMSPE</code> and shortly
-we use it to return model diagnostics and make predictions.
-<code><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv()</a></code> predictions using <code>ssn_mod</code> are
-typically within 0.437. of the true summer mean stream temperature. Note
-that model comparison using <code><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv()</a></code> does not depend on the
+we use it to return model diagnostics and make predictions. Note that
+model comparison using <code><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv()</a></code> does not depend on the
 estimation method (ML vs REML).</p>
 <p>We augment the data with model diagnostics by running</p>
 <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
@@ -816,21 +831,21 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <pre><code><span><span class="co">#&gt; Simple feature collection with 45 features and 9 fields</span></span>
 <span><span class="co">#&gt; Geometry type: POINT</span></span>
 <span><span class="co">#&gt; Dimension:     XY</span></span>
-<span><span class="co">#&gt; Bounding box:  xmin: -1530805 ymin: 2527111 xmax: -1503079 ymax: 2537823</span></span>
-<span><span class="co">#&gt; Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version</span></span>
+<span><span class="co">#&gt; Bounding box:  xmin: -1530805 ymin: 920324.3 xmax: -1503079 ymax: 931036.6</span></span>
+<span><span class="co">#&gt; Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 45 × 10</span></span></span>
-<span><span class="co">#&gt;    Summer_mn ELEV_DEM AREAWTMAP .fitted .resid   .hat  .cooksd .std.resid pid  </span></span>
-<span><span class="co">#&gt;  <span style="color: #BCBCBC;">*</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span>     11.4      <span style="text-decoration: underline;">1</span>977      940.    14.4  -<span style="color: #BB0000;">3.07</span> 0.091<span style="text-decoration: underline;">5</span> 0.096<span style="text-decoration: underline;">2</span>       -<span style="color: #BB0000;">1.78</span>  1    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>     10.7      <span style="text-decoration: underline;">1</span>984     <span style="text-decoration: underline;">1</span>087.    12.9  -<span style="color: #BB0000;">2.20</span> 0.114  0.004<span style="text-decoration: underline;">71</span>      -<span style="color: #BB0000;">0.352</span> 2    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span>     10.4      <span style="text-decoration: underline;">1</span>993     <span style="text-decoration: underline;">1</span>087.    12.7  -<span style="color: #BB0000;">2.25</span> 0.037<span style="text-decoration: underline;">2</span> 0.007<span style="text-decoration: underline;">24</span>      -<span style="color: #BB0000;">0.764</span> 3    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span>     10.1      <span style="text-decoration: underline;">2</span>007     <span style="text-decoration: underline;">1</span>087.    12.3  -<span style="color: #BB0000;">2.18</span> 0.025<span style="text-decoration: underline;">1</span> 0.001<span style="text-decoration: underline;">53</span>      -<span style="color: #BB0000;">0.427</span> 4    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span>     10.1      <span style="text-decoration: underline;">2</span>009     <span style="text-decoration: underline;">1</span>087.    12.3  -<span style="color: #BB0000;">2.13</span> 0.037<span style="text-decoration: underline;">4</span> 0.000<span style="text-decoration: underline;">583</span>     -<span style="color: #BB0000;">0.216</span> 5    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span>      9.81     <span style="text-decoration: underline;">2</span>012     <span style="text-decoration: underline;">1</span>109.    12.0  -<span style="color: #BB0000;">2.16</span> 0.060<span style="text-decoration: underline;">2</span> 0.015<span style="text-decoration: underline;">0</span>       -<span style="color: #BB0000;">0.863</span> 6    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span>      9.76     <span style="text-decoration: underline;">2</span>023     <span style="text-decoration: underline;">1</span>116.    11.6  -<span style="color: #BB0000;">1.85</span> 0.073<span style="text-decoration: underline;">6</span> 0.007<span style="text-decoration: underline;">39</span>       0.549 7    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span>      9.77     <span style="text-decoration: underline;">2</span>023     <span style="text-decoration: underline;">1</span>116.    11.6  -<span style="color: #BB0000;">1.84</span> 0.064<span style="text-decoration: underline;">8</span> 0.006<span style="text-decoration: underline;">87</span>       0.564 8    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span>      9.53     <span style="text-decoration: underline;">2</span>026     <span style="text-decoration: underline;">1</span>130.    11.4  -<span style="color: #BB0000;">1.87</span> 0.112  0.001<span style="text-decoration: underline;">52</span>       0.202 9    </span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span>     12.6      <span style="text-decoration: underline;">1</span>988      864.    14.9  -<span style="color: #BB0000;">2.28</span> 0.049<span style="text-decoration: underline;">8</span> 0.009<span style="text-decoration: underline;">64</span>      -<span style="color: #BB0000;">0.762</span> 10   </span></span>
+<span><span class="co">#&gt;    Summer_mn ELEV_DEM AREAWTMAP .fitted  .resid    .hat .cooksd .std.resid pid  </span></span>
+<span><span class="co">#&gt;  <span style="color: #BCBCBC;">*</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span>      14.9     <span style="text-decoration: underline;">1</span>947     <span style="text-decoration: underline;">1</span>001.    14.1  0.770  0.072<span style="text-decoration: underline;">4</span>  2.74<span style="color: #949494;">e</span><span style="color: #BB0000;">-3</span>     0.337  1    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>      14.7     <span style="text-decoration: underline;">1</span>952     <span style="text-decoration: underline;">1</span>001.    14.0  0.714  0.056<span style="text-decoration: underline;">9</span>  4.49<span style="color: #949494;">e</span><span style="color: #BB0000;">-5</span>    -<span style="color: #BB0000;">0.048</span><span style="color: #BB0000; text-decoration: underline;">6</span> 2    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span>      14.6     <span style="text-decoration: underline;">1</span>958     <span style="text-decoration: underline;">1</span>001.    13.8  0.776  0.062<span style="text-decoration: underline;">9</span>  2.59<span style="color: #949494;">e</span><span style="color: #BB0000;">-3</span>     0.352  3    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span>      15.2     <span style="text-decoration: underline;">1</span>923     <span style="text-decoration: underline;">1</span>007.    14.8  0.427  0.125   4.71<span style="color: #949494;">e</span><span style="color: #BB0000;">-2</span>     1.06   4    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span>      14.5     <span style="text-decoration: underline;">1</span>932     <span style="text-decoration: underline;">1</span>007.    14.5 -<span style="color: #BB0000;">0.043</span><span style="color: #BB0000; text-decoration: underline;">7</span> 0.035<span style="text-decoration: underline;">9</span>  3.43<span style="color: #949494;">e</span><span style="color: #BB0000;">-2</span>    -<span style="color: #BB0000;">1.69</span>   5    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span>      15.3     <span style="text-decoration: underline;">1</span>940     <span style="text-decoration: underline;">1</span>009.    14.3  1.01   0.022<span style="text-decoration: underline;">0</span>  3.29<span style="color: #949494;">e</span><span style="color: #BB0000;">-3</span>     0.670  6    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span>      15.1     <span style="text-decoration: underline;">1</span>940     <span style="text-decoration: underline;">1</span>010.    14.3  0.797  0.017<span style="text-decoration: underline;">8</span>  1.05<span style="color: #949494;">e</span><span style="color: #BB0000;">-4</span>    -<span style="color: #BB0000;">0.133</span>  7    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span>      14.9     <span style="text-decoration: underline;">1</span>945     <span style="text-decoration: underline;">1</span>013.    14.1  0.833  0.002<span style="text-decoration: underline;">13</span> 8.13<span style="color: #949494;">e</span><span style="color: #BB0000;">-5</span>    -<span style="color: #BB0000;">0.338</span>  8    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span>      15.0     <span style="text-decoration: underline;">1</span>948     <span style="text-decoration: underline;">1</span>025.    13.9  1.06   0.056<span style="text-decoration: underline;">0</span>  1.82<span style="color: #949494;">e</span><span style="color: #BB0000;">-4</span>     0.098<span style="text-decoration: underline;">8</span> 9    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span>      15.0     <span style="text-decoration: underline;">1</span>950     <span style="text-decoration: underline;">1</span>025.    13.9  1.15   0.047<span style="text-decoration: underline;">1</span>  6.84<span style="color: #949494;">e</span><span style="color: #BB0000;">-3</span>     0.660  10   </span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 35 more rows</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1 more variable: geometry &lt;POINT [m]&gt;</span></span></span></code></pre>
 <p>The columns of this tibble represent:</p>
@@ -840,7 +855,7 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <li>
 <code>ELEV_DEM</code>: Elevation.</li>
 <li>
-<code>Precipitation</code>: Precipitation.</li>
+<code>AREAWTMAP</code>: Precipitation.</li>
 <li>
 <code>.fitted</code>: The fitted values (the estimated mean given
 the explanatory variable values).</li>
@@ -862,11 +877,11 @@ <h4 id="model-summaries">Model Summaries<a class="anchor" aria-label="anchor" hr
 <p>By default, <code><a href="https://generics.r-lib.org/reference/augment.html" class="external-link">augment()</a></code> only returns the variables in the
 data used by the model. All variables from the original data are
 returned by setting <code>drop = FALSE</code>. We can write the
-augmented data to a shapefile by loading <code>sf</code> (which comes
+augmented data to a geopackage by loading <code>sf</code> (which comes
 installed alongside <code>SSN2</code>) and running</p>
 <div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/" class="external-link">sf</a></span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_write.html" class="external-link">st_write</a></span><span class="op">(</span><span class="va">aug_ssn_mod</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/aug_ssn_mod.shp"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_write.html" class="external-link">st_write</a></span><span class="op">(</span><span class="va">aug_ssn_mod</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/aug_ssn_mod.gpkg"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>Many of the model diagnostics returned by <code><a href="https://generics.r-lib.org/reference/augment.html" class="external-link">augment()</a></code> can
 be visualized by running using <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code>. For example, we plot
 the fitted values against the standardized residuals by running</p>
@@ -896,21 +911,21 @@ <h4 id="prediction-kriging">Prediction (Kriging)<a class="anchor" aria-label="an
 <pre><code><span><span class="co">#&gt; Simple feature collection with 175 features and 1 field</span></span>
 <span><span class="co">#&gt; Geometry type: POINT</span></span>
 <span><span class="co">#&gt; Dimension:     XY</span></span>
-<span><span class="co">#&gt; Bounding box:  xmin: -1530631 ymin: 2521707 xmax: -1500020 ymax: 2540253</span></span>
-<span><span class="co">#&gt; Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version</span></span>
+<span><span class="co">#&gt; Bounding box:  xmin: -1530631 ymin: 914920.7 xmax: -1500020 ymax: 933466.4</span></span>
+<span><span class="co">#&gt; Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 175 × 2</span></span></span>
-<span><span class="co">#&gt;    .fitted           geometry</span></span>
-<span><span class="co">#&gt;      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;POINT [m]&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span>    14.6 (-1520657 2536657)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>    15.0 (-1519866 2536812)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span>    14.8 (-1521823 2536911)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span>    15.0 (-1523183 2537256)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span>    15.2 (-1523860 2537452)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span>    15.1 (-1525443 2537698)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span>    15.1 (-1526397 2537254)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span>    15.0 (-1527436 2536803)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span>    14.9 (-1529043 2536449)</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span>    14.9 (-1529689 2537313)</span></span>
+<span><span class="co">#&gt;    .fitted            geometry</span></span>
+<span><span class="co">#&gt;      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;POINT [m]&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span>    14.7 (-1528406 928161.4)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>    14.7 (-1528202 928821.1)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span>    14.9 (-1528173 929414.9)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span>    14.4 (-1530218 926538.7)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span>    14.5 (-1529466 926808.1)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span>    14.5 (-1520657 929871.1)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span>    15.0 (-1519866 930025.5)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span>    14.7 (-1521823 930124.7)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span>    14.9 (-1523183 930469.7)</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span>    15.2 (-1523860 930665.8)</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 165 more rows</span></span></span></code></pre>
 <p>Here <code>.fitted</code> represents the predictions. Confidence
 intervals for the mean response or prediction intervals for the
@@ -930,10 +945,10 @@ <h4 id="prediction-kriging">Prediction (Kriging)<a class="anchor" aria-label="an
 <code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">mf04p</span><span class="op">$</span><span class="va">edges</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">aug_preds</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>color <span class="op">=</span> <span class="va">.fitted</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_color_viridis_c</a></span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.5</span>, <span class="fl">17</span><span class="op">)</span>, option <span class="op">=</span> <span class="st">"H"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_color_viridis_c</a></span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">17</span><span class="op">)</span>, option <span class="op">=</span> <span class="st">"H"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_bw</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p><img src="introduction_files/figure-html/unnamed-chunk-27-1.png" width="700"></p>
-<p>Previously we wrote out model diagnostics to a shapefile. Now we
+<p>Previously we wrote out model diagnostics to a geopackage. Now we
 write out predictions to a geopackage (recall <code>sf</code> must be
 loaded) by running</p>
 <div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
@@ -946,13 +961,12 @@ <h4 id="prediction-kriging">Prediction (Kriging)<a class="anchor" aria-label="an
 <div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">ssn_mod</span>, newdata <span class="op">=</span> <span class="st">"all"</span><span class="op">)</span></span></code></pre></div>
-<p>makes predictions for <code>pred1km</code>, <code>Knapp</code>, and
-<code>CapeHorn</code> (the names of <code>mf04p$preds</code>). Lastly,
-if there are observations (in the <code>obs</code> object) whose
-response is missing (<code>NA</code>), these observations are removed
-from model fitting and moved to a prediction data set named
-<code>.missing</code>. Then predictions can be obtained at these
-locations.</p>
+<p>makes predictions for <code>pred1km</code> and <code>CapeHorn</code>
+(the names of <code>mf04p$preds</code>). Lastly, if there are
+observations (in the <code>obs</code> object) whose response is missing
+(<code>NA</code>), these observations are removed from model fitting and
+moved to a prediction data set named <code>.missing</code>. Then
+predictions can be obtained at these locations.</p>
 <p>We can also predict the average value in a region using block
 Prediction (instead of making point predictions). We predict the average
 summer mean temperature throughout the Middle Fork stream network by
@@ -960,7 +974,7 @@ <h4 id="prediction-kriging">Prediction (Kriging)<a class="anchor" aria-label="an
 <div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">ssn_mod</span>, newdata <span class="op">=</span> <span class="st">"pred1km"</span>, block <span class="op">=</span> <span class="cn">TRUE</span>, interval <span class="op">=</span> <span class="st">"prediction"</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">#&gt;        fit      lwr      upr</span></span>
-<span><span class="co">#&gt; 1 10.73479 9.987173 11.48241</span></span></code></pre>
+<span><span class="co">#&gt; 1 10.21747 9.317693 11.11725</span></span></code></pre>
 </div>
 <div class="section level4">
 <h4 id="sec:advanced">Advanced Features<a class="anchor" aria-label="anchor" href="#sec:advanced"></a>
@@ -979,9 +993,10 @@ <h4 id="sec:advanced">Advanced Features<a class="anchor" aria-label="anchor" hre
 the <code><a href="../reference/ssn_initial.html">tailup_initial()</a></code>, <code><a href="../reference/ssn_initial.html">taildown_initial()</a></code>,
 <code><a href="../reference/ssn_initial.html">euclid_initial()</a></code>, or <code><a href="../reference/ssn_initial.html">nugget_initial()</a></code>
 function, respectively. For example, suppose we want to fix the
-Euclidean dependent error variance parameter at 1, forcing this
-component to have a moderate effect on the covariance. First, we specify
-the appropriate object by running</p>
+Euclidean dependent error variance parameter (i.e., Euclidean partial
+sill, or parsill) at 1, forcing this component to have a moderate effect
+on the covariance. First, we specify the appropriate object by
+running</p>
 <div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">euclid_init</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/ssn_initial.html">euclid_initial</a></span><span class="op">(</span><span class="st">"gaussian"</span>, de <span class="op">=</span> <span class="fl">1</span>, known <span class="op">=</span> <span class="st">"de"</span><span class="op">)</span></span>
 <span><span class="va">euclid_init</span></span></code></pre></div>
@@ -995,7 +1010,7 @@ <h4 id="sec:advanced">Advanced Features<a class="anchor" aria-label="anchor" hre
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; attr(,"class")</span></span>
 <span><span class="co">#&gt; [1] "euclid_gaussian"</span></span></code></pre>
-<p>The <code>euclid_init</code> output shows that the <code>range</code>
+<p>The <code>euclid_init</code> output shows that the <code>de</code>
 parameter has an initial value of 1 that is assumed known. The
 <code>range</code> parameter will still be estimated. Next the model is
 fit:</p>
@@ -1018,18 +1033,17 @@ <h4 id="sec:advanced">Advanced Features<a class="anchor" aria-label="anchor" hre
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (fixed):</span></span>
 <span><span class="co">#&gt; (Intercept)     ELEV_DEM    AREAWTMAP  </span></span>
-<span><span class="co">#&gt;   77.181436    -0.027921    -0.008011  </span></span>
+<span><span class="co">#&gt;   71.247739    -0.026707    -0.005522  </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (covariance):</span></span>
 <span><span class="co">#&gt;               Effect     Parameter   Estimate</span></span>
-<span><span class="co">#&gt;   tailup exponential  de (parsill)  3.882e+00</span></span>
-<span><span class="co">#&gt;   tailup exponential         range  1.668e+06</span></span>
-<span><span class="co">#&gt;   taildown spherical  de (parsill)  3.233e-02</span></span>
-<span><span class="co">#&gt;   taildown spherical         range  7.041e+03</span></span>
+<span><span class="co">#&gt;   tailup exponential  de (parsill)  1.250e+00</span></span>
+<span><span class="co">#&gt;   tailup exponential         range  4.191e+05</span></span>
+<span><span class="co">#&gt;   taildown spherical  de (parsill)  1.924e-02</span></span>
+<span><span class="co">#&gt;   taildown spherical         range  3.125e+04</span></span>
 <span><span class="co">#&gt;      euclid gaussian  de (parsill)  1.000e+00</span></span>
-<span><span class="co">#&gt;      euclid gaussian         range  8.231e+04</span></span>
-<span><span class="co">#&gt;               nugget        nugget  1.948e-02</span></span></code></pre>
-<p>Notice the Euclidean variance is 1.</p>
+<span><span class="co">#&gt;      euclid gaussian         range  9.998e+03</span></span>
+<span><span class="co">#&gt;               nugget        nugget  3.578e-02</span></span></code></pre>
 <p>Random effects can be added to an SSN model to incorporate additional
 sources of variability separate from those on the stream network. Common
 additional sources of variability modeled include repeated observations
@@ -1060,28 +1074,33 @@ <h4 id="sec:advanced">Advanced Features<a class="anchor" aria-label="anchor" hre
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (fixed):</span></span>
 <span><span class="co">#&gt; (Intercept)     ELEV_DEM    AREAWTMAP  </span></span>
-<span><span class="co">#&gt;   76.219407    -0.026705    -0.009449  </span></span>
+<span><span class="co">#&gt;   59.579720    -0.020328    -0.006484  </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (covariance):</span></span>
 <span><span class="co">#&gt;               Effect             Parameter   Estimate</span></span>
-<span><span class="co">#&gt;   tailup exponential          de (parsill)  3.688e+00</span></span>
-<span><span class="co">#&gt;   tailup exponential                 range  6.971e+05</span></span>
-<span><span class="co">#&gt;   taildown spherical          de (parsill)  1.191e-02</span></span>
-<span><span class="co">#&gt;   taildown spherical                 range  1.089e+04</span></span>
-<span><span class="co">#&gt;      euclid gaussian          de (parsill)  3.377e-02</span></span>
-<span><span class="co">#&gt;      euclid gaussian                 range  1.368e+04</span></span>
-<span><span class="co">#&gt;               nugget                nugget  2.046e-02</span></span>
-<span><span class="co">#&gt;               random  1 | as.factor(netID)  3.809e-01</span></span></code></pre>
+<span><span class="co">#&gt;   tailup exponential          de (parsill)  1.128e+00</span></span>
+<span><span class="co">#&gt;   tailup exponential                 range  2.015e+05</span></span>
+<span><span class="co">#&gt;   taildown spherical          de (parsill)  2.948e-03</span></span>
+<span><span class="co">#&gt;   taildown spherical                 range  8.357e+04</span></span>
+<span><span class="co">#&gt;      euclid gaussian          de (parsill)  1.397e+00</span></span>
+<span><span class="co">#&gt;      euclid gaussian                 range  1.096e+04</span></span>
+<span><span class="co">#&gt;               nugget                nugget  2.977e-02</span></span>
+<span><span class="co">#&gt;               random  1 | as.factor(netID)  4.370e+00</span></span></code></pre>
 <p><code>random = ~ as.factor(netID)</code> is short-hand for
 <code>random = ~ (1 | as.factor(netID))</code>, which is the more
 familiar <code>lme4</code> or <code>nlme</code> syntax.</p>
 <p>A partition factor is a variable that allows observations to be
-uncorrelated when they from different levels of the partition factors.
-For example, one may want to partition the model into two networks
-despite their adjacency because of a significant land mass or similar
-obstruction. Partition factors are modeled using a formula that contains
-a single variable passed to the <code>partition_factor</code> argument.
-We model the two networks as uncorrelated by running</p>
+uncorrelated when they do not share the same value. For example, one may
+want to partition the model into two networks (despite their adjacency)
+because of a significant land mass (or similar obstruction). In this
+case, it may be relevant to assume observations upstream of the land
+mass are not correlated with observations downstream of the land mass.
+Incorporating an effect as a partition factor is different from
+incorporating the effect as fixed (i.e., on the right-hand side of the
+<code>formula</code> argument). Partition factors affect the covariance
+portion of the model while fixed effects affect the mean portion.</p>
+<p>Partition factors are modeled using a formula that contains a single
+variable passed to the <code>partition_factor</code> argument:</p>
 <div class="sourceCode" id="cb52"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">ssn_part</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/ssn_lm.html">ssn_lm</a></span><span class="op">(</span></span>
 <span>  formula <span class="op">=</span> <span class="va">Summer_mn</span> <span class="op">~</span> <span class="va">ELEV_DEM</span> <span class="op">+</span> <span class="va">AREAWTMAP</span>,</span>
@@ -1102,53 +1121,56 @@ <h4 id="sec:advanced">Advanced Features<a class="anchor" aria-label="anchor" hre
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (fixed):</span></span>
 <span><span class="co">#&gt; (Intercept)     ELEV_DEM    AREAWTMAP  </span></span>
-<span><span class="co">#&gt;    76.32118     -0.02805     -0.00697  </span></span>
+<span><span class="co">#&gt;   77.421289    -0.028431    -0.007938  </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (covariance):</span></span>
 <span><span class="co">#&gt;               Effect     Parameter   Estimate</span></span>
-<span><span class="co">#&gt;   tailup exponential  de (parsill)  3.300e+00</span></span>
-<span><span class="co">#&gt;   tailup exponential         range  6.695e+07</span></span>
-<span><span class="co">#&gt;   taildown spherical  de (parsill)  3.618e-07</span></span>
-<span><span class="co">#&gt;   taildown spherical         range  2.233e+06</span></span>
-<span><span class="co">#&gt;      euclid gaussian  de (parsill)  1.002e-01</span></span>
-<span><span class="co">#&gt;      euclid gaussian         range  5.036e+03</span></span>
-<span><span class="co">#&gt;               nugget        nugget  2.525e-02</span></span></code></pre>
-<p>In short, the partition factor enables model fitting that builds
-independence in places not typical on a stream network but deemed
-relevant by the researcher.</p>
+<span><span class="co">#&gt;   tailup exponential  de (parsill)  1.331e+00</span></span>
+<span><span class="co">#&gt;   tailup exponential         range  9.689e+05</span></span>
+<span><span class="co">#&gt;   taildown spherical  de (parsill)  2.923e+00</span></span>
+<span><span class="co">#&gt;   taildown spherical         range  1.880e+05</span></span>
+<span><span class="co">#&gt;      euclid gaussian  de (parsill)  1.654e-04</span></span>
+<span><span class="co">#&gt;      euclid gaussian         range  2.458e+01</span></span>
+<span><span class="co">#&gt;               nugget        nugget  1.487e-02</span></span></code></pre>
+<p>Here, the partition factor ensures that observations from separate
+stream networks are completely uncorrelated.</p>
+<!-- resulting from Euclidean distance (these observations already do not share any tail-up or tail-down covariance because they are from separate stream networks). -->
 </div>
 </div>
 <div class="section level3">
 <h3 id="sec:glm">Generalized Linear SSN Models<a class="anchor" aria-label="anchor" href="#sec:glm"></a>
 </h3>
 <p>Generalized linear spatial stream network models for a response
-vector <span class="math inline">\(\mathbf{y}\)</span> have spatially
-dependent random errors and are often parameterized as</p>
-<span class="math display">\[\begin{equation*}
-g(\boldsymbol{\mu}) = \mathbf{X} \boldsymbol{\beta} +
-\boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu}
-+ \boldsymbol{\epsilon},
-\end{equation*}\]</span>
-<p>where <span class="math inline">\(\boldsymbol{\mu}\)</span> is the
-mean of <span class="math inline">\(\mathbf{y}\)</span>, <span class="math inline">\(g(\cdot)\)</span> is a link function that “links”
-<span class="math inline">\(\mathbf{\mu}\)</span> to a linear function
-of the predictor variables and random errors, and all other terms are
-the same as those defined for linear spatial stream network models.
-Rather than assuming <span class="math inline">\(y\)</span> is normally
-(Gaussian) distributed as is often the case with linear spatial stream
-network models, generalized linear spatial stream network models assume
-<span class="math inline">\(\mathbf{y}\)</span> follows one of many
-distributions and has a corresponding link function. Below we summarize
-the families of generalized linear spatial stream network models
-supported by <code>SSN2</code> their link functions, and the type of
-data typically associated with these families. For more on generalized
-linear models more generally, see <span class="citation">McCullagh and
-Nelder (1989)</span>, <span class="citation">Myers et al. (2012)</span>,
-and <span class="citation">Faraway (2016)</span>.</p>
+vector
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐲</mi><annotation encoding="application/x-tex">\mathbf{y}</annotation></semantics></math>
+have spatially dependent random errors and are often parameterized
+as</p>
+<math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mi>𝐗</mi><mi>𝛃</mi><mo>+</mo><msub><mi>𝛕</mi><mrow><mi>t</mi><mi>u</mi></mrow></msub><mo>+</mo><msub><mi>𝛕</mi><mrow><mi>t</mi><mi>d</mi></mrow></msub><mo>+</mo><msub><mi>𝛕</mi><mrow><mi>e</mi><mi>u</mi></mrow></msub><mo>+</mo><mi>𝛜</mi><mo>,</mo></mrow><annotation encoding="application/x-tex">\begin{equation*}
+g(\boldsymbol{\mu}) = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon},
+\end{equation*}</annotation></semantics></math><p>where
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝛍</mi><annotation encoding="application/x-tex">\boldsymbol{\mu}</annotation></semantics></math>
+is the mean of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐲</mi><annotation encoding="application/x-tex">\mathbf{y}</annotation></semantics></math>,
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mo>⋅</mo><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">g(\cdot)</annotation></semantics></math>
+is a link function that “links”
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝛍</mi><annotation encoding="application/x-tex">\boldsymbol{\mu}</annotation></semantics></math>
+to a linear function of the predictor variables and random errors, and
+all other terms are the same as those defined for linear spatial stream
+network models. Rather than assuming
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>y</mi><annotation encoding="application/x-tex">y</annotation></semantics></math>
+is normally (Gaussian) distributed as is often the case with linear
+spatial stream network models, generalized linear spatial stream network
+models assume
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐲</mi><annotation encoding="application/x-tex">\mathbf{y}</annotation></semantics></math>
+follows one of many distributions and has a corresponding link function.
+Below we summarize the families of generalized linear spatial stream
+network models supported by <code>SSN2</code> their link functions, and
+the type of data typically associated with these families. For more on
+generalized linear models more generally, see <span class="citation">McCullagh and Nelder (1989)</span>, <span class="citation">Myers et al. (2012)</span>, and <span class="citation">Faraway (2016)</span>.</p>
 <p>The <code><a href="../reference/ssn_glm.html">ssn_glm()</a></code> function is used to fit generalized linear
 spatial stream network models and bears many similarities to
 base-<strong>R</strong>’s <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm()</a></code> function for non-spatial
-generalized linear models. The family (i.e., resposne distribution) is
+generalized linear models. The family (i.e., response distribution) is
 controlled by the <code>family</code> argument. When <code>family</code>
 is <code>Gaussian()</code>, the model fit is equivalent to one fit using
 <code><a href="../reference/ssn_lm.html">ssn_lm()</a></code>. Note that parameters are estimated on the
@@ -1172,8 +1194,7 @@ <h3 id="sec:glm">Generalized Linear SSN Models<a class="anchor" aria-label="anch
 <tbody>
 <tr class="odd">
 <td>Gaussian</td>
-<td><span class="math inline">\(g(\mathbf{\mu}) =
-\mathbf{\mu}\)</span></td>
+<td><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mi>𝛍</mi></mrow><annotation encoding="application/x-tex">g(\mathbf{\mu}) = \mathbf{\mu}</annotation></semantics></math></td>
 <td>Identity</td>
 <td>Continuous</td>
 <td>
@@ -1182,48 +1203,42 @@ <h3 id="sec:glm">Generalized Linear SSN Models<a class="anchor" aria-label="anch
 </tr>
 <tr class="even">
 <td>Binomial</td>
-<td><span class="math inline">\(g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1
-- \mathbf{\mu}))\)</span></td>
+<td><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mo>log</mo><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mi>/</mi><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1 - \mathbf{\mu}))</annotation></semantics></math></td>
 <td>Logit</td>
 <td>Binary; Binary Count</td>
 <td><code><a href="../reference/ssn_glm.html">ssn_glm()</a></code></td>
 </tr>
 <tr class="odd">
 <td>Beta</td>
-<td><span class="math inline">\(g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1
-- \mathbf{\mu}))\)</span></td>
+<td><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mo>log</mo><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mi>/</mi><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1 - \mathbf{\mu}))</annotation></semantics></math></td>
 <td>Logit</td>
 <td>Proportion</td>
 <td><code><a href="../reference/ssn_glm.html">ssn_glm()</a></code></td>
 </tr>
 <tr class="even">
 <td>Poisson</td>
-<td><span class="math inline">\(g(\mathbf{\mu}) =
-\log(\mathbf{\mu})\)</span></td>
+<td><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mo>log</mo><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">g(\mathbf{\mu}) = \log(\mathbf{\mu})</annotation></semantics></math></td>
 <td>Log</td>
 <td>Count</td>
 <td><code><a href="../reference/ssn_glm.html">ssn_glm()</a></code></td>
 </tr>
 <tr class="odd">
 <td>Negative Binomial</td>
-<td><span class="math inline">\(g(\mathbf{\mu}) =
-\log(\mathbf{\mu})\)</span></td>
+<td><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mo>log</mo><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">g(\mathbf{\mu}) = \log(\mathbf{\mu})</annotation></semantics></math></td>
 <td>Log</td>
 <td>Count</td>
 <td><code><a href="../reference/ssn_glm.html">ssn_glm()</a></code></td>
 </tr>
 <tr class="even">
 <td>Gamma</td>
-<td><span class="math inline">\(g(\mathbf{\mu}) =
-\log(\mathbf{\mu})\)</span></td>
+<td><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mo>log</mo><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">g(\mathbf{\mu}) = \log(\mathbf{\mu})</annotation></semantics></math></td>
 <td>Log</td>
 <td>Skewed (positive continuous)</td>
 <td><code><a href="../reference/ssn_glm.html">ssn_glm()</a></code></td>
 </tr>
 <tr class="odd">
 <td>Inverse Gaussian</td>
-<td><span class="math inline">\(g(\mathbf{\mu}) =
-\log(\mathbf{\mu})\)</span></td>
+<td><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mo>log</mo><mrow><mo stretchy="true" form="prefix">(</mo><mi>𝛍</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">g(\mathbf{\mu}) = \log(\mathbf{\mu})</annotation></semantics></math></td>
 <td>Log</td>
 <td>Skewed (positive continuous)</td>
 <td><code><a href="../reference/ssn_glm.html">ssn_glm()</a></code></td>
@@ -1266,25 +1281,25 @@ <h3 id="sec:glm">Generalized Linear SSN Models<a class="anchor" aria-label="anch
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Deviance Residuals:</span></span>
 <span><span class="co">#&gt;      Min       1Q   Median       3Q      Max </span></span>
-<span><span class="co">#&gt; -2.12277 -0.43689 -0.07905  0.26269  1.28473 </span></span>
+<span><span class="co">#&gt; -3.10387 -0.36874 -0.05467  0.27401  1.37106 </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (fixed):</span></span>
 <span><span class="co">#&gt;              Estimate Std. Error z value Pr(&gt;|z|)    </span></span>
-<span><span class="co">#&gt; (Intercept) 45.549245  10.025635   4.543 5.54e-06 ***</span></span>
-<span><span class="co">#&gt; ELEV_DEM    -0.017567   0.004544  -3.866 0.000111 ***</span></span>
-<span><span class="co">#&gt; AREAWTMAP   -0.007586   0.003413  -2.222 0.026253 *  </span></span>
+<span><span class="co">#&gt; (Intercept) 45.889749   9.803696   4.681 2.86e-06 ***</span></span>
+<span><span class="co">#&gt; ELEV_DEM    -0.018206   0.004547  -4.004 6.24e-05 ***</span></span>
+<span><span class="co">#&gt; AREAWTMAP   -0.006715   0.002958  -2.270   0.0232 *  </span></span>
 <span><span class="co">#&gt; ---</span></span>
 <span><span class="co">#&gt; Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Pseudo R-squared: 0.2443</span></span>
+<span><span class="co">#&gt; Pseudo R-squared: 0.2527</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients (covariance):</span></span>
 <span><span class="co">#&gt;            Effect     Parameter   Estimate</span></span>
-<span><span class="co">#&gt;        tailup epa  de (parsill)  9.484e-01</span></span>
-<span><span class="co">#&gt;        tailup epa         range  4.290e+04</span></span>
-<span><span class="co">#&gt;   taildown mariah  de (parsill)  1.774e-02</span></span>
-<span><span class="co">#&gt;   taildown mariah         range  9.782e+06</span></span>
-<span><span class="co">#&gt;            nugget        nugget  1.008e-04</span></span>
+<span><span class="co">#&gt;        tailup epa  de (parsill)  6.358e-01</span></span>
+<span><span class="co">#&gt;        tailup epa         range  3.090e+04</span></span>
+<span><span class="co">#&gt;   taildown mariah  de (parsill)  7.500e-02</span></span>
+<span><span class="co">#&gt;   taildown mariah         range  2.878e+06</span></span>
+<span><span class="co">#&gt;            nugget        nugget  1.289e-03</span></span>
 <span><span class="co">#&gt;        dispersion    dispersion  1.000e+00</span></span></code></pre>
 <p>Similar to summaries of <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm()</a></code> objects, summaries of
 <code><a href="../reference/ssn_glm.html">ssn_glm()</a></code> objects include the original function call,
@@ -1296,10 +1311,15 @@ <h3 id="sec:glm">Generalized Linear SSN Models<a class="anchor" aria-label="anch
 <code>AREAWTMAP</code> represents the decrease in log average summer
 mean temperature with a one unit (mm) increase in precipitation. There
 is strong evidence that log average summer mean temperature decreases
-with elevation (<span class="math inline">\(p\)</span>-value <span class="math inline">\(&lt; 0.001\)</span>), while there is moderate
-evidence that log average summer mean temperature decreases with
-precipitation (<span class="math inline">\(p\)</span>-value <span class="math inline">\(\approx\)</span> 0.03). Recall that the covariance
-parameter estimates are on the link (here, log) scale.</p>
+with elevation
+(<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>p</mi><annotation encoding="application/x-tex">p</annotation></semantics></math>-value
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>&lt;</mo><mn>0.001</mn></mrow><annotation encoding="application/x-tex">&lt; 0.001</annotation></semantics></math>),
+while there is moderate evidence that log average summer mean
+temperature decreases with precipitation
+(<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>p</mi><annotation encoding="application/x-tex">p</annotation></semantics></math>-value
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mo>≈</mo><annotation encoding="application/x-tex">\approx</annotation></semantics></math>
+0.03). Recall that the covariance parameter estimates are on the link
+(here, log) scale.</p>
 <p>The Poisson model assumes that each observations mean and variance
 are equal. Often with ecological or environmental data, the variance is
 larger than the mean – this is called overdispersion. The negative
@@ -1319,13 +1339,19 @@ <h3 id="sec:glm">Generalized Linear SSN Models<a class="anchor" aria-label="anch
 <div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">loocv_pois</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv</a></span><span class="op">(</span><span class="va">ssn_pois</span><span class="op">)</span></span>
 <span><span class="va">loocv_pois</span><span class="op">$</span><span class="va">RMSPE</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; [1] 5.249275</span></span></code></pre>
+<pre><code><span><span class="co">#&gt; [1] 6.015313</span></span></code></pre>
 <div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">loocv_nb</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv</a></span><span class="op">(</span><span class="va">ssn_nb</span><span class="op">)</span></span>
 <span><span class="va">loocv_nb</span><span class="op">$</span><span class="va">RMSPE</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; [1] 5.255264</span></span></code></pre>
-<p>The Poisson has a lower <code>RMSPE</code>, which suggests no
-evidence of overdispersion.</p>
+<pre><code><span><span class="co">#&gt; [1] 5.875028</span></span></code></pre>
+<p>The negative binomial model has a lower <code>RMSPE</code>, which
+suggests some evidence of overdispersion. Other ways to assess the
+impact of overdispersion include 1) comparing the models using a
+likelihood-based fit statistic like AIC; if the AIC values are similar
+there is little evidence of overdispersion – and 2) inspecting the
+dispersion parameter estimates of the negative binomial model; if the
+dispersion parameter is very large, there is little evidence of
+overdispersion.</p>
 <p>All advanced modeling features discussed for linear spatial stream
 network models (e.g., fixing covariance parameter values, random
 effects, partition factors) are also available for generalized linear
@@ -1361,7 +1387,7 @@ <h3 id="simulating-spatial-stream-network-data">Simulating Spatial Stream Networ
 <span>  samples <span class="op">=</span> <span class="fl">1</span></span>
 <span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">sims</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; [1] -0.8022248 -0.2953168  0.7179851 -0.2965733 -0.2811469 -0.1010336</span></span></code></pre>
+<pre><code><span><span class="co">#&gt; [1] -0.8022248 -0.4047795  0.3819883 -1.0798647 -0.8943244 -0.4111767</span></span></code></pre>
 <p>We simulate binomial (presence/absence) data by running</p>
 <div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sims</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/ssn_simulate.html">ssn_simulate</a></span><span class="op">(</span></span>
@@ -1381,9 +1407,9 @@ <h3 id="simulating-spatial-stream-network-data">Simulating Spatial Stream Networ
 <span><span class="co">#&gt; [1,] 0 1</span></span>
 <span><span class="co">#&gt; [2,] 0 1</span></span>
 <span><span class="co">#&gt; [3,] 0 1</span></span>
-<span><span class="co">#&gt; [4,] 1 0</span></span>
+<span><span class="co">#&gt; [4,] 1 1</span></span>
 <span><span class="co">#&gt; [5,] 1 0</span></span>
-<span><span class="co">#&gt; [6,] 0 1</span></span></code></pre>
+<span><span class="co">#&gt; [6,] 0 0</span></span></code></pre>
 <p>Currently, <code><a href="../reference/ssn_simulate.html">ssn_simulate()</a></code> only works on the observed
 network (<code>network = "obs"</code>). However, simulation in
 <code>SSN2</code> will be a focus of future updates, and we plan to add
@@ -1440,7 +1466,8 @@ <h2 id="function-glossary">Function Glossary<a class="anchor" aria-label="anchor
 <code><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv()</a></code>: Perform leave-one-out cross validation and
 compute relevant statistics.</li>
 <li>
-<code><a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix()</a></code>: Return the model matrix (<span class="math inline">\(\mathbf{X}\)</span>).</li>
+<code><a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix()</a></code>: Return the model matrix
+(<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>𝐗</mi><annotation encoding="application/x-tex">\mathbf{X}</annotation></semantics></math>).</li>
 <li>
 <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code>: Create fitted model plots.</li>
 <li>
@@ -1517,8 +1544,8 @@ <h2 id="function-glossary">Function Glossary<a class="anchor" aria-label="anchor
 functions:</p>
 <ul>
 <li>
-<code><a href="../reference/ssn_simulate.html">ssn_simulate()</a></code>: Simulate spatially correlated random
-variables on a stream network.</li>
+<code><a href="../reference/ssn_simulate.html">ssn_simulate()</a></code>: Simulate spatially autocorrelated
+random variables on a stream network.</li>
 </ul>
 <p>For a full list of <code>SSN2</code> functions alongside their
 documentation, see the documentation manual.</p>
@@ -1682,10 +1709,10 @@ <h2 class="unnumbered" id="r-code-appendix">R Code Appendix<a class="anchor" ari
 <span><span class="fu"><a href="https://rdrr.io/r/utils/citation.html" class="external-link">citation</a></span><span class="op">(</span>package <span class="op">=</span> <span class="st">"SSN2"</span><span class="op">)</span></span>
 <span><span class="va">path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"lsndata/MiddleFork04.ssn"</span>, package <span class="op">=</span> <span class="st">"SSN2"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="../reference/copy_lsn_to_temp.html">copy_lsn_to_temp</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="va">path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span></span>
+<span><span class="va">path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html" class="external-link">file.path</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"MiddleFork04.ssn"</span><span class="op">)</span></span>
 <span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/ssn_import.html">ssn_import</a></span><span class="op">(</span></span>
 <span>  path <span class="op">=</span> <span class="va">path</span>,</span>
-<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span>, <span class="st">"Knapp"</span><span class="op">)</span>,</span>
+<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span><span class="op">)</span>,</span>
 <span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
 <span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mf04p</span><span class="op">)</span></span>
@@ -1698,14 +1725,14 @@ <h2 class="unnumbered" id="r-code-appendix">R Code Appendix<a class="anchor" ari
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_bw</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="../reference/ssn_create_distmat.html">ssn_create_distmat</a></span><span class="op">(</span></span>
 <span>  ssn.object <span class="op">=</span> <span class="va">mf04p</span>,</span>
-<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span>, <span class="st">"Knapp"</span><span class="op">)</span>,</span>
+<span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span><span class="op">)</span>,</span>
 <span>  among_predpts <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
 <span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">mf04p</span><span class="op">$</span><span class="va">edges</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggsf.html" class="external-link">geom_sf</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">mf04p</span><span class="op">$</span><span class="va">obs</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>color <span class="op">=</span> <span class="va">Summer_mn</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_color_viridis_c</a></span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.5</span>, <span class="fl">17</span><span class="op">)</span>, option <span class="op">=</span> <span class="st">"H"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_color_viridis_c</a></span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">17</span><span class="op">)</span>, option <span class="op">=</span> <span class="st">"H"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_bw</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">tg</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/Torgegram.html">Torgegram</a></span><span class="op">(</span></span>
 <span>  formula <span class="op">=</span> <span class="va">Summer_mn</span> <span class="op">~</span> <span class="va">ELEV_DEM</span> <span class="op">+</span> <span class="va">AREAWTMAP</span>,</span>
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@@ -1863,7 +1890,7 @@ <h2 class="unnumbered" id="r-code-appendix">R Code Appendix<a class="anchor" ari
 <div class="section level2">
 <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-bates2015lme4" class="csl-entry">
 Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. 2015.
 <span>“Fitting Linear Mixed-Effects Models Using <span class="nocase">lme4</span>.”</span> <em>Journal of Statistical
@@ -1886,6 +1913,11 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 <span>“Spatial Prediction on a River Network.”</span> <em>Journal of
 Agricultural, Biological, and Environmental Statistics</em> 11: 127–50.
 </div>
+<div id="ref-dumelle2023spmodel" class="csl-entry">
+Dumelle, Michael, Matt Higham, and Jay M Ver Hoef. 2023. <span>“Spmodel:
+Spatial Statistical Modeling and Prediction in r.”</span> <em>Plos
+One</em> 18 (3): e0282524.
+</div>
 <div id="ref-faraway2016extending" class="csl-entry">
 Faraway, Julian J. 2016. <em>Extending the Linear Model with r:
 Generalized Linear, Mixed Effects and Nonparametric Regression
@@ -1898,10 +1930,6 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 Data.”</span> <em>Wiley Interdisciplinary Reviews: Water</em> 1 (3):
 277–94.
 </div>
-<div id="ref-kattwinkel2020openSTARS" class="csl-entry">
-Kattwinkel, Mira, and Eduard Szöcs. 2022. <em>openSTARS: An Open Source
-Implementation of the ’ArcGIS’ Toolbox ’STARS’</em>. <a href="https://CRAN.R-project.org/package=openSTARS" class="external-link">https://CRAN.R-project.org/package=openSTARS</a>.
-</div>
 <div id="ref-mccullagh1989generalized" class="csl-entry">
 McCullagh, Peter, and John A Nelder. 1989. <em>Generalized Linear
 Models, 2nd Edition</em>. Chapman &amp; Hall Ltd.
@@ -1920,6 +1948,11 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 Standardized Support for Spatial Vector Data</span>.”</span>
 <em><span>The R Journal</span></em> 10 (1): 439–46. <a href="https://doi.org/10.32614/RJ-2018-009" class="external-link">https://doi.org/10.32614/RJ-2018-009</a>.
 </div>
+<div id="ref-peterson2024SSNbler" class="csl-entry">
+Peterson, Erin E., Michael Dumelle, Alan Pearse, Dan Teleki, and Jay M.
+Ver Hoef. 2024. <em><span>SSNbler</span>: Assemble SSN Objects in
+<span>R</span></em>. <a href="https://github.com/pet221/SSNbler" class="external-link">https://github.com/pet221/SSNbler</a>.
+</div>
 <div id="ref-peterson2013modelling" class="csl-entry">
 Peterson, Erin E., Jay M. Ver Hoef, Dan J. Isaak, Jeffrey A. Falke,
 Marie-Jose Fortin, Chris E. Jordan, Kristina McNyset, et al. 2013.
@@ -1974,28 +2007,26 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 </div>
 </div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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           <p><strong>Michael Dumelle</strong>. Author, maintainer. <a href="https://orcid.org/0000-0002-3393-5529" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
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+          <p><strong>Jay M. Ver Hoef</strong>. Author.
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-          <p><strong>Erin Peterson</strong>. Author. 
+          <p><strong>Erin Peterson</strong>. Author.
           </p>
         </li>
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-          <p><strong>Alan Pearse</strong>. Contributor. 
+          <p><strong>Alan Pearse</strong>. Contributor.
           </p>
         </li>
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-          <p><strong>Dan Isaak</strong>. Contributor. 
+          <p><strong>Dan Isaak</strong>. Contributor.
           </p>
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       <p><small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
 
-      <p>Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.1.0</p>
+      <p>Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.2.0</p>
       <pre>@Manual{,
   title = {{SSN2}: Spatial Modeling on Stream Networks in {R}},
   author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak},
   year = {2023},
-  note = {{R} package version 0.1.0},
+  note = {{R} package version 0.2.0},
 }</pre>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
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new file mode 100644
index 0000000..e8f21f7
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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:pe(t)?Je(t):t.contextElement?Je(t.contextElement):[],popper:Je(e)};var r,c,u=function(t){var e=ui(t);return de.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),"fixed"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px 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t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends 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* --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map()\n\nexport default {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map())\n    }\n\n    const instanceMap = elementMap.get(element)\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n      return\n    }\n\n    instanceMap.set(key, instance)\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null\n    }\n\n    return null\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return\n    }\n\n    const instanceMap = elementMap.get(element)\n\n    instanceMap.delete(key)\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element)\n    }\n  }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1_000_000\nconst MILLISECONDS_MULTIPLIER = 1000\nconst TRANSITION_END = 'transitionend'\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`)\n  }\n\n  return selector\n}\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`\n  }\n\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase()\n}\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID)\n  } while (document.getElementById(prefix))\n\n  return prefix\n}\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0\n  }\n\n  // Get transition-duration of the element\n  let { transitionDuration, transitionDelay } = window.getComputedStyle(element)\n\n  const floatTransitionDuration = Number.parseFloat(transitionDuration)\n  const floatTransitionDelay = Number.parseFloat(transitionDelay)\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0]\n  transitionDelay = transitionDelay.split(',')[0]\n\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER\n}\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END))\n}\n\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false\n  }\n\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0]\n  }\n\n  return typeof object.nodeType !== 'undefined'\n}\n\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object))\n  }\n\n  return null\n}\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])')\n\n  if (!closedDetails) {\n    return elementIsVisible\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary')\n    if (summary && summary.parentNode !== closedDetails) {\n      return false\n    }\n\n    if (summary === null) {\n      return false\n    }\n  }\n\n  return elementIsVisible\n}\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode()\n    return root instanceof ShadowRoot ? root : null\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null\n  }\n\n  return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery\n  }\n\n  return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback()\n        }\n      })\n    }\n\n    DOMContentLoadedCallbacks.push(callback)\n  } else {\n    callback()\n  }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery()\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME\n      const JQUERY_NO_CONFLICT = $.fn[name]\n      $.fn[name] = plugin.jQueryInterface\n      $.fn[name].Constructor = plugin\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT\n        return plugin.jQueryInterface\n      }\n    }\n  })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback)\n    return\n  }\n\n  const durationPadding = 5\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n  let called = false\n\n  const handler = ({ target }) => {\n    if (target !== transitionElement) {\n      return\n    }\n\n    called = true\n    transitionElement.removeEventListener(TRANSITION_END, handler)\n    execute(callback)\n  }\n\n  transitionElement.addEventListener(TRANSITION_END, handler)\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement)\n    }\n  }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length\n  let index = list.indexOf(activeElement)\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n  }\n\n  index += shouldGetNext ? 1 : -1\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n  defineJQueryPlugin,\n  execute,\n  executeAfterTransition,\n  findShadowRoot,\n  getElement,\n  getjQuery,\n  getNextActiveElement,\n  getTransitionDurationFromElement,\n  getUID,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop,\n  onDOMContentLoaded,\n  parseSelector,\n  reflow,\n  triggerTransitionEnd,\n  toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n  'click',\n  'dblclick',\n  'mouseup',\n  'mousedown',\n  'contextmenu',\n  'mousewheel',\n  'DOMMouseScroll',\n  'mouseover',\n  'mouseout',\n  'mousemove',\n  'selectstart',\n  'selectend',\n  'keydown',\n  'keypress',\n  'keyup',\n  'orientationchange',\n  'touchstart',\n  'touchmove',\n  'touchend',\n  'touchcancel',\n  'pointerdown',\n  'pointermove',\n  'pointerup',\n  'pointerleave',\n  'pointercancel',\n  'gesturestart',\n  'gesturechange',\n  'gestureend',\n  'focus',\n  'blur',\n  'change',\n  'reset',\n  'select',\n  'submit',\n  'focusin',\n  'focusout',\n  'load',\n  'unload',\n  'beforeunload',\n  'resize',\n  'move',\n  'DOMContentLoaded',\n  'readystatechange',\n  'error',\n  'abort',\n  'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element)\n\n  element.uidEvent = uid\n  eventRegistry[uid] = eventRegistry[uid] || {}\n\n  return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, { delegateTarget: element })\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn)\n    }\n\n    return fn.apply(element, [event])\n  }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector)\n\n    for (let { target } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue\n        }\n\n        hydrateObj(event, { delegateTarget: target })\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn)\n        }\n\n        return fn.apply(target, [event])\n      }\n    }\n  }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events)\n    .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n  let typeEvent = getTypeEvent(originalTypeEvent)\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent\n  }\n\n  return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n          return fn.call(this, event)\n        }\n      }\n    }\n\n    callable = wrapFunction(callable)\n  }\n\n  const events = getElementEvents(element)\n  const handlers = events[typeEvent] || (events[typeEvent] = {})\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n    return\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n  const fn = isDelegated ?\n    bootstrapDelegationHandler(element, handler, callable) :\n    bootstrapHandler(element, callable)\n\n  fn.delegationSelector = isDelegated ? handler : null\n  fn.callable = callable\n  fn.oneOff = oneOff\n  fn.uidEvent = uid\n  handlers[uid] = fn\n\n  element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n  if (!fn) {\n    return\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n  delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {}\n\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '')\n  return customEvents[event] || event\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false)\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true)\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n    const inNamespace = typeEvent !== originalTypeEvent\n    const events = getElementEvents(element)\n    const storeElementEvent = events[typeEvent] || {}\n    const isNamespace = originalTypeEvent.startsWith('.')\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n      return\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n      }\n    }\n\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null\n    }\n\n    const $ = getjQuery()\n    const typeEvent = getTypeEvent(event)\n    const inNamespace = event !== typeEvent\n\n    let jQueryEvent = null\n    let bubbles = true\n    let nativeDispatch = true\n    let defaultPrevented = false\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args)\n\n      $(element).trigger(jQueryEvent)\n      bubbles = !jQueryEvent.isPropagationStopped()\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n      defaultPrevented = jQueryEvent.isDefaultPrevented()\n    }\n\n    const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n    if (defaultPrevented) {\n      evt.preventDefault()\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt)\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault()\n    }\n\n    return evt\n  }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value\n    } catch {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value\n        }\n      })\n    }\n  }\n\n  return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true\n  }\n\n  if (value === 'false') {\n    return false\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value)\n  }\n\n  if (value === '' || value === 'null') {\n    return null\n  }\n\n  if (typeof value !== 'string') {\n    return value\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value))\n  } catch {\n    return value\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {}\n    }\n\n    const attributes = {}\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '')\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n      attributes[pureKey] = normalizeData(element.dataset[key])\n    }\n\n    return attributes\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n  }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {}\n  }\n\n  static get DefaultType() {\n    return {}\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!')\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    return config\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    }\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property]\n      const valueType = isElement(value) ? 'element' : toType(value)\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(\n          `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n        )\n      }\n    }\n  }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super()\n\n    element = getElement(element)\n    if (!element) {\n      return\n    }\n\n    this._element = element\n    this._config = this._getConfig(config)\n\n    Data.set(this._element, this.constructor.DATA_KEY, this)\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY)\n    EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated)\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY)\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n  }\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`\n  }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target')\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href')\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n      return null\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n  }\n\n  return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector)\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector))\n  },\n\n  parents(element, selector) {\n    const parents = []\n    let ancestor = element.parentNode.closest(selector)\n\n    while (ancestor) {\n      parents.push(ancestor)\n      ancestor = ancestor.parentNode.closest(selector)\n    }\n\n    return parents\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous]\n      }\n\n      previous = previous.previousElementSibling\n    }\n\n    return []\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next]\n      }\n\n      next = next.nextElementSibling\n    }\n\n    return []\n  },\n\n  focusableChildren(element) {\n    const focusables = [\n      'a',\n      'button',\n      'input',\n      'textarea',\n      'select',\n      'details',\n      '[tabindex]',\n      '[contenteditable=\"true\"]'\n    ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n  },\n\n  getSelectorFromElement(element) {\n    const selector = getSelector(element)\n\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null\n    }\n\n    return null\n  },\n\n  getElementFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.findOne(selector) : null\n  },\n\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.find(selector) : []\n  }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`\n  const name = component.NAME\n\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault()\n    }\n\n    if (isDisabled(this)) {\n      return\n    }\n\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n    const instance = component.getOrCreateInstance(target)\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]()\n  })\n}\n\nexport {\n  enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n    if (closeEvent.defaultPrevented) {\n      return\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove()\n    EventHandler.trigger(this._element, EVENT_CLOSED)\n    this.dispose()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this)\n\n      if (config === 'toggle') {\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n  event.preventDefault()\n\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n  const data = Button.getOrCreateInstance(button)\n\n  data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n}\n\nconst DefaultType = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super()\n    this._element = element\n\n    if (!element || !Swipe.isSupported()) {\n      return\n    }\n\n    this._config = this._getConfig(config)\n    this._deltaX = 0\n    this._supportPointerEvents = Boolean(window.PointerEvent)\n    this._initEvents()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY)\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX\n\n      return\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX\n    }\n\n    this._handleSwipe()\n    execute(this._config.endCallback)\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ?\n      0 :\n      event.touches[0].clientX - this._deltaX\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX)\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return\n    }\n\n    const direction = absDeltaX / this._deltaX\n\n    this._deltaX = 0\n\n    if (!direction) {\n      return\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n  }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getNextActiveElement,\n  isRTL,\n  isVisible,\n  reflow,\n  triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n}\n\nconst DefaultType = {\n  interval: '(number|boolean)', // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._interval = null\n    this._activeElement = null\n    this._isSliding = false\n    this.touchTimeout = null\n    this._swipeHelper = null\n\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n    this._addEventListeners()\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT)\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next()\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV)\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element)\n    }\n\n    this._clearInterval()\n  }\n\n  cycle() {\n    this._clearInterval()\n    this._updateInterval()\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n      return\n    }\n\n    this.cycle()\n  }\n\n  to(index) {\n    const items = this._getItems()\n    if (index > items.length - 1 || index < 0) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n      return\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive())\n    if (activeIndex === index) {\n      return\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n    this._slide(order, items[index])\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose()\n    }\n\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval\n    return config\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n      EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners()\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause()\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout)\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n    }\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    }\n\n    this._swipeHelper = new Swipe(this._element, swipeConfig)\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key]\n    if (direction) {\n      event.preventDefault()\n      this._slide(this._directionToOrder(direction))\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element)\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n    activeIndicator.removeAttribute('aria-current')\n\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n      newActiveIndicator.setAttribute('aria-current', 'true')\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive()\n\n    if (!element) {\n      return\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n    this._config.interval = elementInterval || this._config.defaultInterval\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return\n    }\n\n    const activeElement = this._getActive()\n    const isNext = order === ORDER_NEXT\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n    if (nextElement === activeElement) {\n      return\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement)\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      })\n    }\n\n    const slideEvent = triggerEvent(EVENT_SLIDE)\n\n    if (slideEvent.defaultPrevented) {\n      return\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return\n    }\n\n    const isCycling = Boolean(this._interval)\n    this.pause()\n\n    this._isSliding = true\n\n    this._setActiveIndicatorElement(nextElementIndex)\n    this._activeElement = nextElement\n\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n    nextElement.classList.add(orderClassName)\n\n    reflow(nextElement)\n\n    activeElement.classList.add(directionalClassName)\n    nextElement.classList.add(directionalClassName)\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName)\n      nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n      activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n      this._isSliding = false\n\n      triggerEvent(EVENT_SLID)\n    }\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n    if (isCycling) {\n      this.cycle()\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE)\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element)\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval)\n      this._interval = null\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config)\n\n      if (typeof config === 'number') {\n        data.to(config)\n        return\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return\n  }\n\n  event.preventDefault()\n\n  const carousel = Carousel.getOrCreateInstance(target)\n  const slideIndex = this.getAttribute('data-bs-slide-to')\n\n  if (slideIndex) {\n    carousel.to(slideIndex)\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next()\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  carousel.prev()\n  carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getElement,\n  reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n  parent: null,\n  toggle: true\n}\n\nconst DefaultType = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isTransitioning = false\n    this._triggerArray = []\n\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem)\n      const filterElement = SelectorEngine.find(selector)\n        .filter(foundElement => foundElement === this._element)\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem)\n      }\n    }\n\n    this._initializeChildren()\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n    }\n\n    if (this._config.toggle) {\n      this.toggle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide()\n    } else {\n      this.show()\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return\n    }\n\n    let activeChildren = []\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n        .filter(element => element !== this._element)\n        .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide()\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE)\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n    this._element.style[dimension] = 0\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true)\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n      this._element.style[dimension] = ''\n\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n    }\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n    const scrollSize = `scroll${capitalizedDimension}`\n\n    this._queueCallback(complete, this._element, true)\n    this._element.style[dimension] = `${this._element[scrollSize]}px`\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger)\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false)\n      }\n    }\n\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.style[dimension] = ''\n\n    this._queueCallback(complete, this._element, true)\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle) // Coerce string values\n    config.parent = getElement(config.parent)\n    return config\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element)\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected))\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n      element.setAttribute('aria-expanded', isOpen)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {}\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n    event.preventDefault()\n  }\n\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  execute,\n  getElement,\n  getNextActiveElement,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n}\n\nconst DefaultType = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._popper = null\n    this._parent = this._element.parentNode // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n    this._inNavbar = this._detectNavbar()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show()\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._createPopper()\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    this._element.focus()\n    this._element.setAttribute('aria-expanded', true)\n\n    this._menu.classList.add(CLASS_NAME_SHOW)\n    this._element.classList.add(CLASS_NAME_SHOW)\n    EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    this._completeHide(relatedTarget)\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    super.dispose()\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar()\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW)\n    this._element.classList.remove(CLASS_NAME_SHOW)\n    this._element.setAttribute('aria-expanded', 'false')\n    Manipulator.removeDataAttribute(this._menu, 'popper')\n    EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config)\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) &&\n      typeof config.reference.getBoundingClientRect !== 'function'\n    ) {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n    }\n\n    return config\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n    }\n\n    let referenceElement = this._element\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference)\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference\n    }\n\n    const popperConfig = this._getPopperConfig()\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      },\n      {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _selectMenuItem({ key, target }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n    if (!items.length) {\n      return\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n      return\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle)\n      if (!context || context._config.autoClose === false) {\n        continue\n      }\n\n      const composedPath = event.composedPath()\n      const isMenuTarget = composedPath.includes(context._menu)\n      if (\n        composedPath.includes(context._element) ||\n        (context._config.autoClose === 'inside' && !isMenuTarget) ||\n        (context._config.autoClose === 'outside' && isMenuTarget)\n      ) {\n        continue\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue\n      }\n\n      const relatedTarget = { relatedTarget: context._element }\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event\n      }\n\n      context._completeHide(relatedTarget)\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName)\n    const isEscapeEvent = event.key === ESCAPE_KEY\n    const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return\n    }\n\n    event.preventDefault()\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n      this :\n      (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n    const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation()\n      instance.show()\n      instance._selectMenuItem(event)\n      return\n    }\n\n    if (instance._isShown()) { // else is escape and we check if it is shown\n      event.stopPropagation()\n      instance.hide()\n      getToggleButton.focus()\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  event.preventDefault()\n  Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isAppended = false\n    this._element = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._append()\n\n    const element = this._getElement()\n    if (this._config.isAnimated) {\n      reflow(element)\n    }\n\n    element.classList.add(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      execute(callback)\n    })\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      this.dispose()\n      execute(callback)\n    })\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n    this._element.remove()\n    this._isAppended = false\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div')\n      backdrop.className = this._config.className\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE)\n      }\n\n      this._element = backdrop\n    }\n\n    return this._element\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement)\n    return config\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return\n    }\n\n    const element = this._getElement()\n    this._config.rootElement.append(element)\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback)\n    })\n\n    this._isAppended = true\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n  }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isActive = false\n    this._lastTabNavDirection = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus()\n    }\n\n    EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n    this._isActive = true\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return\n    }\n\n    this._isActive = false\n    EventHandler.off(document, EVENT_KEY)\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const { trapElement } = this._config\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement)\n\n    if (elements.length === 0) {\n      trapElement.focus()\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus()\n    } else {\n      elements[0].focus()\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n  }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth\n    return Math.abs(window.innerWidth - documentWidth)\n  }\n\n  hide() {\n    const width = this.getWidth()\n    this._disableOverFlow()\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow')\n    this._resetElementAttributes(this._element, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow')\n    this._element.style.overflow = 'hidden'\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth()\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return\n      }\n\n      this._saveInitialAttribute(element, styleProperty)\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty)\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue)\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty)\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty)\n        return\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty)\n      element.style.setProperty(styleProperty, value)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector)\n      return\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel)\n    }\n  }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._isShown = false\n    this._isTransitioning = false\n    this._scrollBar = new ScrollBarHelper()\n\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n      relatedTarget\n    })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._isTransitioning = true\n\n    this._scrollBar.hide()\n\n    document.body.classList.add(CLASS_NAME_OPEN)\n\n    this._adjustDialog()\n\n    this._backdrop.show(() => this._showElement(relatedTarget))\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = false\n    this._isTransitioning = true\n    this._focustrap.deactivate()\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n  }\n\n  dispose() {\n    EventHandler.off(window, EVENT_KEY)\n    EventHandler.off(this._dialog, EVENT_KEY)\n\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n\n    super.dispose()\n  }\n\n  handleUpdate() {\n    this._adjustDialog()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element)\n    }\n\n    this._element.style.display = 'block'\n    this._element.removeAttribute('aria-hidden')\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.scrollTop = 0\n\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n    if (modalBody) {\n      modalBody.scrollTop = 0\n    }\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_SHOW)\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate()\n      }\n\n      this._isTransitioning = false\n      EventHandler.trigger(this._element, EVENT_SHOWN, {\n        relatedTarget\n      })\n    }\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      this._triggerBackdropTransition()\n    })\n\n    EventHandler.on(window, EVENT_RESIZE, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog()\n      }\n    })\n\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition()\n          return\n        }\n\n        if (this._config.backdrop) {\n          this.hide()\n        }\n      })\n    })\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none'\n    this._element.setAttribute('aria-hidden', true)\n    this._element.removeAttribute('aria-modal')\n    this._element.removeAttribute('role')\n    this._isTransitioning = false\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN)\n      this._resetAdjustments()\n      this._scrollBar.reset()\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    })\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE)\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const initialOverflowY = this._element.style.overflowY\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden'\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC)\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC)\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY\n      }, this._dialog)\n    }, this._dialog)\n\n    this._element.focus()\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const scrollbarWidth = this._scrollBar.getWidth()\n    const isBodyOverflowing = scrollbarWidth > 0\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = ''\n    this._element.style.paddingRight = ''\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](relatedTarget)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  EventHandler.one(target, EVENT_SHOW, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN, () => {\n      if (isVisible(this)) {\n        this.focus()\n      }\n    })\n  })\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Modal.getOrCreateInstance(target)\n\n  data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n  defineJQueryPlugin,\n  isDisabled,\n  isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isShown = false\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._backdrop.show()\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide()\n    }\n\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.classList.add(CLASS_NAME_SHOWING)\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate()\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW)\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n    }\n\n    this._queueCallback(completeCallBack, this._element, true)\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._focustrap.deactivate()\n    this._element.blur()\n    this._isShown = false\n    this._element.classList.add(CLASS_NAME_HIDING)\n    this._backdrop.hide()\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n      this._element.removeAttribute('aria-modal')\n      this._element.removeAttribute('role')\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset()\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._queueCallback(completeCallback, this._element, true)\n  }\n\n  dispose() {\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n    super.dispose()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n        return\n      }\n\n      this.hide()\n    }\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop)\n\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    })\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus()\n    }\n  })\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target)\n  data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show()\n  }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide()\n    }\n  }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n  'background',\n  'cite',\n  'href',\n  'itemtype',\n  'longdesc',\n  'poster',\n  'src',\n  'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase()\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n    }\n\n    return true\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n    .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml)\n  }\n\n  const domParser = new window.DOMParser()\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase()\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove()\n      continue\n    }\n\n    const attributeList = [].concat(...element.attributes)\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName)\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  content: {}, // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n}\n\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content)\n      .map(config => this._resolvePossibleFunction(config))\n      .filter(Boolean)\n  }\n\n  hasContent() {\n    return this.getContent().length > 0\n  }\n\n  changeContent(content) {\n    this._checkContent(content)\n    this._config.content = { ...this._config.content, ...content }\n    return this\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div')\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector)\n    }\n\n    const template = templateWrapper.children[0]\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '))\n    }\n\n    return template\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config)\n    this._checkContent(config.content)\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template)\n\n    if (!templateElement) {\n      return\n    }\n\n    content = this._resolvePossibleFunction(content)\n\n    if (!content) {\n      templateElement.remove()\n      return\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement)\n      return\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content)\n      return\n    }\n\n    templateElement.textContent = content\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this])\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = ''\n      templateElement.append(element)\n      return\n    }\n\n    templateElement.textContent = element.textContent\n  }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' +\n            '<div class=\"tooltip-arrow\"></div>' +\n            '<div class=\"tooltip-inner\"></div>' +\n            '</div>',\n  title: '',\n  trigger: 'hover focus'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n    }\n\n    super(element, config)\n\n    // Private\n    this._isEnabled = true\n    this._timeout = 0\n    this._isHovered = null\n    this._activeTrigger = {}\n    this._popper = null\n    this._templateFactory = null\n    this._newContent = null\n\n    // Protected\n    this.tip = null\n\n    this._setListeners()\n\n    if (!this._config.selector) {\n      this._fixTitle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true\n  }\n\n  disable() {\n    this._isEnabled = false\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click\n    if (this._isShown()) {\n      this._leave()\n      return\n    }\n\n    this._enter()\n  }\n\n  dispose() {\n    clearTimeout(this._timeout)\n\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n    }\n\n    this._disposePopper()\n    super.dispose()\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements')\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n    const shadowRoot = findShadowRoot(this._element)\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper()\n\n    const tip = this._getTipElement()\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n    const { container } = this._config\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip)\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n    }\n\n    this._popper = this._createPopper(tip)\n\n    tip.classList.add(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n      if (this._isHovered === false) {\n        this._leave()\n      }\n\n      this._isHovered = false\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const tip = this._getTipElement()\n    tip.classList.remove(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false\n    this._activeTrigger[TRIGGER_FOCUS] = false\n    this._activeTrigger[TRIGGER_HOVER] = false\n    this._isHovered = null // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper()\n      }\n\n      this._element.removeAttribute('aria-describedby')\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle())\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n    }\n\n    return this.tip\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml()\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n    const tipId = getUID(this.constructor.NAME).toString()\n\n    tip.setAttribute('id', tipId)\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE)\n    }\n\n    return tip\n  }\n\n  setContent(content) {\n    this._newContent = content\n    if (this._isShown()) {\n      this._disposePopper()\n      this.show()\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content)\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      })\n    }\n\n    return this._templateFactory\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    }\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n  }\n\n  _isAnimated() {\n    return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element])\n    const attachment = AttachmentMap[placement.toUpperCase()]\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element])\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [\n        {\n          name: 'flip',\n          options: {\n            fallbackPlacements: this._config.fallbackPlacements\n          }\n        },\n        {\n          name: 'offset',\n          options: {\n            offset: this._getOffset()\n          }\n        },\n        {\n          name: 'preventOverflow',\n          options: {\n            boundary: this._config.boundary\n          }\n        },\n        {\n          name: 'arrow',\n          options: {\n            element: `.${this.constructor.NAME}-arrow`\n          }\n        },\n        {\n          name: 'preSetPlacement',\n          enabled: true,\n          phase: 'beforeMain',\n          fn: data => {\n            // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n            // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n            this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n          }\n        }\n      ]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ')\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context.toggle()\n        })\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSEENTER) :\n          this.constructor.eventName(EVENT_FOCUSIN)\n        const eventOut = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSELEAVE) :\n          this.constructor.eventName(EVENT_FOCUSOUT)\n\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n          context._enter()\n        })\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n            context._element.contains(event.relatedTarget)\n\n          context._leave()\n        })\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide()\n      }\n    }\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title')\n\n    if (!title) {\n      return\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title)\n    }\n\n    this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title')\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true\n      return\n    }\n\n    this._isHovered = true\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show()\n      }\n    }, this._config.delay.show)\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return\n    }\n\n    this._isHovered = false\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide()\n      }\n    }, this._config.delay.hide)\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout)\n    this._timeout = setTimeout(handler, timeout)\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true)\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute]\n      }\n    }\n\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    }\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container)\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      }\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString()\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString()\n    }\n\n    return config\n  }\n\n  _getDelegateConfig() {\n    const config = {}\n\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value\n      }\n    }\n\n    config.selector = false\n    config.trigger = 'manual'\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy()\n      this._popper = null\n    }\n\n    if (this.tip) {\n      this.tip.remove()\n      this.tip = null\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' +\n    '<div class=\"popover-arrow\"></div>' +\n    '<h3 class=\"popover-header\"></h3>' +\n    '<div class=\"popover-body\"></div>' +\n    '</div>',\n  trigger: 'click'\n}\n\nconst DefaultType = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent()\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    }\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content)\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n  offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n  offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n    this._activeTarget = null\n    this._observer = null\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    }\n    this.refresh() // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables()\n    this._maybeEnableSmoothScroll()\n\n    if (this._observer) {\n      this._observer.disconnect()\n    } else {\n      this._observer = this._getNewObserver()\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section)\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect()\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n    }\n\n    return config\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK)\n\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash)\n      if (observableSection) {\n        event.preventDefault()\n        const root = this._rootElement || window\n        const height = observableSection.offsetTop - this._element.offsetTop\n        if (root.scrollTo) {\n          root.scrollTo({ top: height, behavior: 'smooth' })\n          return\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height\n      }\n    })\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    }\n\n    return new IntersectionObserver(entries => this._observerCallback(entries), options)\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n      this._process(targetElement(entry))\n    }\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n    this._previousScrollData.parentScrollTop = parentScrollTop\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null\n        this._clearActiveClass(targetElement(entry))\n\n        continue\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry)\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return\n        }\n\n        continue\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry)\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue\n      }\n\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor)\n        this._observableSections.set(anchor.hash, observableSection)\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return\n    }\n\n    this._clearActiveClass(this._config.target)\n    this._activeTarget = target\n    target.classList.add(CLASS_NAME_ACTIVE)\n    this._activateParents(target)\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n        .classList.add(CLASS_NAME_ACTIVE)\n      return\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE)\n      }\n    }\n  }\n\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE)\n\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE}`, parent)\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy)\n\nexport default ScrollSpy\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getNextActiveElement, isDisabled } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tab'\nconst DATA_KEY = 'bs.tab'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}`\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst HOME_KEY = 'Home'\nconst END_KEY = 'End'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_DROPDOWN = 'dropdown'\n\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu'\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = ':not(.dropdown-toggle)'\n\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]'\nconst SELECTOR_OUTER = '.nav-item, .list-group-item'\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]' // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`\n\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element)\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL)\n\n    if (!this._parent) {\n      return\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren())\n\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() { // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element\n    if (this._elemIsActive(innerElem)) {\n      return\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem()\n\n    const hideEvent = active ?\n      EventHandler.trigger(active, EVENT_HIDE, { relatedTarget: innerElem }) :\n      null\n\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW, { relatedTarget: active })\n\n    if (showEvent.defaultPrevented || (hideEvent && hideEvent.defaultPrevented)) {\n      return\n    }\n\n    this._deactivate(active, innerElem)\n    this._activate(innerElem, active)\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE)\n\n    this._activate(SelectorEngine.getElementFromSelector(element)) // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.removeAttribute('tabindex')\n      element.setAttribute('aria-selected', true)\n      this._toggleDropDown(element, true)\n      EventHandler.trigger(element, EVENT_SHOWN, {\n        relatedTarget: relatedElem\n      })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.remove(CLASS_NAME_ACTIVE)\n    element.blur()\n\n    this._deactivate(SelectorEngine.getElementFromSelector(element)) // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.setAttribute('aria-selected', false)\n      element.setAttribute('tabindex', '-1')\n      this._toggleDropDown(element, false)\n      EventHandler.trigger(element, EVENT_HIDDEN, { relatedTarget: relatedElem })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _keydown(event) {\n    if (!([ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key))) {\n      return\n    }\n\n    event.stopPropagation()// stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault()\n\n    const children = this._getChildren().filter(element => !isDisabled(element))\n    let nextActiveElement\n\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1]\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key)\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true)\n    }\n\n    if (nextActiveElement) {\n      nextActiveElement.focus({ preventScroll: true })\n      Tab.getOrCreateInstance(nextActiveElement).show()\n    }\n  }\n\n  _getChildren() { // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent)\n  }\n\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null\n  }\n\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist')\n\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child)\n    }\n  }\n\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child)\n    const isActive = this._elemIsActive(child)\n    const outerElem = this._getOuterElement(child)\n    child.setAttribute('aria-selected', isActive)\n\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation')\n    }\n\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1')\n    }\n\n    this._setAttributeIfNotExists(child, 'role', 'tab')\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child)\n  }\n\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child)\n\n    if (!target) {\n      return\n    }\n\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel')\n\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`)\n    }\n  }\n\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element)\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return\n    }\n\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem)\n      if (element) {\n        element.classList.toggle(className, open)\n      }\n    }\n\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE)\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW)\n    outerElem.setAttribute('aria-expanded', open)\n  }\n\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value)\n    }\n  }\n\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE)\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem)\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  Tab.getOrCreateInstance(this).show()\n})\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element)\n  }\n})\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab)\n\nexport default Tab\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin, reflow } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'toast'\nconst DATA_KEY = 'bs.toast'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_HIDE = 'hide' // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\n\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n}\n\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n}\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._timeout = null\n    this._hasMouseInteraction = false\n    this._hasKeyboardInteraction = false\n    this._setListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._clearTimeout()\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE)\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n\n      this._maybeScheduleHide()\n    }\n\n    this._element.classList.remove(CLASS_NAME_HIDE) // @deprecated\n    reflow(this._element)\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING)\n\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  hide() {\n    if (!this.isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE) // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.classList.add(CLASS_NAME_SHOWING)\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  dispose() {\n    this._clearTimeout()\n\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW)\n    }\n\n    super.dispose()\n  }\n\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide()\n    }, this._config.delay)\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout': {\n        this._hasMouseInteraction = isInteracting\n        break\n      }\n\n      case 'focusin':\n      case 'focusout': {\n        this._hasKeyboardInteraction = isInteracting\n        break\n      }\n\n      default: {\n        break\n      }\n    }\n\n    if (isInteracting) {\n      this._clearTimeout()\n      return\n    }\n\n    const nextElement = event.relatedTarget\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return\n    }\n\n    this._maybeScheduleHide()\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false))\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false))\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout)\n    this._timeout = null\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config](this)\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast)\n\nexport default Toast\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap index.umd.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Alert from './src/alert.js'\nimport Button from './src/button.js'\nimport Carousel from './src/carousel.js'\nimport Collapse from './src/collapse.js'\nimport Dropdown from './src/dropdown.js'\nimport Modal from './src/modal.js'\nimport Offcanvas from './src/offcanvas.js'\nimport Popover from './src/popover.js'\nimport ScrollSpy from './src/scrollspy.js'\nimport Tab from './src/tab.js'\nimport Toast from './src/toast.js'\nimport Tooltip from 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\ No newline at end of file
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.min.css b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
new file mode 100644
index 0000000..5d96aa0
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -0,0 +1,5 @@
+:root{--bslib-bootstrap-version: 5;--bslib-preset-name: ;--bslib-preset-type: }/*!
+   * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+   * Copyright 2011-2023 The Bootstrap Authors
+   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+   */:root,[data-bs-theme="light"]{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #dee2e6;--bs-primary: #0d6efd;--bs-secondary: #6c757d;--bs-success: #198754;--bs-info: #0dcaf0;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #f8f9fa;--bs-dark: #212529;--bs-default-rgb: 222,226,230;--bs-primary-rgb: 13,110,253;--bs-secondary-rgb: 108,117,125;--bs-success-rgb: 25,135,84;--bs-info-rgb: 13,202,240;--bs-warning-rgb: 255,193,7;--bs-danger-rgb: 220,53,69;--bs-light-rgb: 248,249,250;--bs-dark-rgb: 33,37,41;--bs-primary-text-emphasis: 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#198754;--bs-btn-border-color: #198754;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #198754;--bs-btn-hover-border-color: #198754;--bs-btn-focus-shadow-rgb: 25,135,84;--bs-btn-active-color: #fff;--bs-btn-active-bg: #198754;--bs-btn-active-border-color: #198754;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #198754;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #198754;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #0dcaf0;--bs-btn-border-color: #0dcaf0;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #0dcaf0;--bs-btn-hover-border-color: #0dcaf0;--bs-btn-focus-shadow-rgb: 13,202,240;--bs-btn-active-color: #000;--bs-btn-active-bg: #0dcaf0;--bs-btn-active-border-color: #0dcaf0;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #0dcaf0;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #0dcaf0;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-warning{--bs-btn-color: #ffc107;--bs-btn-border-color: #ffc107;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #ffc107;--bs-btn-hover-border-color: #ffc107;--bs-btn-focus-shadow-rgb: 255,193,7;--bs-btn-active-color: #000;--bs-btn-active-bg: #ffc107;--bs-btn-active-border-color: #ffc107;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #ffc107;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ffc107;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-danger{--bs-btn-color: #dc3545;--bs-btn-border-color: #dc3545;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #dc3545;--bs-btn-hover-border-color: #dc3545;--bs-btn-focus-shadow-rgb: 220,53,69;--bs-btn-active-color: #fff;--bs-btn-active-bg: #dc3545;--bs-btn-active-border-color: #dc3545;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #dc3545;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #dc3545;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248,249,250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #212529;--bs-btn-border-color: #212529;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #212529;--bs-btn-hover-border-color: #212529;--bs-btn-focus-shadow-rgb: 33,37,41;--bs-btn-active-color: #fff;--bs-btn-active-bg: #212529;--bs-btn-active-border-color: #212529;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #212529;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #212529;--bs-btn-bg: transparent;--bs-gradient: none}.btn-link{--bs-btn-font-weight: 400;--bs-btn-color: var(--bs-link-color);--bs-btn-bg: transparent;--bs-btn-border-color: transparent;--bs-btn-hover-color: var(--bs-link-hover-color);--bs-btn-hover-border-color: transparent;--bs-btn-active-color: var(--bs-link-hover-color);--bs-btn-active-border-color: transparent;--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-border-color: transparent;--bs-btn-box-shadow: 0 0 0 #000;--bs-btn-focus-shadow-rgb: 49,132,253;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: .5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: var(--bs-border-radius-lg)}.btn-sm,.btn-group-sm>.btn{--bs-btn-padding-y: .25rem;--bs-btn-padding-x: .5rem;--bs-btn-font-size:.875rem;--bs-btn-border-radius: var(--bs-border-radius-sm)}.fade{transition:opacity 0.15s linear}@media (prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing.collapse-horizontal{transition:none}}.dropup,.dropend,.dropdown,.dropstart,.dropup-center,.dropdown-center{position:relative}.dropdown-toggle{white-space:nowrap}.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid transparent;border-bottom:0;border-left:.3em solid transparent}.dropdown-toggle:empty::after{margin-left:0}.dropdown-menu{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: .5rem;--bs-dropdown-spacer: .125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: var(--bs-body-color);--bs-dropdown-bg: var(--bs-body-bg);--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-border-radius: var(--bs-border-radius);--bs-dropdown-border-width: var(--bs-border-width);--bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-divider-margin-y: .5rem;--bs-dropdown-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-dropdown-link-color: var(--bs-body-color);--bs-dropdown-link-hover-color: var(--bs-body-color);--bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #0d6efd;--bs-dropdown-link-disabled-color: var(--bs-tertiary-color);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: .25rem;--bs-dropdown-header-color: #6c757d;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: .5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);border-radius:var(--bs-dropdown-border-radius)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media (min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: 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.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:""}.dropstart .dropdown-toggle::after{display:none}.dropstart .dropdown-toggle::before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:.3em solid;border-bottom:.3em solid transparent}.dropstart .dropdown-toggle:empty::after{margin-left:0}.dropstart .dropdown-toggle::before{vertical-align:0}.dropdown-divider{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #0d6efd;--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(13,110,253,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius: var(--bs-border-radius);--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #0d6efd}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: .125rem;--bs-nav-underline-link-active-color: var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833,37,41,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23052c65'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y) * .5) calc(var(--bs-modal-header-padding-x) * .5);margin:calc(-.5 * var(--bs-modal-header-padding-y)) calc(-.5 * var(--bs-modal-header-padding-x)) calc(-.5 * var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap) * .5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap) * .5)}@media (min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media (min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media (min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: .5rem;--bs-tooltip-padding-y: .25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:.875rem;--bs-tooltip-color: var(--bs-body-bg);--bs-tooltip-bg: var(--bs-emphasis-color);--bs-tooltip-border-radius: var(--bs-border-radius);--bs-tooltip-opacity: .9;--bs-tooltip-arrow-width: .8rem;--bs-tooltip-arrow-height: .4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow{bottom:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow{left:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow{top:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow{right:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) 0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:.875rem;--bs-popover-bg: var(--bs-body-bg);--bs-popover-border-width: var(--bs-border-width);--bs-popover-border-color: var(--bs-border-color-translucent);--bs-popover-border-radius: var(--bs-border-radius-lg);--bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: .5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: var(--bs-secondary-bg);--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: var(--bs-body-color);--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: .5rem;--bs-popover-arrow-border: 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diff --git a/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
new file mode 100644
index 0000000..c628326
--- /dev/null
+++ b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap Table of Contents v1.0.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+!function(a){"use strict";window.Toc={helpers:{findOrFilter:function(e,t){var n=e.find(t);return e.filter(t).add(n).filter(":not([data-toc-skip])")},generateUniqueIdBase:function(e){return a(e).text().trim().replace(/\'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,64).replace(/^-+|-+$/gm,"").toLowerCase()||e.tagName.toLowerCase()},generateUniqueId:function(e){for(var t=this.generateUniqueIdBase(e),n=0;;n++){var r=t;if(0<n&&(r+="-"+n),!document.getElementById(r))return r}},generateAnchor:function(e){if(e.id)return e.id;var t=this.generateUniqueId(e);return e.id=t},createNavList:function(){return a('<ul class="nav navbar-nav"></ul>')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('<a class="nav-link"></a>');n.attr("href","#"+e),n.text(t);var r=a("<li></li>");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1<this.findOrFilter(e,"h"+t).length)return t}return 1},getHeadings:function(e,t){var n="h"+t,r="h"+(t+1);return this.findOrFilter(e,n+","+r)},getNavLevel:function(e){return parseInt(e.tagName.charAt(1),10)},populateNav:function(r,a,e){var i,s=r,c=this;e.each(function(e,t){var n=c.generateNavItem(t);c.getNavLevel(t)===a?s=r:i&&s===r&&(s=c.createChildNavList(i)),s.append(n),i=n})},parseOps:function(e){var t;return(t=e.jquery?{$nav:e}:e).$scope=t.$scope||a(document.body),t}},init:function(e){(e=this.helpers.parseOps(e)).$nav.attr("data-toggle","toc");var t=this.helpers.createChildNavList(e.$nav),n=this.helpers.getTopLevel(e.$scope),r=this.helpers.getHeadings(e.$scope,n);this.helpers.populateNav(t,n,r)}},a(function(){a('nav[data-toggle="toc"]').each(function(e,t){var n=a(t);Toc.init(n)})})}(jQuery);
\ No newline at end of file
diff --git a/docs/deps/clipboard.js-2.0.11/clipboard.min.js b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
new file mode 100644
index 0000000..1103f81
--- /dev/null
+++ b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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\ No newline at end of file
diff --git a/docs/deps/data-deps.txt b/docs/deps/data-deps.txt
index 21592b5..8aa49e2 100644
--- a/docs/deps/data-deps.txt
+++ b/docs/deps/data-deps.txt
@@ -1,4 +1,13 @@
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script>
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
-<link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet" />
-<script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script>
+<link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
+<link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet" />
+<script src="deps/headroom-0.11.0/headroom.min.js"></script>
+<script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
+<script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
+<script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script>
+<script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script>
+<script src="deps/search-1.0.0/fuse.min.js"></script>
+<script src="deps/search-1.0.0/mark.min.js"></script>
diff --git a/docs/deps/font-awesome-6.4.2/css/all.css b/docs/deps/font-awesome-6.4.2/css/all.css
new file mode 100644
index 0000000..bdb6e3a
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/all.css
@@ -0,0 +1,7968 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, none));
+          transform: rotate(var(--fa-rotate-angle, none)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
+.fa-paste::before {
+  content: "\f0ea"; }
+
+.fa-file-clipboard::before {
+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
+
+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
+.fa-blog::before {
+  content: "\f781"; }
+
+.fa-user-ninja::before {
+  content: "\f504"; }
+
+.fa-person-arrow-up-from-line::before {
+  content: "\e539"; }
+
+.fa-scroll-torah::before {
+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
+
+.fa-broom-ball::before {
+  content: "\f458"; }
+
+.fa-quidditch::before {
+  content: "\f458"; }
+
+.fa-quidditch-broom-ball::before {
+  content: "\f458"; }
+
+.fa-toggle-off::before {
+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
+.fa-arrow-down-9-1::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-desc::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-down-alt::before {
+  content: "\f886"; }
+
+.fa-face-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
+
+.fa-trowel-bricks::before {
+  content: "\e58a"; }
+
+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
+
+.fa-hospital-user::before {
+  content: "\f80d"; }
+
+.fa-tent-arrow-left-right::before {
+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
+.fa-square-caret-down::before {
+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
+
+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
+.fa-chevron-circle-down::before {
+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
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+  content: "\f641"; }
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+  content: "\f0a9"; }
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+  content: "\f0a9"; }
+
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+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
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+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
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+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
+.fa-tram::before {
+  content: "\f7da"; }
+
+.fa-cloud-rain::before {
+  content: "\f73d"; }
+
+.fa-building-circle-xmark::before {
+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
+
+.fa-arrows-down-to-line::before {
+  content: "\e4b8"; }
+
+.fa-download::before {
+  content: "\f019"; }
+
+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
+
+.fa-delete-left::before {
+  content: "\f55a"; }
+
+.fa-backspace::before {
+  content: "\f55a"; }
+
+.fa-eye-dropper::before {
+  content: "\f1fb"; }
+
+.fa-eye-dropper-empty::before {
+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
+
+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
+.fa-forward::before {
+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
+.fa-align-center::before {
+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
+
+.fa-file-medical::before {
+  content: "\f477"; }
+
+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
+
+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
+.fa-arrow-up-wide-short::before {
+  content: "\f161"; }
+
+.fa-sort-amount-up::before {
+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
+.fa-long-arrow-alt-up::before {
+  content: "\f30c"; }
+
+.fa-stop::before {
+  content: "\f04d"; }
+
+.fa-code-merge::before {
+  content: "\f387"; }
+
+.fa-upload::before {
+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
+.fa-file-arrow-down::before {
+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
+
+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
+.fa-anchor-circle-xmark::before {
+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
+.fa-ellipsis-h::before {
+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
+
+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-arrow-turn-right::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
+  content: "\f1e9"; }
+
+.fa-cc-visa:before {
+  content: "\f1f0"; }
+
+.fa-lastfm:before {
+  content: "\f202"; }
+
+.fa-shopware:before {
+  content: "\f5b5"; }
+
+.fa-creative-commons-nc:before {
+  content: "\f4e8"; }
+
+.fa-aws:before {
+  content: "\f375"; }
+
+.fa-redhat:before {
+  content: "\f7bc"; }
+
+.fa-yoast:before {
+  content: "\f2b1"; }
+
+.fa-cloudflare:before {
+  content: "\e07d"; }
+
+.fa-ups:before {
+  content: "\f7e0"; }
+
+.fa-wpexplorer:before {
+  content: "\f2de"; }
+
+.fa-dyalog:before {
+  content: "\f399"; }
+
+.fa-bity:before {
+  content: "\f37a"; }
+
+.fa-stackpath:before {
+  content: "\f842"; }
+
+.fa-buysellads:before {
+  content: "\f20d"; }
+
+.fa-first-order:before {
+  content: "\f2b0"; }
+
+.fa-modx:before {
+  content: "\f285"; }
+
+.fa-guilded:before {
+  content: "\e07e"; }
+
+.fa-vnv:before {
+  content: "\f40b"; }
+
+.fa-square-js:before {
+  content: "\f3b9"; }
+
+.fa-js-square:before {
+  content: "\f3b9"; }
+
+.fa-microsoft:before {
+  content: "\f3ca"; }
+
+.fa-qq:before {
+  content: "\f1d6"; }
+
+.fa-orcid:before {
+  content: "\f8d2"; }
+
+.fa-java:before {
+  content: "\f4e4"; }
+
+.fa-invision:before {
+  content: "\f7b0"; }
+
+.fa-creative-commons-pd-alt:before {
+  content: "\f4ed"; }
+
+.fa-centercode:before {
+  content: "\f380"; }
+
+.fa-glide-g:before {
+  content: "\f2a6"; }
+
+.fa-drupal:before {
+  content: "\f1a9"; }
+
+.fa-hire-a-helper:before {
+  content: "\f3b0"; }
+
+.fa-creative-commons-by:before {
+  content: "\f4e7"; }
+
+.fa-unity:before {
+  content: "\e049"; }
+
+.fa-whmcs:before {
+  content: "\f40d"; }
+
+.fa-rocketchat:before {
+  content: "\f3e8"; }
+
+.fa-vk:before {
+  content: "\f189"; }
+
+.fa-untappd:before {
+  content: "\f405"; }
+
+.fa-mailchimp:before {
+  content: "\f59e"; }
+
+.fa-css3-alt:before {
+  content: "\f38b"; }
+
+.fa-square-reddit:before {
+  content: "\f1a2"; }
+
+.fa-reddit-square:before {
+  content: "\f1a2"; }
+
+.fa-vimeo-v:before {
+  content: "\f27d"; }
+
+.fa-contao:before {
+  content: "\f26d"; }
+
+.fa-square-font-awesome:before {
+  content: "\e5ad"; }
+
+.fa-deskpro:before {
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+  content: "\f3c0"; }
+
+.fa-google-drive:before {
+  content: "\f3aa"; }
+
+.fa-servicestack:before {
+  content: "\f3ec"; }
+
+.fa-simplybuilt:before {
+  content: "\f215"; }
+
+.fa-bitbucket:before {
+  content: "\f171"; }
+
+.fa-imdb:before {
+  content: "\f2d8"; }
+
+.fa-deezer:before {
+  content: "\e077"; }
+
+.fa-raspberry-pi:before {
+  content: "\f7bb"; }
+
+.fa-jira:before {
+  content: "\f7b1"; }
+
+.fa-docker:before {
+  content: "\f395"; }
+
+.fa-screenpal:before {
+  content: "\e570"; }
+
+.fa-bluetooth:before {
+  content: "\f293"; }
+
+.fa-gitter:before {
+  content: "\f426"; }
+
+.fa-d-and-d:before {
+  content: "\f38d"; }
+
+.fa-microblog:before {
+  content: "\e01a"; }
+
+.fa-cc-diners-club:before {
+  content: "\f24c"; }
+
+.fa-gg-circle:before {
+  content: "\f261"; }
+
+.fa-pied-piper-hat:before {
+  content: "\f4e5"; }
+
+.fa-kickstarter-k:before {
+  content: "\f3bc"; }
+
+.fa-yandex:before {
+  content: "\f413"; }
+
+.fa-readme:before {
+  content: "\f4d5"; }
+
+.fa-html5:before {
+  content: "\f13b"; }
+
+.fa-sellsy:before {
+  content: "\f213"; }
+
+.fa-sass:before {
+  content: "\f41e"; }
+
+.fa-wirsindhandwerk:before {
+  content: "\e2d0"; }
+
+.fa-wsh:before {
+  content: "\e2d0"; }
+
+.fa-buromobelexperte:before {
+  content: "\f37f"; }
+
+.fa-salesforce:before {
+  content: "\f83b"; }
+
+.fa-octopus-deploy:before {
+  content: "\e082"; }
+
+.fa-medapps:before {
+  content: "\f3c6"; }
+
+.fa-ns8:before {
+  content: "\f3d5"; }
+
+.fa-pinterest-p:before {
+  content: "\f231"; }
+
+.fa-apper:before {
+  content: "\f371"; }
+
+.fa-fort-awesome:before {
+  content: "\f286"; }
+
+.fa-waze:before {
+  content: "\f83f"; }
+
+.fa-cc-jcb:before {
+  content: "\f24b"; }
+
+.fa-snapchat:before {
+  content: "\f2ab"; }
+
+.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa-fantasy-flight-games:before {
+  content: "\f6dc"; }
+
+.fa-rust:before {
+  content: "\e07a"; }
+
+.fa-wix:before {
+  content: "\f5cf"; }
+
+.fa-square-behance:before {
+  content: "\f1b5"; }
+
+.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa-supple:before {
+  content: "\f3f9"; }
+
+.fa-rebel:before {
+  content: "\f1d0"; }
+
+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/docs/deps/font-awesome-6.4.2/css/all.min.css b/docs/deps/font-awesome-6.4.2/css/all.min.css
new file mode 100644
index 0000000..6604a06
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-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diff --git a/docs/deps/font-awesome-6.4.2/css/v4-shims.css b/docs/deps/font-awesome-6.4.2/css/v4-shims.css
new file mode 100644
index 0000000..a85953d
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
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+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f15b"; }
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+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
+
+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
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+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
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+.fa.fa-video-camera:before {
+  content: "\f03d"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f03e"; }
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+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f03e"; }
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+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-image:before {
+  content: "\f03e"; }
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+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f044"; }
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+.fa.fa-edit {
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+  font-weight: 400; }
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+.fa.fa-edit:before {
+  content: "\f044"; }
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+.fa.fa-share-square-o:before {
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
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+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
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+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
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+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
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+.fa.fa-compress:before {
+  content: "\f422"; }
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+.fa.fa-eye {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-eye-slash {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-warning:before {
+  content: "\f071"; }
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+.fa.fa-calendar:before {
+  content: "\f073"; }
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+  content: "\f338"; }
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+  content: "\f337"; }
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+.fa.fa-bar-chart:before {
+  content: "\e0e3"; }
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+.fa.fa-bar-chart-o:before {
+  content: "\e0e3"; }
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+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
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+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/docs/deps/font-awesome-6.4.2/css/v4-shims.min.css b/docs/deps/font-awesome-6.4.2/css/v4-shims.min.css
new file mode 100644
index 0000000..64e4e8d
--- /dev/null
+++ b/docs/deps/font-awesome-6.4.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 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+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/docs/deps/headroom-0.11.0/jQuery.headroom.min.js b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 0000000..17f70c9
--- /dev/null
+++ b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/autocomplete.jquery.min.js b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 0000000..fb63a6a
--- /dev/null
+++ b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 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d.Dropdown({appendTo:a.appendTo,wrapper:this.$node,menu:b,datasets:a.datasets,templates:a.templates,cssClasses:a.cssClasses,minLength:this.minLength}).onSync("suggestionClicked",this._onSuggestionClicked,this).onSync("cursorMoved",this._onCursorMoved,this).onSync("cursorRemoved",this._onCursorRemoved,this).onSync("opened",this._onOpened,this).onSync("closed",this._onClosed,this).onSync("shown",this._onShown,this).onSync("empty",this._onEmpty,this).onSync("redrawn",this._onRedrawn,this).onAsync("datasetRendered",this._onDatasetRendered,this),this.input=new 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required").prop("readonly",!0).attr({"aria-hidden":"true",autocomplete:"off",spellcheck:"false",tabindex:-1}),e.removeData&&e.removeData(),b.data(h,{"aria-autocomplete":b.attr("aria-autocomplete"),"aria-expanded":b.attr("aria-expanded"),"aria-owns":b.attr("aria-owns"),autocomplete:b.attr("autocomplete"),dir:b.attr("dir"),role:b.attr("role"),spellcheck:b.attr("spellcheck"),style:b.attr("style"),type:b.attr("type")}),b.addClass(i.className(a.cssClasses.prefix,a.cssClasses.input,!0)).attr({autocomplete:"off",spellcheck:!1,role:"combobox","aria-autocomplete":a.datasets&&a.datasets[0]&&a.datasets[0].displayKey?"both":"list","aria-expanded":"false","aria-label":a.ariaLabel,"aria-owns":a.listboxId}).css(a.hint?a.css.input:a.css.inputWithNoHint);try{b.attr("dir")||b.attr("dir","auto")}catch(a){}return c=a.appendTo?c.appendTo(j.element(a.appendTo).eq(0)).eq(0):b.wrap(c).parent(),c.prepend(a.hint?e:null).append(d),{wrapper:c,input:b,hint:e,menu:d}}function f(a){return{backgroundAttachment:a.css("background-attachment"),backgroundClip:a.css("background-clip"),backgroundColor:a.css("background-color"),backgroundImage:a.css("background-image"),backgroundOrigin:a.css("background-origin"),backgroundPosition:a.css("background-position"),backgroundRepeat:a.css("background-repeat"),backgroundSize:a.css("background-size")}}function g(a,b){var c=a.find(i.className(b.prefix,b.input));i.each(c.data(h),function(a,b){void 0===a?c.removeAttr(b):c.attr(b,a)}),c.detach().removeClass(i.className(b.prefix,b.input,!0)).insertAfter(a),c.removeData&&c.removeData(h),a.remove()}var h="aaAttrs",i=c(4),j=c(2),k=c(6),l=c(7),m=c(16),n=c(18),o=c(19);i.mixin(d.prototype,{_bindKeyboardShortcuts:function(a){if(a.keyboardShortcuts){var b=this.$input,c=[];i.each(a.keyboardShortcuts,function(a){"string"==typeof a&&(a=a.toUpperCase().charCodeAt(0)),c.push(a)}),j.element(document).keydown(function(a){var 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a=this.input.getInputValue();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty()}this.dropdown.open()},close:function(){this.dropdown.close()},setVal:function(a){a=i.toStr(a),this.isActivated?this.input.setInputValue(a):(this.input.setQuery(a),this.input.setInputValue(a,!0)),this._setLanguageDirection()},getVal:function(){return this.input.getQuery()},destroy:function(){this.input.destroy(),this.dropdown.destroy(),g(this.$node,this.cssClasses),this.$node=null},getWrapper:function(){return this.dropdown.$container[0]}}),d.Dropdown=m,d.Input=l,d.sources=c(20),a.exports=d},function(a,b,c){"use strict";function d(a){a&&a.el||e.error("EventBus initialized without el"),this.$el=f.element(a.el)}var e=c(4),f=c(2);e.mixin(d.prototype,{trigger:function(a,b,c,d){var f=e.Event("autocomplete:"+a);return this.$el.trigger(f,[b,c,d]),f}}),a.exports=d},function(a,b,c){"use strict";function d(a){var b,c,d,f,g=this;a=a||{},a.input||i.error("input is 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+var c=[].slice.call(arguments,1);this.cacheSuggestions(a,b,c),this._render.apply(this,[a,b].concat(c))}}if(this.query=a,this.canceled=!1,this.shouldFetchFromCache(a))b.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var c=this,d=function(){c.canceled||c.source(a,b.bind(c))};if(this.debounce){var e=function(){c.debounceTimeout=null,d()};clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout(e,this.debounce)}else d()}},cacheSuggestions:function(a,b,c){this.cachedQuery=a,this.cachedSuggestions=b,this.cachedRenderExtraArgs=c},shouldFetchFromCache:function(a){return this.cache&&this.cachedQuery===a&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),a.exports=d},function(a,b){"use strict";a.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},function(a,b,c){"use strict";var d=c(4),e={wrapper:{position:"relative",display:"inline-block"},hint:{position:"absolute",top:"0",left:"0",borderColor:"transparent",boxShadow:"none",opacity:"1"},input:{position:"relative",verticalAlign:"top",backgroundColor:"transparent"},inputWithNoHint:{position:"relative",verticalAlign:"top"},dropdown:{position:"absolute",top:"100%",left:"0",zIndex:"100",display:"none"},suggestions:{display:"block"},suggestion:{whiteSpace:"nowrap",cursor:"pointer"},suggestionChild:{whiteSpace:"normal"},ltr:{left:"0",right:"auto"},rtl:{left:"auto",right:"0"},defaultClasses:{root:"algolia-autocomplete",prefix:"aa",noPrefix:!1,dropdownMenu:"dropdown-menu",input:"input",hint:"hint",suggestions:"suggestions",suggestion:"suggestion",cursor:"cursor",dataset:"dataset",empty:"empty"},appendTo:{wrapper:{position:"absolute",zIndex:"100",display:"none"},input:{},inputWithNoHint:{},dropdown:{display:"block"}}};d.isMsie()&&d.mixin(e.input,{backgroundImage:"url(data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)"}),d.isMsie()&&d.isMsie()<=7&&d.mixin(e.input,{marginTop:"-1px"}),a.exports=e},function(a,b,c){"use strict";a.exports={hits:c(21),popularIn:c(24)}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23),g=function(){function a(a,d){return window.Promise.resolve().then(function(){return b.length&&(e=a.search(b),b=[]),e}).then(function(a){if(a)return c=a.results,c[d]})}var b=[],c=[],e=window.Promise.resolve();return function(c,e){return function(f,g){a(c.as,b.push({indexName:c.indexName,query:f,params:e})-1).then(function(a){a&&g(a.hits,a)}).catch(function(a){d.error(a.message)})}}}();a.exports=function(a,b){var c=f(a.as._ua);if(c&&c[0]>=3&&c[1]>20){var d="autocomplete.js "+e;a.as._ua.indexOf(d)===-1&&(a.as._ua+="; "+d)}return g(a,b)}},function(a,b){a.exports="0.38.0"},function(a,b){"use strict";a.exports=function(a){var b=a.match(/Algolia for JavaScript \((\d+\.)(\d+\.)(\d+)\)/)||a.match(/Algolia for vanilla JavaScript (\d+\.)(\d+\.)(\d+)/);if(b)return[b[1],b[2],b[3]]}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23);a.exports=function(a,b,c,g){function h(h,i){a.search(h,b,function(a,h){if(a)return void d.error(a.message);if(h.hits.length>0){var l=h.hits[0],m=d.mixin({hitsPerPage:0},c);delete m.source,delete m.index;var n=f(k.as._ua);return n&&n[0]>=3&&n[1]>20&&(b.additionalUA="autocomplete.js "+e),void k.search(j(l),m,function(a,b){if(a)return void d.error(a.message);var c=[];if(g.includeAll){var e=g.allTitle||"All departments";c.push(d.mixin({facet:{value:e,count:b.nbHits}},d.cloneDeep(l)))}d.each(b.facets,function(a,b){d.each(a,function(a,e){c.push(d.mixin({facet:{facet:b,value:e,count:a}},d.cloneDeep(l)))})});for(var f=1;f<h.hits.length;++f)c.push(h.hits[f]);i(c,h)})}i([])})}var i=f(a.as._ua);if(i&&i[0]>=3&&i[1]>20&&(b=b||{},b.additionalUA="autocomplete.js "+e),!c.source)return d.error("Missing 'source' key");var j=d.isFunction(c.source)?c.source:function(a){return a[c.source]};if(!c.index)return d.error("Missing 'index' key");var k=c.index;return g=g||{},h}}]);
\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/fuse.min.js b/docs/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 0000000..7def598
--- /dev/null
+++ b/docs/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?o(Object(n),!0).forEach((function(t){i(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):o(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function a(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),t&&u(e,t)}function s(e){return(s=Object.setPrototypeOf?Object.getPrototypeOf:function(e){return e.__proto__||Object.getPrototypeOf(e)})(e)}function u(e,t){return(u=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e})(e,t)}function h(e,t){return!t||"object"!=typeof t&&"function"!=typeof t?function(e){if(void 0===e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}(e):t}function f(e){var t=function(){if("undefined"==typeof 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diff --git a/docs/deps/search-1.0.0/mark.min.js b/docs/deps/search-1.0.0/mark.min.js
new file mode 100644
index 0000000..1eea053
--- /dev/null
+++ b/docs/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):e.Mark=t()}(this,function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),r=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i=function(){function e(n){var r=!(arguments.length>1&&void 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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-home">
 <div class="row">
@@ -77,24 +63,24 @@ <h2 id="citation">Citation<a class="anchor" aria-label="anchor" href="#citation"
 <p>If you use <code>SSN2</code> in a formal publication or report, please cite it. Citing <code>SSN2</code> lets us devote more resources to it in the future. View the <code>SSN2</code> citation by running</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/citation.html" class="external-link">citation</a></span><span class="op">(</span>package <span class="op">=</span> <span class="st">"SSN2"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; </span></span>
+<pre><code><span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt; To cite SSN2 in publications use:</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.1.0</span></span>
-<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt;</span></span>
+<span><span class="co">#&gt;   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.2.0</span></span>
+<span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt; A BibTeX entry for LaTeX users is</span></span>
-<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt;   @Manual{,</span></span>
 <span><span class="co">#&gt;     title = {{SSN2}: Spatial Modeling on Stream Networks in {R}},</span></span>
 <span><span class="co">#&gt;     author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak},</span></span>
 <span><span class="co">#&gt;     year = {2023},</span></span>
-<span><span class="co">#&gt;     note = {{R} package version 0.1.0},</span></span>
+<span><span class="co">#&gt;     note = {{R} package version 0.2.0},</span></span>
 <span><span class="co">#&gt;   }</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="statement-of-need">Statement of Need<a class="anchor" aria-label="anchor" href="#statement-of-need"></a>
 </h2>
-<p>Streams provide vital aquatic services that sustain wildlife, provide drinking and irrigation water, and support recreational and cultural activities. Data are often collected at various locations on a stream network and used to characterize some scientific phenomenon in the stream. Spatial stream network (SSN) models use a spatial statistical modeling framework to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume. SSN models relate a response variable to one or more explanatory variables, a spatially independent error term (i.e., nugget), and up to three spatially dependent error terms: tail-down errors, tail-up errors, and Euclidean errors. Tail-down errors restrict spatial dependence to flow-connected sites (i.e., water flows from an upstream to a downstream site) and incorporate spatial weights (i.e., additive function) to describe the branching network between them. Tail-up errors describe spatial dependence between both flow-connected and flow-unconnected (i.e., sites that share a common downstream junction but not flow) sites, but spatial weights are not required. Euclidean errors describe spatial dependence between sites based on Euclidean distance and are governed by factors not confined to the stream network like regional geology.</p>
+<p>Streams provide vital aquatic services that sustain wildlife, provide drinking and irrigation water, and support recreational and cultural activities. Data are often collected at various locations on a stream network and used to characterize some scientific phenomenon in the stream. Spatial stream network (SSN) models use a spatial statistical modeling framework to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume. SSN models relate a response variable to one or more explanatory variables, a spatially independent error term (i.e., nugget), and up to three spatially dependent error terms: tail-down errors, tail-up errors, and Euclidean errors. Tail-down errors restrict spatial dependence to flow-connected sites (i.e., water flows from an upstream to a downstream site) and incorporate spatial weights (i.e., additive function) to describe the branching network between them. Tail-up errors describe spatial dependence between both flow-connected and flow-unconnected (i.e., sites that share a common downstream junction but not flow) sites, but spatial weights are not required. Euclidean errors describe spatial dependence between sites based on Euclidean distance and are governed by factors not confined to the stream network like regional geology. The <code>SSN2</code> <strong>R</strong> package is designed to help users fit SSN models to their stream network data.</p>
 </div>
 <div class="section level2">
 <h2 id="installation-instructions">Installation Instructions<a class="anchor" aria-label="anchor" href="#installation-instructions"></a>
@@ -125,6 +111,22 @@ <h2 id="installation-instructions">Installation Instructions<a class="anchor" ar
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://usepa.github.io/SSN2/" class="external-link">SSN2</a></span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
+<h2 id="contributing-to-ssn2">Contributing to <code>SSN2</code>
+<a class="anchor" aria-label="anchor" href="#contributing-to-ssn2"></a>
+</h2>
+<p>We encourage users to report bugs and/or contribute to <code>SSN2</code>. For more detail on how to do this, please see our contributing guide (<code>CONTRIBUTING.md</code>).</p>
+</div>
+<div class="section level2">
+<h2 id="getting-help">Getting Help<a class="anchor" aria-label="anchor" href="#getting-help"></a>
+</h2>
+<p>There are several ways to get help with <code>SSN2</code>:</p>
+<ol style="list-style-type: decimal">
+<li>Open a GitHub issue <a href="https://github.com/USEPA/SSN2/issues" class="external-link">link here</a>.</li>
+<li>Email the SSN support team (<a href="mailto:support@spatialstreamnetworks.com" class="email">support@spatialstreamnetworks.com</a> or <a href="mailto:Dumelle.Michael@epa.gov" class="email">Dumelle.Michael@epa.gov</a>)</li>
+<li>Post on a support website like Stack Overflow or Cross Validated.</li>
+</ol>
+</div>
+<div class="section level2">
 <h2 id="example-usage">Example Usage<a class="anchor" aria-label="anchor" href="#example-usage"></a>
 </h2>
 <p>Below we provide a brief example showing how to use <code>SSN2</code>. For a thorough introduction to the software, see our introductory vignette <a href="https://usepa.github.io/SSN2/articles/introduction.html" class="external-link">linked here</a>. For a list of all functions available in <code>SSN2</code>, see our function reference <a href="https://usepa.github.io/SSN2/reference/index.html" class="external-link">linked here</a>.</p>
@@ -146,26 +148,26 @@ <h2 id="example-usage">Example Usage<a class="anchor" aria-label="anchor" href="
 <span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span>
 <span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; </span></span>
+<pre><code><span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt; Call:</span></span>
-<span><span class="co">#&gt; ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p, </span></span>
-<span><span class="co">#&gt;     tailup_type = "exponential", taildown_type = "spherical", </span></span>
+<span><span class="co">#&gt; ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,</span></span>
+<span><span class="co">#&gt;     tailup_type = "exponential", taildown_type = "spherical",</span></span>
 <span><span class="co">#&gt;     euclid_type = "gaussian", additive = "afvArea")</span></span>
-<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt; Residuals:</span></span>
-<span><span class="co">#&gt;     Min      1Q  Median      3Q     Max </span></span>
-<span><span class="co">#&gt; -3.6393 -2.0646 -0.5952  0.2143  0.7497 </span></span>
-<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt;     Min      1Q  Median      3Q     Max</span></span>
+<span><span class="co">#&gt; -3.6393 -2.0646 -0.5952  0.2143  0.7497</span></span>
+<span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt; Coefficients (fixed):</span></span>
-<span><span class="co">#&gt;              Estimate Std. Error z value Pr(&gt;|z|)    </span></span>
+<span><span class="co">#&gt;              Estimate Std. Error z value Pr(&gt;|z|)</span></span>
 <span><span class="co">#&gt; (Intercept) 76.195041   7.871574   9.680  &lt; 2e-16 ***</span></span>
 <span><span class="co">#&gt; ELEV_DEM    -0.026905   0.003646  -7.379  1.6e-13 ***</span></span>
-<span><span class="co">#&gt; AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *  </span></span>
+<span><span class="co">#&gt; AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *</span></span>
 <span><span class="co">#&gt; ---</span></span>
 <span><span class="co">#&gt; Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
-<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt; Pseudo R-squared: 0.6124</span></span>
-<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt;</span></span>
 <span><span class="co">#&gt; Coefficients (covariance):</span></span>
 <span><span class="co">#&gt;               Effect     Parameter   Estimate</span></span>
 <span><span class="co">#&gt;   tailup exponential  de (parsill)  3.800e+00</span></span>
@@ -181,7 +183,7 @@ <h2 id="example-usage">Example Usage<a class="anchor" aria-label="anchor" href="
 <pre><code><span><span class="co">#&gt; # A tibble: 3 × 7</span></span>
 <span><span class="co">#&gt;   term        estimate std.error statistic  p.value conf.low conf.high</span></span>
 <span><span class="co">#&gt;   &lt;chr&gt;          &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;</span></span>
-<span><span class="co">#&gt; 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6     </span></span>
+<span><span class="co">#&gt; 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6</span></span>
 <span><span class="co">#&gt; 2 AREAWTMAP   -0.00910   0.00446     -2.04 4.14e- 2  -0.0178 -0.000356</span></span>
 <span><span class="co">#&gt; 3 ELEV_DEM    -0.0269    0.00365     -7.38 1.60e-13  -0.0341 -0.0198</span></span></code></pre>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
@@ -198,14 +200,14 @@ <h2 id="example-usage">Example Usage<a class="anchor" aria-label="anchor" href="
 <span><span class="co">#&gt; Bounding box:  xmin: -1515032 ymin: 2529461 xmax: -1512690 ymax: 2531883</span></span>
 <span><span class="co">#&gt; Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version</span></span>
 <span><span class="co">#&gt; # A tibble: 6 × 10</span></span>
-<span><span class="co">#&gt;   Summer_mn ELEV_DEM AREAWTMAP .fitted .resid   .hat  .cooksd .std.resid pid  </span></span>
+<span><span class="co">#&gt;   Summer_mn ELEV_DEM AREAWTMAP .fitted .resid   .hat  .cooksd .std.resid pid</span></span>
 <span><span class="co">#&gt;       &lt;dbl&gt;    &lt;int&gt;     &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;    &lt;dbl&gt;      &lt;dbl&gt; &lt;chr&gt;</span></span>
-<span><span class="co">#&gt; 1     11.4      1977      940.    14.4  -3.07 0.0915 0.0962       -1.78  1    </span></span>
-<span><span class="co">#&gt; 2     10.7      1984     1087.    12.9  -2.20 0.114  0.00471      -0.352 2    </span></span>
-<span><span class="co">#&gt; 3     10.4      1993     1087.    12.7  -2.25 0.0372 0.00724      -0.764 3    </span></span>
-<span><span class="co">#&gt; 4     10.1      2007     1087.    12.3  -2.18 0.0251 0.00153      -0.427 4    </span></span>
-<span><span class="co">#&gt; 5     10.1      2009     1087.    12.3  -2.13 0.0374 0.000583     -0.216 5    </span></span>
-<span><span class="co">#&gt; 6      9.81     2012     1109.    12.0  -2.16 0.0602 0.0150       -0.863 6    </span></span>
+<span><span class="co">#&gt; 1     11.4      1977      940.    14.4  -3.07 0.0915 0.0962       -1.78  1</span></span>
+<span><span class="co">#&gt; 2     10.7      1984     1087.    12.9  -2.20 0.114  0.00471      -0.352 2</span></span>
+<span><span class="co">#&gt; 3     10.4      1993     1087.    12.7  -2.25 0.0372 0.00724      -0.764 3</span></span>
+<span><span class="co">#&gt; 4     10.1      2007     1087.    12.3  -2.18 0.0251 0.00153      -0.427 4</span></span>
+<span><span class="co">#&gt; 5     10.1      2009     1087.    12.3  -2.13 0.0374 0.000583     -0.216 5</span></span>
+<span><span class="co">#&gt; 6      9.81     2012     1109.    12.0  -2.16 0.0602 0.0150       -0.863 6</span></span>
 <span><span class="co">#&gt; # ℹ 1 more variable: geometry &lt;POINT [m]&gt;</span></span></code></pre>
 <p>We make predictions at the prediction sites (<code>pred1km</code>) by running <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> (or <code><a href="https://generics.r-lib.org/reference/augment.html" class="external-link">augment()</a></code>:</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
@@ -220,21 +222,45 @@ <h2 id="example-usage">Example Usage<a class="anchor" aria-label="anchor" href="
 <span><span class="co">#&gt; 6 15.12783 14.76358 15.49208</span></span></code></pre>
 </div>
 <div class="section level2">
-<h2 id="contributing-to-ssn2">Contributing to <code>SSN2</code>
-<a class="anchor" aria-label="anchor" href="#contributing-to-ssn2"></a>
+<h2 id="imported-packages">Imported Packages<a class="anchor" aria-label="anchor" href="#imported-packages"></a>
 </h2>
-<p>We encourage users to propose changes to <code>SSN2</code>. Please see our contributing guide (<code>CONTRIBUTING.md</code>) for more detail.</p>
+<p><code>SSN2</code> imports the following <strong>R</strong> packages:</p>
+<ul>
+<li>generics: For exporting generic functions.</li>
+<li>graphics: For visualizations (e.g., <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code>).</li>
+<li>Matrix: For efficient matrix manipulations.</li>
+<li>RSQlite: For various functions that read and write (e.g., <code><a href="reference/ssn_create_distmat.html">ssn_create_distmat()</a></code>).</li>
+<li>sf: For handling spatial data.</li>
+<li>spmodel: For various modeling functions (e.g., <code><a href="https://usepa.github.io/spmodel/reference/randcov_initial.html" class="external-link">randcov_initial()</a></code>) and generic functions (e.g., <code><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv()</a></code>).</li>
+<li>stats: For various modeling functions (e.g., <code><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint()</a></code>).</li>
+<li>tibble: For creating tibbles as output for various functions (e.g., <code><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy()</a></code>).</li>
+<li>utils: For various utility functions.</li>
+<li>withr: For path handling while reading and writing.</li>
+</ul>
 </div>
 <div class="section level2">
-<h2 id="epa-disclaimer">EPA Disclaimer<a class="anchor" aria-label="anchor" href="#epa-disclaimer"></a>
+<h2 id="suggested-packages">Suggested Packages<a class="anchor" aria-label="anchor" href="#suggested-packages"></a>
 </h2>
-<p>The United States Environmental Protection Agency (EPA) GitHub project code is provided on an “as is” basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.</p>
+<p><code>SSN2</code> suggests the following <strong>R</strong> packages:</p>
+<ul>
+<li>ggplot2: For vignette visualizations.</li>
+<li>knitr: For vignette building.</li>
+<li>rmarkdown: For vignette building.</li>
+<li>sp: For making <code>SSN</code> objects from the <code>SSN</code> <strong>R</strong> package compatible with <code>SSN2</code>.</li>
+<li>statmod: For modeling and simulation of inverse Gaussian data.</li>
+<li>testthat: For unit testing.</li>
+</ul>
 </div>
 <div class="section level2">
 <h2 id="license">License<a class="anchor" aria-label="anchor" href="#license"></a>
 </h2>
 <p>This project is licensed under the GNU General Public License, <a href="https://cran.r-project.org/web/licenses/GPL-3" class="external-link">GPL-3</a>.</p>
 </div>
+<div class="section level2">
+<h2 id="epa-disclaimer">EPA Disclaimer<a class="anchor" aria-label="anchor" href="#epa-disclaimer"></a>
+</h2>
+<p>The United States Environmental Protection Agency (EPA) GitHub project code is provided on an “as is” basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.</p>
+</div>
 </div>
 
   </main><aside class="col-md-3"><div class="links">
@@ -285,6 +311,7 @@ <h2 data-toc-skip>Dev status</h2>
 <li><a href="https://cran.r-project.org/web/checks/check_results_SSN2.html" class="external-link"><img src="https://badges.cranchecks.info/worst/SSN2.svg" alt="cran checks"></a></li>
 <li><a href="https://github.com/USEPA/SSN2/actions/workflows/R-CMD-check.yaml" class="external-link"><img src="https://github.com/USEPA/SSN2/actions/workflows/R-CMD-check.yaml/badge.svg" alt="R-CMD-check"></a></li>
 <li><a href="https://www.repostatus.org/#active" class="external-link"><img src="https://www.repostatus.org/badges/latest/active.svg" alt="Project Status: Active – The project has reached a stable, usable state and is being actively developed."></a></li>
+<li><a href="https://joss.theoj.org/papers/66fd932526762f8ccd8bd9c3954e0e3d" class="external-link"><img src="https://joss.theoj.org/papers/66fd932526762f8ccd8bd9c3954e0e3d/status.svg" alt="status"></a></li>
 </ul>
 </div>
 
@@ -293,21 +320,19 @@ <h2 data-toc-skip>Dev status</h2>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/katex-auto.js b/docs/katex-auto.js
new file mode 100644
index 0000000..20651d9
--- /dev/null
+++ b/docs/katex-auto.js
@@ -0,0 +1,14 @@
+// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345
+document.addEventListener("DOMContentLoaded", function () {
+  var mathElements = document.getElementsByClassName("math");
+  var macros = [];
+  for (var i = 0; i < mathElements.length; i++) {
+    var texText = mathElements[i].firstChild;
+    if (mathElements[i].tagName == "SPAN") {
+      katex.render(texText.data, mathElements[i], {
+        displayMode: mathElements[i].classList.contains("display"),
+        throwOnError: false,
+        macros: macros,
+        fleqn: false
+      });
+    }}});
diff --git a/docs/lightswitch.js b/docs/lightswitch.js
new file mode 100644
index 0000000..9467125
--- /dev/null
+++ b/docs/lightswitch.js
@@ -0,0 +1,85 @@
+
+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under the Creative Commons Attribution 3.0 Unported License.
+ * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0.
+ */
+
+const getStoredTheme = () => localStorage.getItem('theme')
+const setStoredTheme = theme => localStorage.setItem('theme', theme)
+
+const getPreferredTheme = () => {
+  const storedTheme = getStoredTheme()
+  if (storedTheme) {
+    return storedTheme
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+
+  return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'
+}
+
+const setTheme = theme => {
+  if (theme === 'auto') {
+    document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'))
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+    document.documentElement.setAttribute('data-bs-theme', theme)
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+}
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+function bsSetupThemeToggle () {
+  'use strict'
+
+  const showActiveTheme = (theme, focus = false) => {
+    var activeLabel, activeIcon;
+
+    document.querySelectorAll('[data-bs-theme-value]').forEach(element => {
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+      element.classList.toggle('active', isActive)
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+    const themeSwitcher = document.querySelector('#dropdown-lightswitch')
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+    themeSwitcher.setAttribute('aria-label', activeLabel)
+    themeSwitcher.querySelector('span').classList.value = activeIcon;
+
+    if (focus) {
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+    }
+  }
+
+  window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => {
+    const storedTheme = getStoredTheme()
+    if (storedTheme !== 'light' && storedTheme !== 'dark') {
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+  })
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+  window.addEventListener('DOMContentLoaded', () => {
+    showActiveTheme(getPreferredTheme())
+
+    document
+      .querySelectorAll('[data-bs-theme-value]')
+      .forEach(toggle => {
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+          const theme = toggle.getAttribute('data-bs-theme-value')
+          setTheme(theme)
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+        })
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+}
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diff --git a/docs/news/index.html b/docs/news/index.html
index f617267..d8bca9a 100644
--- a/docs/news/index.html
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@@ -1,55 +1,64 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"></head><body>
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     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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-      <img src="" class="logo" alt=""><h1>Changelog</h1>
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+      <h1>Changelog</h1>
       <small>Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.2.0" id="ssn2-020">SSN2 0.2.0<a class="anchor" aria-label="anchor" href="#ssn2-020"></a></h2><p class="text-muted">CRAN release: 2024-07-26</p>
+<div class="section level3">
+<h3 id="major-updates-0-2-0">Major Updates<a class="anchor" aria-label="anchor" href="#major-updates-0-2-0"></a></h3>
+<ul><li>Significant testing, documentation, and auxiliary (e.g., <code>README.md</code>) updates as part of a submission to <em>Journal of Open Source Software</em>. Relevant issues associated with the review are available at <a href="https://github.com/USEPA/SSN2/issues/11" class="external-link">#11</a>, <a href="https://github.com/USEPA/SSN2/issues/12" class="external-link">#12</a>, <a href="https://github.com/USEPA/SSN2/issues/13" class="external-link">#13</a>, <a href="https://github.com/USEPA/SSN2/issues/14" class="external-link">#14</a>, <a href="https://github.com/USEPA/SSN2/issues/15" class="external-link">#15</a>, <a href="https://github.com/USEPA/SSN2/issues/16" class="external-link">#16</a>, <a href="https://github.com/USEPA/SSN2/issues/17" class="external-link">#17</a>, <a href="https://github.com/USEPA/SSN2/issues/20" class="external-link">#20</a>, <a href="https://github.com/USEPA/SSN2/issues/21" class="external-link">#21</a>. The review is <a href="https://github.com/openjournals/joss-reviews/issues/6389" class="external-link">linked here</a>.</li>
+<li>Added support for geopackage file formats in the <code>.ssn</code> folder that is accessed when importing SSN objects via <code><a href="../reference/ssn_import.html">ssn_import()</a></code>.</li>
+</ul></div>
+<div class="section level3">
+<h3 id="minor-updates-0-2-0">Minor Updates<a class="anchor" aria-label="anchor" href="#minor-updates-0-2-0"></a></h3>
+<ul><li>Added <code><a href="../reference/ssn_names.html">ssn_names()</a></code> to return column names in the <code>edges</code>, <code>obs</code>, and <code>preds</code> elements of an SSN object.</li>
+<li>Changed <code>Matrix::rankMatrix(X, method = "tolNorm2")</code> to <code>Matrix::rankMatrix(X, method = "qr")</code> to enhance stability when determining linear independence in <code>X</code>, the design matrix of explanatory variables.</li>
+<li>Replaced an error message with a warning message when <code>X</code> has perfect collinearities (i.e., is not full rank).</li>
+<li>Removed <code>format_additive</code> argument from <code><a href="../reference/ssn_import.html">ssn_import()</a></code> because of transition to geopackage support, which eliminates the need to convert additive function values to text.</li>
+<li>Added the <code><a href="../reference/create_netgeom.html">create_netgeom()</a></code> function to create the network geometry column for the <code>edges</code>, <code>obs</code>, and <code>preds</code> elements in an SSN object.</li>
+<li>Minor vignette updates.</li>
+<li>Minor documentation updates.</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-2-0">Bug Fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-2-0"></a></h3>
+<ul><li>Fixed a bug in <code><a href="../reference/SSN_to_SSN2.html">SSN_to_SSN2()</a></code> that caused an error using <code><a href="../reference/ssn_write.html">ssn_write()</a></code> with no prediction sites.</li>
+<li>Replaced <code>names.SSN()</code> with <code><a href="../reference/ssn_names.html">ssn_names()</a></code>, as <code>names.SSN()</code> prevented proper naming of elements in the SSN object.</li>
+</ul></div>
+</div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.1.1" id="ssn2-011">SSN2 0.1.1<a class="anchor" aria-label="anchor" href="#ssn2-011"></a></h2><p class="text-muted">CRAN release: 2024-01-15</p>
 <div class="section level3">
@@ -75,23 +84,23 @@ <h3 id="bug-fixes-0-1-1">Bug Fixes<a class="anchor" aria-label="anchor" href="#b
 <h2 class="pkg-version" data-toc-text="0.1.0" id="ssn2-010">SSN2 0.1.0<a class="anchor" aria-label="anchor" href="#ssn2-010"></a></h2><p class="text-muted">CRAN release: 2023-11-08</p>
 <ul><li>Initial CRAN submission.</li>
 </ul></div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
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 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index 5fccd9c..9757bf9 100644
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+++ b/docs/pkgdown.js
@@ -30,10 +30,10 @@
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+    element.setAttribute('data-bs-original-title', msg);
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+    element.setAttribute('data-bs-original-title', tooltipOriginalTitle);
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@@ -60,7 +60,7 @@
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-      clipboard.on('error', function() {
+      clipboard.on('error', function(e) {
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@@ -152,5 +152,3 @@ async function searchFuse(query, callback) {
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-
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diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 67507a8..6b5728f 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,7 +1,6 @@
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-last_built: 2024-01-15T22:20Z
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diff --git a/docs/reference/AIC.SSN2.html b/docs/reference/AIC.SSN2.html
index d7d3a6f..9cb3e27 100644
--- a/docs/reference/AIC.SSN2.html
+++ b/docs/reference/AIC.SSN2.html
@@ -1,56 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Compute AIC and AICc for one or
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 several fitted model objects for which a log-likelihood
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+value can be obtained."><meta property="og:description" content="Compute AIC and AICc for one or
 several fitted model objects for which a log-likelihood
-value can be obtained."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compute AIC and AICc of fitted model objects</h1>
+
+      <h1>Compute AIC and AICc of fitted model objects</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/AIC.R" class="external-link"><code>R/AIC.R</code></a></small>
       <div class="d-none name"><code>AIC.SSN2.Rd</code></div>
     </div>
@@ -63,42 +49,40 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/AIC.spmodel.html" class="external-link">AICc</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/AIC.spmodel.html" class="external-link">AICc</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Optionally more fitted model objects.</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>The penalty parameter, taken to be 2. Currently not allowed to differ
 from 2 (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>If just one object is provided, a numeric value with the corresponding
+    <p>If just one object is provided, a numeric value with the corresponding
 AIC or AICc.</p>
-
-
 <p>If multiple objects are provided, a <code>data.frame</code> with rows corresponding
 to the objects and columns representing the number of parameters estimated
 (<code>df</code>) and the AIC or AICc.</p>
@@ -108,7 +92,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <p>When comparing models fit by maximum or restricted maximum
 likelihood, the smaller the AIC or AICc, the better the fit. The AICc contains
 a correction to AIC for small sample sizes. AIC and AICc comparisons between <code>"ml"</code>
-and <code>"reml"</code> models are meaningless -- comparisons should only be made
+and <code>"reml"</code> models are meaningless – comparisons should only be made
 within a set of models estimated using <code>"ml"</code> or a set of models estimated
 using <code>"reml"</code>. AIC and AICc comparisons for <code>"reml"</code> must
 use the same fixed effects. To vary the covariance parameters and
@@ -138,28 +122,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 60.18256</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 82.8932</span>
 <span class="r-in"><span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/AIC.spmodel.html" class="external-link">AICc</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 60.76793</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 83.47857</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
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+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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index 69b474c..d9d7c7d 100644
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+++ b/docs/reference/MiddleFork04.ssn.html
@@ -1,56 +1,42 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset</h1>
+
+      <h1>MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/data.R" class="external-link"><code>R/data.R</code></a></small>
       <div class="d-none name"><code>MiddleFork04.ssn.Rd</code></div>
     </div>
@@ -66,45 +52,44 @@
     <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p><code>edges</code> are a modified version of the United States
 National Hydrography Dataset
-(http://nhd.usgs.gov/). <code>sites</code>, <code>pred1km</code>, <code>CapeHorn</code>
-and <code>Knapp</code> are unpublished United States Forest Service data.</p>
+(http://nhd.usgs.gov/). <code>sites</code>, <code>pred1km</code> and <code>CapeHorn</code>
+are unpublished United States Forest Service data.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>The <code>MiddleFork04.ssn</code> folder contains five shapefiles:</p>
-<ul><li><p>edges: polyline shapefile representing the stream network</p></li>
-<li><p>sites: point shapefile representing the observed site locations</p></li>
-<li><p>pred1km: point shapefile representing prediction site locations at
+    <p>The <code>MiddleFork04.ssn</code> folder contains five geopackages:</p>
+<ul><li><p>edges: geopackage with LINESTRING geometry representing the stream network</p></li>
+<li><p>sites: geopackage with POINT geometry representing the observed site locations</p></li>
+<li><p>pred1km: geopackage with POINT geometry prediction site locations at
 approximately 1km intervals throughout the stream network</p></li>
-<li><p>Knapp: point shapefile representing prediction site locations on the Knapp River</p></li>
-<li><p>CapeHorn: point shapefile representing prediction site locations on the Cape Horn River</p></li>
-</ul><p>The <code>MiddleFork04.ssn</code> includes one text file, <code>netID1.txt</code>, which contains the
-topological information for the stream network in the Middle Fork 2004
+<li><p>CapeHorn: geopackage with POINT geometry representing prediction site locations on the Cape Horn River</p></li>
+</ul><p>The <code>MiddleFork04.ssn</code> includes two text files, <code>netID1.txt</code> and <code>netID2.txt</code>, which contain the
+topological information for the two stream networks in the Middle Fork 2004
 dataset.</p>
 <p>The distance folder contains four folders that store the hydrologic
-distance matrices for each of the point shapefiles (<code>obs</code>, <code>CapeHorn</code>,
-<code>Knapp</code>, and <code>pred1km</code>). See <code><a href="ssn_create_distmat.html">ssn_create_distmat()</a></code> for a
+distance matrices for each of the point datasets (<code>obs</code>, <code>CapeHorn</code>, and <code>pred1km</code>). See <code><a href="ssn_create_distmat.html">ssn_create_distmat()</a></code> for a
 detailed description of the distance matrix file structure.</p>
 <p>Attribute data is also stored within each of the spatial
 datasets. The column names are defined as follows:</p>
-<p><code>edges</code>:</p><ul><li><p>COMID: Common identifier of an NHD feature or relationship</p></li>
+<p><code>edges</code>:</p><ul><li><p>rid: Reach identifier</p></li>
+<li><p>COMID: Common identifier of an NHD feature or relationship</p></li>
 <li><p>GNIS_Name: Feature name as found in the Geographic Names Information System</p></li>
 <li><p>REACHCODE: Unique identifier for a reach. The first 8 digits contain the identfier for the HUC8 and the last 6 digits are a unique within-HUC8 identifier for the reach</p></li>
 <li><p>FTYPE: three-digit integer used to classify hydrography features in the NHD and define subtypes</p></li>
 <li><p>FCODE: Numeric code that contains the feature type and its attributes as found in the NHDFCode lookup table</p></li>
-<li><p>CDRAINAG: Cumulative drainage area (km2) for the lowermost location on the edge</p></li>
 <li><p>AREAWTMAP: Area weighted mean annual precipitation (mm) at the lowermost location on the edge</p></li>
 <li><p>SLOPE: Slope of the edge (cm/cm)</p></li>
-<li><p>h2oAreaKm2: Watershed area (km2) for the lowermost location on the line segment</p></li>
-<li><p>rid: Reach identifier</p></li>
+<li><p>rcaAreaKm2: Reach contributing area (km2) for each edge feature. The RCA represents the land area that drains directly to the edge feature</p></li>
+<li><p>h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature</p></li>
 <li><p>areaPI: Segment proportional influence value, calculated using watershed area (h2oAreaKm2)</p></li>
 <li><p>afvArea: Additive function value, calculated using areaPI</p></li>
 <li><p>upDist: Distance from the stream outlet (most downstream location in the the stream network) to the uppermost location on the line segment</p></li>
 <li><p>Length: Length of line segment (m)</p></li>
 <li><p>netID: Network identifier</p></li>
-</ul><p><code>sites</code>:</p><ul><li><p>STREAMNAME: Stream name</p></li>
+</ul><p><code>sites</code>:</p><ul><li><p>rid: Reach identifier of the edge the site resides on</p></li>
+<li><p>pid: Point identifier</p></li>
+<li><p>STREAMNAME: Stream name</p></li>
 <li><p>COMID: Common identifier of an NHD feature or relationship</p></li>
-<li><p>CDRAINAG: Cumulative drainage area (km2)</p></li>
 <li><p>AREAWTMAP: Area weighted mean annual precipitation (mm) at lowermost
 location on the line segment where the site resides</p></li>
 <li><p>SLOPE: Slope of the line segment (cm/cm) where the site resides</p></li>
@@ -124,30 +109,29 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 from 1980-2009 across 10 COOP air stations within the domain.
 MWMT = maximum 7-day moving average of the maximum daily temperature
 (i.e. maximum of all the 7-day maximums)</p></li>
-<li><p>NEAR_X: x coordinate</p></li>
-<li><p>NEAR_Y: y coordinate</p></li>
-<li><p>rid: Reach identifier of the edge the site resides on</p></li>
-<li><p>ratio: Site ratio value; provides the proportional distance along the edge to the site location</p></li>
+<li><p>rcaAreaKm2: Reach contributing area (km2) for each edge feature the site resides on. The RCA represents the land area that drains directly to the edge feature</p></li>
+<li><p>h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature the site resides on</p></li>
+<li><p>ratio: Site ratio value; provides the proportional distance from the downstream node of the edge to the site location</p></li>
 <li><p>upDist: Distance upstream from the stream outlet (m)</p></li>
 <li><p>afvArea: Additive function value calculated using waterhsed area (h2oAreaKm2)</p></li>
 <li><p>locID: Location identifier</p></li>
 <li><p>netID: Stream network identifier</p></li>
+<li><p>snapdist: Distance (m) that site was moved when it was snapped to the edges</p></li>
+</ul><p><code>pred1km</code> and <code>CapeHorn</code>:</p><ul><li><p>rid: Reach identifier of the edge the site resides on</p></li>
 <li><p>pid: Point identifier</p></li>
-</ul><p><code>pred1km</code>, <code>CapeHorn</code>, and <code>Knapp</code>:</p><ul><li><p>COMID: Common identifier of an NHD feature or relationship</p></li>
+<li><p>COMID: Common identifier of an NHD feature or relationship</p></li>
 <li><p>GNIS_Name: Feature name of the edge the site resides on, as found in the Geographic Names Information System</p></li>
-<li><p>CDRAINAG: Cumulative drainage area (km2)</p></li>
 <li><p>AREAWTMAP: Area weighted mean annual precipitation (mm) at lowermost location on the line segment where the site resides</p></li>
 <li><p>SLOPE: Slope of the line segment (cm/cm) where the site resides</p></li>
 <li><p>ELEV_DEM: Elevation at the site based on a 30m DEM</p></li>
-<li><p>NEAR_X: x coordinate</p></li>
-<li><p>NEAR_Y: y coordinate</p></li>
-<li><p>rid: Reach identifier of the edge the site resides on</p></li>
+<li><p>rcaAreaKm2: Reach contributing area (km2) for each edge feature the site resides on. The RCA represents the land area that drains directly to the edge feature</p></li>
+<li><p>h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature the site resides on</p></li>
 <li><p>ratio: Site ratio value; provides the proportional distance along the edge to the site location</p></li>
 <li><p>upDist: Distance upstream from the stream outlet (m)</p></li>
 <li><p>afvArea: Additive function value calculated using watershed area (h2oAreaKm2)</p></li>
 <li><p>locID: Location identifier</p></li>
 <li><p>netID: Stream network identifier</p></li>
-<li><p>pid: Point identifier</p></li>
+<li><p>snapdist: Distance (m) that site was moved when it was snapped to the edges</p></li>
 <li><p>FlowCMS: Average stream flow (cubic meters per sec) for August, by year, from 1950-2010 across 9 USGS gauges in the region</p></li>
 <li><p>AirMEANc: Average mean air temperature (C) from July 15 - August 31, from 1980-2009 across 10 COOP air stations within the domain</p></li>
 <li><p>AirMWMTc: Average maximum air temperature (C) from July 15 - August 31, from 1980-2009 across 10 COOP air stations within the domain. MWMT = maximum 7-day moving average of the maximum daily temperature(i.e. maximum of all the 7-day maximums)</p></li>
@@ -157,23 +141,23 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index"><p><a href="mf04p.html">mf04p</a> for the Middle For 04 data as an <code>SSN</code> object.</p></div>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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index 0cbce1d..e7a1810 100644
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+++ b/docs/reference/SSN2-package.html
@@ -1,54 +1,40 @@
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+.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included."></head><body>
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-      <img src="" class="logo" alt=""><h1>SSN2: Spatial Modeling on Stream Networks</h1>
+
+      <h1>SSN2: Spatial Modeling on Stream Networks</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/SSN2.R" class="external-link"><code>R/SSN2.R</code></a></small>
       <div class="d-none name"><code>SSN2-package.Rd</code></div>
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@@ -74,23 +60,23 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 <li><p>Dan Isaak [contributor]</p></li>
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diff --git a/docs/reference/SSN_to_SSN2.html b/docs/reference/SSN_to_SSN2.html
index cc68724..3266288 100644
--- a/docs/reference/SSN_to_SSN2.html
+++ b/docs/reference/SSN_to_SSN2.html
@@ -1,56 +1,42 @@
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Convert an S4 SpatialStreamNetwork object
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+SSN2 package."><meta property="og:description" content="Convert an S4 SpatialStreamNetwork object
 created in the SSN package to an S3 SSN object used in the
-SSN2 package."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Convert object from <code>SpatialStreamNetwork</code> class to <code>SSN</code> class</h1>
+
+      <h1>Convert object from <code>SpatialStreamNetwork</code> class to <code>SSN</code> class</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/SSN_to_SSN2.R" class="external-link"><code>R/SSN_to_SSN2.R</code></a></small>
       <div class="d-none name"><code>SSN_to_SSN2.Rd</code></div>
     </div>
@@ -63,33 +49,20 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SSN_to_SSN2</span><span class="op">(</span><span class="va">object</span>, edge_additive <span class="op">=</span> <span class="cn">NULL</span>, site_additive <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SSN_to_SSN2</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
-<dd><p>A SpatialStreamNetwork object</p></dd>
 
 
-<dt>edge_additive</dt>
-<dd><p>A character vector of additive function value
-column names found in edges. Default is NULL. See Details for
-more information.</p></dd>
-
-
-<dt>site_additive</dt>
-<dd><p>A character vector of additive function value
-column names found in the observed sites and prediction
-sites. See Details for more information. Default is NULL.</p></dd>
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>A SpatialStreamNetwork object</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An S3 <code>SSN</code> class object, with additive function value
-columns converted to text format.</p>
+    <p>An S3 <code>SSN</code> class object.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -100,45 +73,25 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 used in the <code>SSN2</code> package. Note that <code><a href="ssn_import.html">ssn_import</a></code> is
 used to create an S3 <code>SSN</code> object from data stored locally in a .ssn
 directory.</p>
-<p>Additive function values are used to generate spatial weights for
-the tail-up covariance function used in <code>ssn_glm</code>. The range
-of additive function values are restricted to \(0 \le AFV \le
-  1\). In the <code>SSN2</code> package, columns containing additive
-function values are stored as text, rather than numeric
-format. This prevents values less than 1 with more than 10 digits
-from being truncated when writing/reading shapefiles (and their
-.dbf tables). The columns containing additive function values are
-specified using the <code>edge_additive</code> and <code>site_additive</code> arguments
-and converted to character format in the <code>SSN</code> class object
-returned. The arguments <code>edge_additive</code> and <code>site_additive</code>
-accept a single column name in character format, or a vector
-containing multiple column names. Note that, column names for
-additive function values on the edges, sites, and prediction
-sites may differ. If a column specified in <code>edge_additive</code> or
-<code>site_additive</code> is not present, the function will return a
-warning, rather than an error. Columns containing additive
-function values can also be converted to text manually using the
-<code><a href="https://rdrr.io/r/base/formatc.html" class="external-link">formatC</a></code> function, which provides the
-flexibility needed to store the values with their full precision.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/Torgegram-1.png b/docs/reference/Torgegram-1.png
index 4a98d33..4873b03 100644
Binary files a/docs/reference/Torgegram-1.png and b/docs/reference/Torgegram-1.png differ
diff --git a/docs/reference/Torgegram.html b/docs/reference/Torgegram.html
index bcd779a..490b9a3 100644
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+++ b/docs/reference/Torgegram.html
@@ -1,54 +1,40 @@
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compute the empirical semivariogram</h1>
+
+      <h1>Compute the empirical semivariogram</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/Torgegram.R" class="external-link"><code>R/Torgegram.R</code></a></small>
       <div class="d-none name"><code>Torgegram.Rd</code></div>
     </div>
@@ -72,31 +58,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>formula</dt>
+
+
+<dl><dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>A formula describing the fixed effect structure.</p></dd>
 
 
-<dt>ssn.object</dt>
+<dt id="arg-ssn-object">ssn.object<a class="anchor" aria-label="anchor" href="#arg-ssn-object"></a></dt>
 <dd><p>A spatial stream network object with class <code>SSN</code>.</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>The Torgegram type. A vector with possible values <code>"flowcon"</code>
 for flow-connected distances, <code>"flowuncon"</code> for flow-unconnected distances,
 and <code>"euclid"</code> for Euclidean distances. The default is to show both
 flow-connected and flow-unconnected distances.</p></dd>
 
 
-<dt>bins</dt>
+<dt id="arg-bins">bins<a class="anchor" aria-label="anchor" href="#arg-bins"></a></dt>
 <dd><p>The number of equally spaced bins. The default is 15.</p></dd>
 
 
-<dt>cutoff</dt>
+<dt id="arg-cutoff">cutoff<a class="anchor" aria-label="anchor" href="#arg-cutoff"></a></dt>
 <dd><p>The maximum distance considered.
 The default is half the diagonal of the bounding box from the coordinates.</p></dd>
 
 
-<dt>partition_factor</dt>
+<dt id="arg-partition-factor">partition_factor<a class="anchor" aria-label="anchor" href="#arg-partition-factor"></a></dt>
 <dd><p>An optional formula specifying the partition factor.
 If specified, semivariances are only computed for observations sharing the
 same level of the partition factor.</p></dd>
@@ -104,9 +92,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list with elements correspond to <code>type</code>. Each element
+    <p>A list with elements correspond to <code>type</code>. Each element
 is data frame with distance bins (<code>bins</code>), the  average distance
 (<code>dist</code>), the semivariance (<code>gamma</code>), and the
 number of (unique) pairs (<code>np</code>) for the respective <code>type</code>.</p>
@@ -138,7 +124,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
     <p>Zimmerman, D. L., &amp; Ver Hoef, J. M. (2017). The Torgegram for fluvial
 variography: characterizing spatial dependence on stream networks.
 <em>Journal of Computational and Graphical Statistics</em>,
-<b>26(2)</b>, 253--264.</p>
+<b>26(2)</b>, 253–264.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -159,23 +145,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="Torgegram-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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index bf6cf21..19504a3 100644
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+++ b/docs/reference/anova.SSN2.html
@@ -1,54 +1,40 @@
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+a likelihood ratio test for two fitted model objects."><meta property="og:description" content="Compute analysis of variance tables for a fitted model object or
+a likelihood ratio test for two fitted model objects."></head><body>
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+
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compute analysis of variance and likelihood ratio tests of fitted model objects</h1>
+
+      <h1>Compute analysis of variance and likelihood ratio tests of fitted model objects</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/anova.R" class="external-link"><code>R/anova.R</code></a></small>
       <div class="d-none name"><code>anova.SSN2.Rd</code></div>
     </div>
@@ -60,36 +46,38 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, test <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">Terms</span>, <span class="va">L</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, test <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">Terms</span>, <span class="va">L</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for anova.ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'anova.ssn_lm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for anova.ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'anova.ssn_glm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>An additional fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>
 (for <code><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova()</a></code>).</p></dd>
 
 
-<dt>test</dt>
+<dt id="arg-test">test<a class="anchor" aria-label="anchor" href="#arg-test"></a></dt>
 <dd><p>A logical value indicating whether p-values from asymptotic Chi-squared
 hypothesis tests should be returned. Defaults to <code>TRUE</code>.</p></dd>
 
 
-<dt>Terms</dt>
+<dt id="arg-terms">Terms<a class="anchor" aria-label="anchor" href="#arg-terms"></a></dt>
 <dd><p>An optional character or integer vector that specifies terms in the model
 used to jointly compute test statistics and p-values (if <code>test = TRUE</code>)
 against a null hypothesis of zero. <code>Terms</code> is only used when a single fitted model
@@ -101,7 +89,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 integer representation matches the positions of this vector).</p></dd>
 
 
-<dt>L</dt>
+<dt id="arg-l">L<a class="anchor" aria-label="anchor" href="#arg-l"></a></dt>
 <dd><p>An optional numeric matrix or list specifying linear combinations
 of the coefficients in the model used to compute test statistics
 and p-values (if <code>test = TRUE</code>) for coefficient constraints corresponding to a null
@@ -115,29 +103,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 way to obtain all possible coefficients is to run <code>tidy(object)$term</code>.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object from <code>anova(object)</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>When one fitted model object is present, <code><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova()</a></code></p>
-
-
-<p>returns a data frame with degrees of
+    <p>When one fitted model object is present, <code><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova()</a></code>
+returns a data frame with degrees of
 freedom (<code>Df</code>), test statistics (<code>Chi2</code>), and p-values
 (<code>Pr(&gt;Chi2)</code> if <code>test = TRUE</code>) corresponding
 to asymptotic Chi-squared hypothesis tests for each model term.</p>
-
-
 <p>When two fitted model objects are present, <code><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova()</a></code> returns a data frame
 with the difference in degrees of freedom between the full and reduced model (<code>Df</code>), a test
 statistic (<code>Chi2</code>), and a p-value corresponding to the likelihood ratio test
 (<code>Pr(&gt;Chi2)</code> if <code>test = TRUE</code>).</p>
-
-
 <p>Whether one or two fitted model objects are provided,
 <code><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy()</a></code> can be used
 to obtain tidy tibbles of the <code>anova(object)</code> output.</p>
@@ -175,36 +155,36 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Analysis of Variance Table</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Response: Summer_mn</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             Df    Chi2 Pr(&gt;Chi2)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)  1 132.633 &lt; 2.2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM     1  87.829 &lt; 2.2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>             Df   Chi2 Pr(&gt;Chi2)    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)  1 84.156 &lt; 2.2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM     1 60.799 6.322e-15 ***</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span>
 <span class="r-in"><span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 2 × 4</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   effects        df statistic  p.value</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> (Intercept)     1     133.  1.09<span style="color: #949494;">e</span><span style="color: #BB0000;">-30</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ELEV_DEM        1      87.8 7.14<span style="color: #949494;">e</span><span style="color: #BB0000;">-21</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> (Intercept)     1      84.2 4.57<span style="color: #949494;">e</span><span style="color: #BB0000;">-20</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ELEV_DEM        1      60.8 6.32<span style="color: #949494;">e</span><span style="color: #BB0000;">-15</span></span>
 </code></pre></div>
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diff --git a/docs/reference/augment.SSN2.html b/docs/reference/augment.SSN2.html
index f0a2429..1cf31a3 100644
--- a/docs/reference/augment.SSN2.html
+++ b/docs/reference/augment.SSN2.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Augment accepts a fitted model object and a data set and adds
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 begin with a . prefix to avoid overwriting columns in the original
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@@ -9,7 +9,7 @@
 and newdata are specified. When augmenting the original data, diagnostic
 statistics are augmented to each row in the data set. When augmenting new data,
 predictions and optional intervals (confidence or prediction) or standard errors are augmented to each
-row in the new data set."><title>Augment data with information from fitted model objects — augment.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Augment data with information from fitted model objects — augment.SSN2"><meta property="og:description" content="Augment accepts a fitted model object and a data set and adds
+row in the new data set."><meta property="og:description" content="Augment accepts a fitted model object and a data set and adds
 information about each observation in the data set. New columns always
 begin with a . prefix to avoid overwriting columns in the original
 data set.
@@ -19,54 +19,40 @@
 and newdata are specified. When augmenting the original data, diagnostic
 statistics are augmented to each row in the data set. When augmenting new data,
 predictions and optional intervals (confidence or prediction) or standard errors are augmented to each
-row in the new data set."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Augment data with information from fitted model objects</h1>
+
+      <h1>Augment data with information from fitted model objects</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/augment.R" class="external-link"><code>R/augment.R</code></a></small>
       <div class="d-none name"><code>augment.SSN2.Rd</code></div>
     </div>
@@ -87,7 +73,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/augment.html" class="external-link">augment</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  drop <span class="op">=</span> <span class="cn">TRUE</span>,</span>
@@ -98,7 +84,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/augment.html" class="external-link">augment</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  drop <span class="op">=</span> <span class="cn">TRUE</span>,</span>
@@ -115,17 +101,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>drop</dt>
+<dt id="arg-drop">drop<a class="anchor" aria-label="anchor" href="#arg-drop"></a></dt>
 <dd><p>A logical indicating whether to drop extra variables in the
 fitted model object <code>x</code> when augmenting. The default for <code>drop</code> is <code>TRUE</code>.
 <code>drop</code> is ignored if augmenting <code>newdata</code>.</p></dd>
 
 
-<dt>newdata</dt>
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
 <dd><p>A vector that contains the names of the prediction <code>sf</code>
 objects from the original <code>ssn.object</code> requiring prediction.
 All of the original explanatory variables used to create the fitted model object <code>x</code>
@@ -136,37 +124,37 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 all prediction <code>sf</code> objects.</p></dd>
 
 
-<dt>se_fit</dt>
+<dt id="arg-se-fit">se_fit<a class="anchor" aria-label="anchor" href="#arg-se-fit"></a></dt>
 <dd><p>Logical indicating whether or not a <code>.se.fit</code> column should
 be added to augmented output. Passed to <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> and
 defaults to <code>FALSE</code>.</p></dd>
 
 
-<dt>interval</dt>
+<dt id="arg-interval">interval<a class="anchor" aria-label="anchor" href="#arg-interval"></a></dt>
 <dd><p>Character indicating the type of confidence interval columns to
 add to the augmented <code>newdata</code> output. Passed to <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> and defaults
 to <code>"none"</code>.</p></dd>
 
 
-<dt>level</dt>
+<dt id="arg-level">level<a class="anchor" aria-label="anchor" href="#arg-level"></a></dt>
 <dd><p>Tolerance/confidence level. The default is <code>0.95</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional arguments to <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> when augmenting <code>newdata</code>.</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>The scale (<code>response</code> or <code>link</code>) of predictions obtained
 using <code>ssn_glm</code> objects.</p></dd>
 
 
-<dt>newdata_size</dt>
+<dt id="arg-newdata-size">newdata_size<a class="anchor" aria-label="anchor" href="#arg-newdata-size"></a></dt>
 <dd><p>The <code>size</code> value for each observation in <code>newdata</code>
 used when predicting for the binomial family.</p></dd>
 
 
-<dt>var_correct</dt>
+<dt id="arg-var-correct">var_correct<a class="anchor" aria-label="anchor" href="#arg-var-correct"></a></dt>
 <dd><p>A logical indicating whether to return the corrected prediction
 variances when predicting via models fit using <code>ssn_glm</code>. The default is
 <code>TRUE</code>.</p></dd>
@@ -174,9 +162,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>When augmenting the original data set, a tibble with additional columns</p><ul><li><p><code>.fitted</code>: Fitted value</p></li>
+    <p>When augmenting the original data set, a tibble with additional columns</p><ul><li><p><code>.fitted</code>: Fitted value</p></li>
 <li><p><code>.resid</code>: Response residual (the difference between observed and fitted values)</p></li>
 <li><p><code>.hat</code>: Leverage (diagonal of the hat matrix)</p></li>
 <li><p><code>.cooksd</code>: Cook's distance</p></li>
@@ -221,65 +207,65 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Simple feature collection with 45 features and 8 fields</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Geometry type: POINT</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Dimension:     XY</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Bounding box:  xmin: -1530805 ymin: 2527111 xmax: -1503079 ymax: 2537823</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Bounding box:  xmin: -1530805 ymin: 920324.3 xmax: -1503079 ymax: 931036.6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 45 × 9</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    Summer_mn ELEV_DEM .fitted .resid    .hat    .cooksd .std.resid pid  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  <span style="color: #BCBCBC;">*</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>     11.4      <span style="text-decoration: underline;">1</span>977    14.3  -<span style="color: #BB0000;">2.96</span> 0.157   0.135         -<span style="color: #BB0000;">1.31</span>   1    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>     10.7      <span style="text-decoration: underline;">1</span>984    14.1  -<span style="color: #BB0000;">3.42</span> 0.092<span style="text-decoration: underline;">4</span>  0.018<span style="text-decoration: underline;">4</span>        -<span style="color: #BB0000;">0.631</span>  2    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span>     10.4      <span style="text-decoration: underline;">1</span>993    13.9  -<span style="color: #BB0000;">3.45</span> 0.030<span style="text-decoration: underline;">1</span>  0.010<span style="text-decoration: underline;">3</span>        -<span style="color: #BB0000;">0.827</span>  3    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span>     10.1      <span style="text-decoration: underline;">2</span>007    13.5  -<span style="color: #BB0000;">3.35</span> 0.007<span style="text-decoration: underline;">98</span> 0.000<span style="text-decoration: underline;">239</span>      -<span style="color: #BB0000;">0.245</span>  4    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span>     10.1      <span style="text-decoration: underline;">2</span>009    13.4  -<span style="color: #BB0000;">3.30</span> 0.010<span style="text-decoration: underline;">5</span>  0.000<span style="text-decoration: underline;">006</span>74     0.035<span style="text-decoration: underline;">9</span> 5    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span>      9.81     <span style="text-decoration: underline;">2</span>012    13.3  -<span style="color: #BB0000;">3.52</span> 0.003<span style="text-decoration: underline;">50</span> 0.002<span style="text-decoration: underline;">85</span>       -<span style="color: #BB0000;">1.28</span>   6    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span>      9.76     <span style="text-decoration: underline;">2</span>023    13.0  -<span style="color: #BB0000;">3.26</span> 0.017<span style="text-decoration: underline;">9</span>  0.000<span style="text-decoration: underline;">014</span>4      0.040<span style="text-decoration: underline;">1</span> 7    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span>      9.77     <span style="text-decoration: underline;">2</span>023    13.0  -<span style="color: #BB0000;">3.25</span> 0.016<span style="text-decoration: underline;">9</span>  0.000<span style="text-decoration: underline;">074</span>1      0.093<span style="text-decoration: underline;">5</span> 8    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span>      9.53     <span style="text-decoration: underline;">2</span>026    12.9  -<span style="color: #BB0000;">3.40</span> 0.008<span style="text-decoration: underline;">31</span> 0.000<span style="text-decoration: underline;">891</span>      -<span style="color: #BB0000;">0.463</span>  9    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span>     12.6      <span style="text-decoration: underline;">1</span>988    14.0  -<span style="color: #BB0000;">1.45</span> 0.056<span style="text-decoration: underline;">1</span>  0.029<span style="text-decoration: underline;">0</span>        -<span style="color: #BB0000;">1.02</span>   10   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    Summer_mn ELEV_DEM .fitted  .resid    .hat   .cooksd .std.resid pid  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  <span style="color: #BCBCBC;">*</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>      14.9     <span style="text-decoration: underline;">1</span>947    14.4  0.503  0.111   0.016<span style="text-decoration: underline;">5</span>         0.545 1    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>      14.7     <span style="text-decoration: underline;">1</span>952    14.2  0.473  0.055<span style="text-decoration: underline;">7</span>  0.001<span style="text-decoration: underline;">73</span>        0.249 2    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span>      14.6     <span style="text-decoration: underline;">1</span>958    14.0  0.568  0.033<span style="text-decoration: underline;">7</span>  0.006<span style="text-decoration: underline;">58</span>        0.625 3    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span>      15.2     <span style="text-decoration: underline;">1</span>923    15.2 -<span style="color: #BB0000;">0.016</span><span style="color: #BB0000; text-decoration: underline;">4</span> 0.074<span style="text-decoration: underline;">4</span>  0.008<span style="text-decoration: underline;">93</span>        0.490 4    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span>      14.5     <span style="text-decoration: underline;">1</span>932    14.9 -<span style="color: #BB0000;">0.439</span>  0.020<span style="text-decoration: underline;">2</span>  0.015<span style="text-decoration: underline;">8</span>        -<span style="color: #BB0000;">1.25</span>  5    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span>      15.3     <span style="text-decoration: underline;">1</span>940    14.7  0.634  0.005<span style="text-decoration: underline;">69</span> 0.000<span style="text-decoration: underline;">970</span>       0.584 6    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span>      15.1     <span style="text-decoration: underline;">1</span>940    14.7  0.414  0.001<span style="text-decoration: underline;">62</span> 0.000<span style="text-decoration: underline;">050</span>7     -<span style="color: #BB0000;">0.250</span> 7    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span>      14.9     <span style="text-decoration: underline;">1</span>945    14.5  0.454  0.057<span style="text-decoration: underline;">4</span>  0.014<span style="text-decoration: underline;">3</span>        -<span style="color: #BB0000;">0.706</span> 8    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span>      15.0     <span style="text-decoration: underline;">1</span>948    14.4  0.607  0.073<span style="text-decoration: underline;">9</span>  0.006<span style="text-decoration: underline;">66</span>        0.425 9    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span>      15.0     <span style="text-decoration: underline;">1</span>950    14.3  0.705  0.058<span style="text-decoration: underline;">1</span>  0.019<span style="text-decoration: underline;">6</span>         0.821 10   </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 35 more rows</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: geometry &lt;POINT [m]&gt;</span></span>
 <span class="r-in"><span><span class="fu"><a href="https://generics.r-lib.org/reference/augment.html" class="external-link">augment</a></span><span class="op">(</span><span class="va">ssn_mod</span>, newdata <span class="op">=</span> <span class="st">"CapeHorn"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Simple feature collection with 654 features and 20 fields</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Simple feature collection with 654 features and 19 fields</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Geometry type: POINT</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Dimension:     XY</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Bounding box:  xmin: -1516634 ymin: 2527816 xmax: -1512722 ymax: 2531418</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 654 × 21</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       COMID GNIS_NAME       CDRAINAG AREAWTMAP   SLOPE ELEV_DEM FlowCMS AirMEANc</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  <span style="color: #BCBCBC;">*</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>013    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>013    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>013    34.8     21.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 23<span style="text-decoration: underline;">519</span>461 Cape Horn Creek     69.9     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Bounding box:  xmin: -1516634 ymin: 921030.2 xmax: -1512722 ymax: 924632.2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 654 × 20</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      rid   pid    COMID AREAWTMAP   SLOPE ELEV_DEM FlowCMS AirMEANc AirMWMTc</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>    34  <span style="text-decoration: underline;">1</span>494 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>    34  <span style="text-decoration: underline;">1</span>495 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span>    34  <span style="text-decoration: underline;">1</span>496 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span>    34  <span style="text-decoration: underline;">1</span>497 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span>    34  <span style="text-decoration: underline;">1</span>498 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span>    34  <span style="text-decoration: underline;">1</span>499 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span>    34  <span style="text-decoration: underline;">1</span>500 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>013    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span>    34  <span style="text-decoration: underline;">1</span>501 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>013    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span>    34  <span style="text-decoration: underline;">1</span>502 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>013    34.8     21.5     35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span>    34  <span style="text-decoration: underline;">1</span>503 23<span style="text-decoration: underline;">519</span>461     <span style="text-decoration: underline;">1</span>087. 0.008<span style="text-decoration: underline;">43</span>     <span style="text-decoration: underline;">2</span>011    34.8     21.5     35.5</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 644 more rows</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 13 more variables: AirMWMTc &lt;dbl&gt;, NEAR_X &lt;dbl&gt;, NEAR_Y &lt;dbl&gt;, rid &lt;int&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   ratio &lt;dbl&gt;, afvArea &lt;chr&gt;, upDist &lt;dbl&gt;, locID &lt;int&gt;, netID &lt;int&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   pid &lt;int&gt;, netgeom &lt;chr&gt;, .fitted &lt;dbl&gt;, geometry &lt;POINT [m]&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 11 more variables: rcaAreaKm2 &lt;dbl&gt;, h2oAreaKm2 &lt;dbl&gt;, ratio &lt;dbl&gt;,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   snapdist &lt;dbl&gt;, upDist &lt;dbl&gt;, afvArea &lt;dbl&gt;, locID &lt;int&gt;, netID &lt;dbl&gt;,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   netgeom &lt;chr&gt;, .fitted &lt;dbl&gt;, geometry &lt;POINT [m]&gt;</span></span>
 </code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
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 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/coef.SSN2.html b/docs/reference/coef.SSN2.html
index 5180387..d731f45 100644
--- a/docs/reference/coef.SSN2.html
+++ b/docs/reference/coef.SSN2.html
@@ -1,54 +1,40 @@
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+coefficients is an alias for it."><meta property="og:description" content="coef extracts fitted model coefficients from fitted model objects.
+coefficients is an alias for it."></head><body>
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-      <img src="" class="logo" alt=""><h1>Extract fitted model coefficients</h1>
+
+      <h1>Extract fitted model coefficients</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/coef.R" class="external-link"><code>R/coef.R</code></a></small>
       <div class="d-none name"><code>coef.SSN2.Rd</code></div>
     </div>
@@ -60,26 +46,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coef</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"fixed"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coefficients</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"fixed"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coef</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"fixed"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coefficients</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"fixed"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p><code>"fixed"</code> for fixed effect coefficients, <code>"tailup"</code> for
 tailup covariance parameter coefficients, <code>"taildown"</code> for
 taildown covariance parameter coefficients, <code>"euclid"</code> for
@@ -91,15 +79,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 Defaults to <code>"fixed"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A named vector of coefficients.</p>
+    <p>A named vector of coefficients.</p>
     </div>
 
     <div class="section level2">
@@ -119,34 +105,34 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coef</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)    ELEV_DEM </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71.32316197 -0.02882225 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  80.8578372  -0.0341245 </span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coef</a></span><span class="op">(</span><span class="va">ssn_mod</span>, type <span class="op">=</span> <span class="st">"tailup"</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           de        range </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.415469e+00 1.174539e+06 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.390296e+00 1.306032e+05 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"class")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "tailup_exponential"</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coefficients</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)    ELEV_DEM </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71.32316197 -0.02882225 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  80.8578372  -0.0341245 </span>
 </code></pre></div>
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+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
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-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a>
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-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
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-  <a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Confidence intervals for fitted model parameters</h1>
+
+      <h1>Confidence intervals for fitted model parameters</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/confint.R" class="external-link"><code>R/confint.R</code></a></small>
       <div class="d-none name"><code>confint.SSN2.Rd</code></div>
     </div>
@@ -60,37 +46,37 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">parm</span>, level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">parm</span>, level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>parm</dt>
+<dt id="arg-parm">parm<a class="anchor" aria-label="anchor" href="#arg-parm"></a></dt>
 <dd><p>A specification of which parameters are to be given confidence
 intervals (a character vector of names). If missing, all parameters are considered.</p></dd>
 
 
-<dt>level</dt>
+<dt id="arg-level">level<a class="anchor" aria-label="anchor" href="#arg-level"></a></dt>
 <dd><p>The confidence level required. The default is <code>0.95</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Gaussian-based confidence intervals (two-sided and equal-tailed) for the
+    <p>Gaussian-based confidence intervals (two-sided and equal-tailed) for the
 fixed effect coefficients based on the confidence level specified by <code>level</code>.
 For <code><a href="ssn_glm.html">ssn_glm()</a></code> objects, confidence intervals are on the link scale.</p>
     </div>
@@ -112,31 +98,31 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                   2.5 %      97.5 %</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 59.18500695 83.46131699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.03485002 -0.02279448</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 63.58249155 98.13318289</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.04270212 -0.02554688</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">ssn_mod</span>, level <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                     5 %        95 %</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 61.13650116 81.50982278</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.03388091 -0.02376358</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 66.35991011 95.35576433</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.04132307 -0.02692593</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/cooks.distance.SSN2.html b/docs/reference/cooks.distance.SSN2.html
index 874320d..b149ec1 100644
--- a/docs/reference/cooks.distance.SSN2.html
+++ b/docs/reference/cooks.distance.SSN2.html
@@ -1,54 +1,40 @@
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+model object."><meta property="og:description" content="Compute the Cook's distance for each observation from a fitted
+model object."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compute Cook's distance</h1>
+
+      <h1>Compute Cook's distance</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/cooks.distance.R" class="external-link"><code>R/cooks.distance.R</code></a></small>
       <div class="d-none name"><code>cooks.distance.SSN2.Rd</code></div>
     </div>
@@ -60,28 +46,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">cooks.distance</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">cooks.distance</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>model</dt>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A vector of Cook's distance values for each observation from the
+    <p>A vector of Cook's distance values for each observation from the
 fitted model object.</p>
     </div>
     <div class="section level2">
@@ -113,40 +99,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">cooks.distance</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>            1            2            3            4            5            6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.352055e-01 1.838183e-02 1.031477e-02 2.394619e-04 6.738905e-06 2.853609e-03 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.647503e-02 1.728298e-03 6.579438e-03 8.927099e-03 1.578560e-02 9.701374e-04 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>            7            8            9           10           11           12 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.444345e-05 7.407903e-05 8.913727e-04 2.902180e-02 7.151814e-02 1.478130e-01 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5.068794e-05 1.432315e-02 6.663274e-03 1.956699e-02 5.615854e-03 3.310957e-04 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           13           14           15           16           17           18 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.325513e-03 8.944452e-02 2.518628e-02 2.855456e-02 4.559303e-02 3.189087e-02 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3.666827e-04 1.131630e-01 3.727255e-02 1.286650e-02 6.826981e-04 6.797500e-05 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           19           20           21           22           23           24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.236705e-03 1.820146e-03 1.613470e-04 2.607051e-04 5.023190e-04 9.990331e-02 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3.876893e-03 1.462109e-04 8.518871e-05 1.666836e-03 2.434997e-03 3.522678e-02 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           25           26           27           28           29           30 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.362150e-03 1.769408e-03 2.791224e-02 2.507513e-03 7.423961e-03 5.168470e-02 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9.185776e-02 1.757621e-04 4.823722e-03 9.509376e-02 1.457562e-02 2.230686e-02 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           31           32           33           34           35           36 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.062191e-04 3.654006e-04 1.628686e-03 2.898565e-03 1.940961e-04 8.450436e-03 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.639139e-02 1.328369e-02 1.305442e-03 1.840481e-03 6.386774e-04 3.336002e-04 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           37           38           39           40           41           42 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5.261079e-05 7.816457e-05 6.103392e-05 9.366436e-03 1.201142e-02 1.870026e-03 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.436665e-04 1.288808e-01 4.569277e-04 1.439454e-03 4.960449e-03 4.691920e-03 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           43           44           45 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5.228889e-03 8.723253e-03 2.611896e-03 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.208746e-02 4.362963e-02 5.795615e-01 </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/docs/reference/copy_lsn_to_temp.html b/docs/reference/copy_lsn_to_temp.html
index aaba1bf..a905072 100644
--- a/docs/reference/copy_lsn_to_temp.html
+++ b/docs/reference/copy_lsn_to_temp.html
@@ -1,56 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Copies the LSN directory MiddleFork04.ssn to R's temporary
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Copy LSN to temporary directory — copy_lsn_to_temp • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Copy LSN to temporary directory — copy_lsn_to_temp"><meta name="description" content="Copies the LSN directory MiddleFork04.ssn to R's temporary
 directory so the examples in SSN2 do not write to the local
-library or any other places."><title>Copy LSN to temporary directory — copy_lsn_to_temp • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Copy LSN to temporary directory — copy_lsn_to_temp"><meta property="og:description" content="Copies the LSN directory MiddleFork04.ssn to R's temporary
+library or any other places."><meta property="og:description" content="Copies the LSN directory MiddleFork04.ssn to R's temporary
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-library or any other places."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Copy LSN to temporary directory</h1>
+
+      <h1>Copy LSN to temporary directory</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/copy_lsn_to_temp.R" class="external-link"><code>R/copy_lsn_to_temp.R</code></a></small>
       <div class="d-none name"><code>copy_lsn_to_temp.Rd</code></div>
     </div>
@@ -68,9 +54,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A copy of MiddleFork04.ssn residing in R's temporary directory</p>
+    <p>A copy of MiddleFork04.ssn residing in R's temporary directory</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -87,23 +71,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># if windows, list temporary directory contents using: shell('dir')</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/covmatrix.SSN2.html b/docs/reference/covmatrix.SSN2.html
index 579ea03..34fe3a2 100644
--- a/docs/reference/covmatrix.SSN2.html
+++ b/docs/reference/covmatrix.SSN2.html
@@ -1,52 +1,38 @@
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Create a covariance matrix</h1>
+
+      <h1>Create a covariance matrix</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/covmatrix.R" class="external-link"><code>R/covmatrix.R</code></a></small>
       <div class="d-none name"><code>covmatrix.SSN2.Rd</code></div>
     </div>
@@ -57,20 +43,22 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/covmatrix.html" class="external-link">covmatrix</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">newdata</span>, <span class="va">cov_type</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/covmatrix.html" class="external-link">covmatrix</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">newdata</span>, <span class="va">cov_type</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object (e.g., <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>).</p></dd>
 
 
-<dt>newdata</dt>
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
 <dd><p>If omitted, the covariance matrix of
 the observed data is returned. If provided, <code>newdata</code> is
 a data frame or <code>sf</code> object that contains coordinate information
@@ -81,7 +69,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>sf</code> object, coordinates are obtained from the geometry of <code>newdata</code>.</p></dd>
 
 
-<dt>cov_type</dt>
+<dt id="arg-cov-type">cov_type<a class="anchor" aria-label="anchor" href="#arg-cov-type"></a></dt>
 <dd><p>The type of covariance matrix returned. If <code>newdata</code>
 is omitted, the \(n \times n\) covariance matrix of the observed
 data is returned, where \(n\) is the sample size used to fit <code>object</code>.
@@ -94,15 +82,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 the \(m \times m\) covariance matrix of the prediction data is returned.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A covariance matrix (see <code>cov_type</code>).</p>
+    <p>A covariance matrix (see <code>cov_type</code>).</p>
     </div>
 
     <div class="section level2">
@@ -121,3577 +107,3623 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/covmatrix.html" class="external-link">covmatrix</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 4.441093 2.171225 2.169448 2.167548 2.166820 2.159888 2.152978 2.152538</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 2.171225 4.441093 4.411857 4.407992 4.406512 4.392414 4.378361 4.377466</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 2.169448 4.411857 4.441093 4.411602 4.410120 4.396010 4.381946 4.381051</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 2.167548 4.407992 4.411602 4.441093 4.413986 4.399864 4.385788 4.384891</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 2.166820 4.406512 4.410120 4.413986 4.441093 4.401342 4.387261 4.386364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 2.159888 4.392414 4.396010 4.399864 4.401342 4.441093 4.401343 4.400443</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 2.152978 4.378361 4.381946 4.385788 4.387261 4.401343 4.441093 4.414567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 2.152538 4.377466 4.381051 4.384891 4.386364 4.400443 4.414567 4.441093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 2.118957 4.309175 4.312703 4.316484 4.317934 4.331794 4.345697 4.346585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 3.841226 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 3.678119 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 3.086145 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 3.077317 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 3.073538 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 3.068528 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 3.004544 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 3.085052 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           [,9]    [,10]    [,11]    [,12]    [,13]    [,14]    [,15]    [,16]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 2.118957 3.841226 3.678119 3.086145 3.077317 3.073538 3.068528 3.004544</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 4.309175 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 4.312703 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 4.316484 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 4.317934 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 4.331794 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 4.345697 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 4.346585 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.000000 4.227979 4.441093 3.704823 3.694225 3.689688 3.683674 3.606863</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.000000 3.547507 3.704823 4.441093 4.402839 4.397432 4.390264 4.298720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.000000 3.537360 3.694225 4.402839 4.441093 4.410047 4.402859 4.311052</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.000000 3.533016 3.689688 4.397432 4.410047 4.441093 4.408272 4.316353</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 0.000000 3.453708 3.606863 4.298720 4.311052 4.316353 4.323400 4.441093</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.945844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.956125</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.954376</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.985768</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.018792</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.225595</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.285782</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.286820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.307138</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 3.348840 3.319002 3.308258 3.305658 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 3.351072 3.321214 3.310462 3.307861 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 3.421392 3.390908 3.379930 3.377274 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 3.430075 3.399513 3.388508 3.385845 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 3.441418 3.410756 3.399714 3.397043 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 4.414503 4.375170 4.361007 4.357580 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 4.441093 4.376128 4.361961 4.358533 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 4.376128 4.441093 4.401176 4.397717 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 4.361961 4.401176 4.441093 4.411999 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 4.358533 4.397717 4.411999 4.441093 0.000000 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 0.000000 0.000000 0.000000 0.000000 4.441093 0.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.000000 0.000000 0.000000 0.000000 0.000000 4.441093</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>            [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 1.4444501 1.3695561 1.3639389 0.5187511 0.5218678 0.7639806 0.8291424</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 1.3695561 1.4444501 1.3845940 0.5110125 0.5140827 0.7525837 0.8167735</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 1.3639389 1.3845940 1.4444501 0.5089166 0.5119742 0.7494970 0.8134235</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 0.5187511 0.5110125 0.5089166 1.4444501 1.3819932 0.9440263 0.8698358</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 0.5218678 0.5140827 0.5119742 1.3819932 1.4444501 0.9496981 0.8750618</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 0.7639806 0.7525837 0.7494970 0.9440263 0.9496981 1.4444501 1.2810337</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 0.8291424 0.8167735 0.8134235 0.8698358 0.8750618 1.2810337 1.4444501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 0.9046265 0.8911314 0.8874765 0.7972548 0.8020447 1.1741414 1.2742869</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 0.0000000 0.0000000 0.0000000 0.6018175 0.6054333 0.8863150 0.9619110</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 0.0000000 0.0000000 0.0000000 0.6006342 0.6042428 0.8845722 0.9600196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.0000000 0.0000000 0.0000000 0.5122583 0.5153359 0.7544183 0.8187646</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.0000000 0.0000000 0.0000000 0.4112291 0.4136998 0.6056296 0.6572853</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.0000000 0.0000000 0.0000000 0.4083319 0.4107852 0.6013628 0.6526546</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>            [,8]      [,9]     [,10]     [,11]     [,12]     [,13]     [,14]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 0.9046265 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 0.8911314 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 0.8874765 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 0.7972548 0.6018175 0.6006342 0.5122583 0.4112291 0.4083319 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 0.8020447 0.6054333 0.6042428 0.5153359 0.4136998 0.4107852 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 1.1741414 0.8863150 0.8845722 0.7544183 0.6056296 0.6013628 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 1.2742869 0.9619110 0.9600196 0.8187646 0.6572853 0.6526546 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 1.4444501 1.0494821 1.0474185 0.8933038 0.7171237 0.7120714 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 1.0494821 1.4444501 1.3875626 1.1833999 0.9500061 0.9433131 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 1.0474185 1.3875626 1.4444501 1.1857314 0.9518777 0.9451716 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.8933038 1.1833999 1.1857314 1.4444501 1.1160977 1.1082346 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.7171237 0.9500061 0.9518777 1.1160977 1.4444501 1.3805014 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.7120714 0.9433131 0.9451716 1.1082346 1.3805014 1.4444501 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.4444501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7600376</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7544633</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7485414</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7462835</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7123108</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6854196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6841607</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5799626</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.1538975</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.8382513</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6655399</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6634222</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6486203</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6414849</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6321428</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5157278</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5031842</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4399735</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           [,15]     [,16]     [,17]     [,18]     [,19]     [,20]     [,21]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.7600376 0.7544633 0.7485414 0.7462835 0.7123108 0.6854196 0.6841607</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 1.4444501 1.3800996 1.3692669 1.3651367 1.3029922 1.2538016 1.2514986</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 1.3800996 1.4444501 1.3793836 1.3752228 1.3126192 1.2630651 1.2607452</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17,] 1.3692669 1.3793836 1.4444501 1.3861026 1.3230038 1.2730576 1.2707193</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18,] 1.3651367 1.3752228 1.3861026 1.4444501 1.3270065 1.2769093 1.2745639</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 1.3029922 1.3126192 1.3230038 1.3270065 1.4444501 1.3378097 1.3353525</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 1.2538016 1.2630651 1.2730576 1.2769093 1.3378097 1.4444501 1.3877427</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 1.2514986 1.2607452 1.2707193 1.2745639 1.3353525 1.3877427 1.4444501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 1.0608946 1.0687329 1.0771880 1.0804470 1.1319775 1.1763886 1.1785533</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 0.8048201 0.8107665 0.8171807 0.8196531 0.8587453 0.8924366 0.8940788</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           [,22]     [,23]     [,24]     [,25]     [,26]     [,27]     [,28]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.5799626 1.1538975 0.8382513 0.6655399 0.6634222 0.6486203 0.6414849</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 1.0608946 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 1.0687329 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17,] 1.0771880 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18,] 1.0804470 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 1.1319775 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 1.1763886 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 1.1785533 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 1.4444501 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.0000000 1.4444501 1.0099837 0.8018889 0.7993374 0.7815031 0.7729059</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 0.0000000 1.0099837 1.4444501 1.1038428 1.1003305 1.0757806 1.0639461</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 0.0000000 0.8018889 1.1038428 1.4444501 1.3858726 1.3549518 1.3400461</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 0.0000000 0.7993374 1.1003305 1.3858726 1.4444501 1.3592768 1.3443235</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 0.0000000 0.7815031 1.0757806 1.3549518 1.3592768 1.4444501 1.3750018</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 0.0000000 0.7729059 1.0639461 1.3400461 1.3443235 1.3750018 1.4444501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 0.0000000 0.7616498 1.0484515 1.3205306 1.3247457 1.3549772 1.3700490</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 0.0000000 0.6213848 0.8553693 1.0773425 1.0807814 1.1054455 1.1177416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 0.0000000 0.6062715 0.8345649 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 1.0547121 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           [,29]     [,30]     [,31]     [,32]     [,33]     [,34]     [,35]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.6321428 0.5157278 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.7616498 0.6213848 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 1.0484515 0.8553693 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 1.3205306 1.0773425 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 1.3247457 1.0807814 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 1.3549772 1.1054455 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 1.3700490 1.1177416 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 1.4444501 1.1342602 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 1.1342602 1.4444501 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 0.0000000 0.0000000 1.4444501 1.3834966 1.3055425 1.2591504 1.2658696</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 0.0000000 0.0000000 1.3834966 1.4444501 1.3119591 1.2653390 1.2720912</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 0.0000000 0.0000000 1.3055425 1.3119591 1.4444501 1.3408925 1.3480479</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 0.0000000 0.0000000 1.2591504 1.2653390 1.3408925 1.4444501 1.3829167</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 0.0000000 0.0000000 1.2658696 1.2720912 1.3480479 1.3829167 1.4444501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 0.0000000 0.0000000 1.1822100 1.1880204 1.2589572 1.3053422 1.2984135</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 0.0000000 0.0000000 1.0540673 1.0592479 1.1224957 1.1638529 1.1576752</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 0.0000000 0.0000000 0.9019153 0.9063481 0.9604662 0.9958536 0.9905677</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 0.0000000 0.0000000 0.7412357 0.7448788 0.7893555 0.8184385 0.8140943</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.0000000 0.0000000 0.7391329 0.7427656 0.7871162 0.8161167 0.8117848</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.0000000 0.0000000 0.6543039 0.6575197 0.6967803 0.7224524 0.7186177</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.0000000 0.0000000 0.5398135 0.5424666 0.5748574 0.5960374 0.5928737</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.0000000 0.0000000 0.5010885 0.5035513 0.5336184 0.5532790 0.5503422</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           [,36]     [,37]     [,38]     [,39]     [,40]     [,41]     [,42]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 1.1822100 1.0540673 0.9019153 0.7412357 0.7391329 0.6543039 0.5398135</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 1.1880204 1.0592479 0.9063481 0.7448788 0.7427656 0.6575197 0.5424666</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 1.2589572 1.1224957 0.9604662 0.7893555 0.7871162 0.6967803 0.5748574</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 1.3053422 1.1638529 0.9958536 0.8184385 0.8161167 0.7224524 0.5960374</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 1.2984135 1.1576752 0.9905677 0.8140943 0.8117848 0.7186177 0.5928737</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 1.4444501 1.2395986 1.0606656 0.8717040 0.8692311 0.7694710 0.6348286</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 1.2395986 1.4444501 1.1896105 0.9776769 0.9749033 0.8630153 0.7120046</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 1.0606656 1.1896105 1.4444501 1.1426097 1.1393682 1.0086049 0.8321188</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 0.8717040 0.9776769 1.1426097 1.4444501 1.3863521 1.2272429 1.0124995</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.8692311 0.9749033 1.1393682 1.3863521 1.4444501 1.2307344 1.0153800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.7694710 0.8630153 1.0086049 1.2272429 1.2307344 1.4444501 1.1470217</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.6348286 0.7120046 0.8321188 1.0124995 1.0153800 1.1470217 1.4444501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.5892874 0.6609269 0.7724245 0.0000000 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           [,43]     [,44]     [,45]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14,] 0.5031842 0.4399735 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15,] 0.0000000 0.8048201 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16,] 0.0000000 0.8107665 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17,] 0.0000000 0.8171807 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18,] 0.0000000 0.8196531 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19,] 0.0000000 0.8587453 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20,] 0.0000000 0.8924366 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21,] 0.0000000 0.8940788 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22,] 0.0000000 1.0547121 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23,] 0.6062715 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24,] 0.8345649 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31,] 0.0000000 0.0000000 0.5010885</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32,] 0.0000000 0.0000000 0.5035513</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33,] 0.0000000 0.0000000 0.5336184</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34,] 0.0000000 0.0000000 0.5532790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35,] 0.0000000 0.0000000 0.5503422</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36,] 0.0000000 0.0000000 0.5892874</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37,] 0.0000000 0.0000000 0.6609269</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38,] 0.0000000 0.0000000 0.7724245</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42,] 0.0000000 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43,] 1.4444501 0.0000000 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44,] 0.0000000 1.4444501 0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45,] 0.0000000 0.0000000 1.4444501</span>
 <span class="r-in"><span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/covmatrix.html" class="external-link">covmatrix</a></span><span class="op">(</span><span class="va">ssn_mod</span>, <span class="st">"CapeHorn"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1,] 2.165766 4.404367 4.407973 4.411838 4.413320 4.403486 4.389398 4.388501</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [2,] 2.165784 4.404404 4.408011 4.411875 4.413357 4.403448 4.389361 4.388463</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [3,] 2.165802 4.404442 4.408049 4.411913 4.413395 4.403411 4.389323 4.388426</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [4,] 2.165821 4.404479 4.408086 4.411950 4.413433 4.403373 4.389286 4.388389</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [5,] 2.165839 4.404517 4.408124 4.411988 4.413470 4.403336 4.389248 4.388351</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [6,] 2.165858 4.404554 4.408161 4.412025 4.413508 4.403299 4.389211 4.388314</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [7,] 2.165876 4.404592 4.408199 4.412063 4.413545 4.403261 4.389174 4.388277</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [8,] 2.165895 4.404629 4.408236 4.412101 4.413583 4.403224 4.389136 4.388239</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [9,] 2.165913 4.404667 4.408274 4.412138 4.413620 4.403186 4.389099 4.388202</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [10,] 2.165931 4.404704 4.408311 4.412176 4.413658 4.403149 4.389062 4.388164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [11,] 2.165950 4.404742 4.408349 4.412213 4.413696 4.403111 4.389024 4.388127</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [12,] 2.165968 4.404779 4.408386 4.412251 4.413733 4.403074 4.388987 4.388090</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [13,] 2.165987 4.404817 4.408424 4.412288 4.413771 4.403036 4.388949 4.388052</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [14,] 2.166005 4.404854 4.408461 4.412326 4.413808 4.402999 4.388912 4.388015</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [15,] 2.166024 4.404892 4.408499 4.412363 4.413846 4.402961 4.388875 4.387978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [16,] 2.166042 4.404929 4.408536 4.412401 4.413883 4.402924 4.388837 4.387940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [17,] 2.166061 4.404967 4.408574 4.412439 4.413921 4.402886 4.388800 4.387903</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [18,] 2.166079 4.405004 4.408611 4.412476 4.413959 4.402849 4.388763 4.387866</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [19,] 2.166097 4.405042 4.408649 4.412514 4.413996 4.402811 4.388725 4.387828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [20,] 2.166116 4.405079 4.408687 4.412551 4.414034 4.402774 4.388688 4.387791</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [21,] 2.166134 4.405117 4.408724 4.412589 4.414071 4.402736 4.388651 4.387753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [22,] 2.166153 4.405154 4.408762 4.412626 4.414109 4.402699 4.388613 4.387716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [23,] 2.166171 4.405192 4.408799 4.412664 4.414147 4.402661 4.388576 4.387679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [24,] 2.166190 4.405229 4.408837 4.412702 4.414184 4.402624 4.388538 4.387641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [25,] 2.166208 4.405267 4.408874 4.412739 4.414222 4.402586 4.388501 4.387604</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [26,] 2.166227 4.405304 4.408912 4.412777 4.414259 4.402549 4.388464 4.387567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [27,] 2.166245 4.405342 4.408949 4.412814 4.414297 4.402511 4.388426 4.387529</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [28,] 2.166263 4.405379 4.408987 4.412852 4.414334 4.402474 4.388389 4.387492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [29,] 2.166282 4.405417 4.409024 4.412889 4.414372 4.402436 4.388352 4.387455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [30,] 2.166300 4.405454 4.409062 4.412927 4.414410 4.402399 4.388314 4.387417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [31,] 2.166319 4.405492 4.409099 4.412965 4.414447 4.402361 4.388277 4.387380</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [32,] 2.166337 4.405529 4.409137 4.413002 4.414485 4.402324 4.388240 4.387343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [33,] 2.166356 4.405567 4.409175 4.413040 4.414522 4.402286 4.388202 4.387305</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [34,] 2.166374 4.405604 4.409212 4.413077 4.414560 4.402249 4.388165 4.387268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [35,] 2.166393 4.405642 4.409250 4.413115 4.414598 4.402211 4.388127 4.387231</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [36,] 2.166411 4.405679 4.409287 4.413152 4.414635 4.402174 4.388090 4.387193</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [37,] 2.166429 4.405717 4.409325 4.413190 4.414673 4.402136 4.388053 4.387156</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [38,] 2.166448 4.405755 4.409362 4.413228 4.414710 4.402099 4.388015 4.387118</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [39,] 2.166466 4.405792 4.409400 4.413265 4.414748 4.402062 4.387978 4.387081</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [40,] 2.166485 4.405830 4.409437 4.413303 4.414785 4.402024 4.387941 4.387044</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [41,] 2.166503 4.405867 4.409475 4.413340 4.414823 4.401987 4.387903 4.387006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [42,] 2.166522 4.405905 4.409512 4.413378 4.414861 4.401949 4.387866 4.386969</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [43,] 2.166540 4.405942 4.409550 4.413415 4.414898 4.401912 4.387829 4.386932</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [44,] 2.166559 4.405980 4.409588 4.413453 4.414936 4.401874 4.387791 4.386894</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [45,] 2.166577 4.406017 4.409625 4.413491 4.414973 4.401837 4.387754 4.386857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [46,] 2.166595 4.406055 4.409663 4.413528 4.415011 4.401799 4.387717 4.386820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [47,] 2.166614 4.406092 4.409700 4.413566 4.415049 4.401762 4.387679 4.386782</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [48,] 2.166632 4.406130 4.409738 4.413603 4.415086 4.401724 4.387642 4.386745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [49,] 2.166651 4.406167 4.409775 4.413641 4.415124 4.401687 4.387604 4.386708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [50,] 2.166669 4.406205 4.409813 4.413679 4.415161 4.401649 4.387567 4.386670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [51,] 2.166688 4.406242 4.409850 4.413716 4.415199 4.401612 4.387530 4.386633</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [52,] 2.166706 4.406280 4.409888 4.413754 4.415237 4.401574 4.387492 4.386596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [53,] 2.166725 4.406317 4.409925 4.413791 4.415274 4.401537 4.387455 4.386558</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [54,] 2.166743 4.406355 4.409963 4.413829 4.415312 4.401499 4.387418 4.386521</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [55,] 2.166761 4.406392 4.410001 4.413866 4.415349 4.401462 4.387380 4.386484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [56,] 2.166780 4.406430 4.410038 4.413904 4.415387 4.401424 4.387343 4.386446</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [57,] 2.166798 4.406467 4.410076 4.413942 4.415425 4.401387 4.387306 4.386409</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [58,] 2.166817 4.406505 4.410113 4.413979 4.415462 4.401349 4.387268 4.386371</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [59,] 2.166835 4.406542 4.410151 4.414017 4.415439 4.401312 4.387231 4.386334</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [60,] 2.166854 4.406580 4.410188 4.414054 4.415401 4.401275 4.387194 4.386297</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [61,] 2.166872 4.406617 4.410226 4.414092 4.415364 4.401237 4.387156 4.386259</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [62,] 2.166891 4.406655 4.410263 4.414129 4.415326 4.401200 4.387119 4.386222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [63,] 2.166909 4.406692 4.410301 4.414167 4.415289 4.401162 4.387082 4.386185</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [64,] 2.166927 4.406730 4.410338 4.414205 4.415251 4.401125 4.387044 4.386147</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [65,] 2.166946 4.406767 4.410376 4.414242 4.415213 4.401087 4.387007 4.386110</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [66,] 2.166964 4.406805 4.410414 4.414280 4.415176 4.401050 4.386969 4.386073</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [67,] 2.166983 4.406842 4.410451 4.414317 4.415138 4.401012 4.386932 4.386035</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [68,] 2.167001 4.406880 4.410489 4.414355 4.415101 4.400975 4.386895 4.385998</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [69,] 2.167020 4.406917 4.410526 4.414393 4.415063 4.400937 4.386857 4.385961</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [70,] 2.167038 4.406955 4.410564 4.414430 4.415026 4.400900 4.386820 4.385923</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [71,] 2.167057 4.406993 4.410601 4.414468 4.414988 4.400862 4.386783 4.385886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [72,] 2.167075 4.407030 4.410639 4.414505 4.414950 4.400825 4.386745 4.385849</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [73,] 2.167094 4.407068 4.410676 4.414543 4.414913 4.400787 4.386708 4.385811</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [74,] 2.167112 4.407105 4.410714 4.414580 4.414875 4.400750 4.386671 4.385774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [75,] 2.167130 4.407143 4.410752 4.414618 4.414838 4.400712 4.386633 4.385737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76,] 2.167149 4.407180 4.410789 4.414656 4.414800 4.400675 4.386596 4.385699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [77,] 2.167167 4.407218 4.410827 4.414693 4.414762 4.400638 4.386559 4.385662</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [78,] 2.167186 4.407255 4.410864 4.414731 4.414725 4.400600 4.386521 4.385625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [79,] 2.167204 4.407293 4.410902 4.414768 4.414687 4.400563 4.386484 4.385587</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [80,] 2.167223 4.407330 4.410939 4.414806 4.414650 4.400525 4.386447 4.385550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [81,] 2.167241 4.407368 4.410977 4.414844 4.414612 4.400488 4.386409 4.385513</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [82,] 2.167260 4.407405 4.411014 4.414881 4.414574 4.400450 4.386372 4.385475</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [83,] 2.167278 4.407443 4.411052 4.414919 4.414537 4.400413 4.386335 4.385438</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [84,] 2.167297 4.407480 4.411089 4.414956 4.414499 4.400375 4.386297 4.385401</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [85,] 2.167315 4.407518 4.411127 4.414994 4.414462 4.400338 4.386260 4.385363</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [86,] 2.167333 4.407555 4.411165 4.415032 4.414424 4.400300 4.386223 4.385326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [87,] 2.167352 4.407593 4.411202 4.415069 4.414387 4.400263 4.386185 4.385289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [88,] 2.167370 4.407630 4.411240 4.415107 4.414349 4.400225 4.386148 4.385251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [89,] 2.167389 4.407668 4.411277 4.415144 4.414311 4.400188 4.386110 4.385214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [90,] 2.167407 4.407705 4.411315 4.415182 4.414274 4.400151 4.386073 4.385177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [91,] 2.167426 4.407743 4.411352 4.415219 4.414236 4.400113 4.386036 4.385139</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [92,] 2.167444 4.407781 4.411390 4.415257 4.414199 4.400076 4.385998 4.385102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [93,] 2.167463 4.407818 4.411428 4.415295 4.414161 4.400038 4.385961 4.385065</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [94,] 2.167481 4.407856 4.411465 4.415332 4.414123 4.400001 4.385924 4.385027</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [95,] 2.167499 4.407893 4.411503 4.415370 4.414086 4.399963 4.385886 4.384990</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [96,] 2.167518 4.407931 4.411540 4.415407 4.414048 4.399926 4.385849 4.384953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [97,] 2.167536 4.407968 4.411578 4.415445 4.414011 4.399888 4.385812 4.384915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [98,] 2.167555 4.408006 4.411615 4.415456 4.413973 4.399851 4.385774 4.384878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [99,] 2.167573 4.408043 4.411653 4.415418 4.413936 4.399813 4.385737 4.384841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [100,] 2.167592 4.408081 4.411690 4.415381 4.413898 4.399776 4.385700 4.384803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [101,] 2.167610 4.408118 4.411728 4.415343 4.413860 4.399738 4.385662 4.384766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [102,] 2.167629 4.408156 4.411766 4.415306 4.413823 4.399701 4.385625 4.384729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [103,] 2.167647 4.408193 4.411803 4.415268 4.413785 4.399664 4.385588 4.384691</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [104,] 2.167666 4.408231 4.411841 4.415231 4.413748 4.399626 4.385550 4.384654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [105,] 2.167684 4.408268 4.411878 4.415193 4.413710 4.399589 4.385513 4.384617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [106,] 2.167702 4.408306 4.411916 4.415155 4.413672 4.399551 4.385476 4.384579</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [107,] 2.167721 4.408343 4.411953 4.415118 4.413635 4.399514 4.385438 4.384542</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [108,] 2.167739 4.408381 4.411991 4.415080 4.413597 4.399476 4.385401 4.384505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [109,] 2.167758 4.408419 4.412028 4.415043 4.413560 4.399439 4.385364 4.384467</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [110,] 2.167776 4.408456 4.412066 4.415005 4.413522 4.399401 4.385326 4.384430</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [111,] 2.167795 4.408494 4.412104 4.414967 4.413485 4.399364 4.385289 4.384393</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [112,] 2.167813 4.408531 4.412141 4.414930 4.413447 4.399326 4.385252 4.384355</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [113,] 2.167832 4.408569 4.412179 4.414892 4.413409 4.399289 4.385214 4.384318</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [114,] 2.167850 4.408606 4.412216 4.414855 4.413372 4.399251 4.385177 4.384281</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [115,] 2.167869 4.408644 4.412254 4.414817 4.413334 4.399214 4.385140 4.384243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [116,] 2.167887 4.408681 4.412291 4.414779 4.413297 4.399177 4.385102 4.384206</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [117,] 2.167906 4.408719 4.412329 4.414742 4.413259 4.399139 4.385065 4.384169</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [118,] 2.167924 4.408756 4.412367 4.414704 4.413222 4.399102 4.385028 4.384131</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [119,] 2.167942 4.408794 4.412404 4.414667 4.413184 4.399064 4.384990 4.384094</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [120,] 2.167961 4.408831 4.412442 4.414629 4.413146 4.399027 4.384953 4.384057</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [121,] 2.167979 4.408869 4.412479 4.414592 4.413109 4.398989 4.384916 4.384019</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [122,] 2.167998 4.408907 4.412517 4.414554 4.413071 4.398952 4.384878 4.383982</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [123,] 2.168016 4.408944 4.412554 4.414516 4.413034 4.398914 4.384841 4.383945</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [124,] 2.168035 4.408982 4.412592 4.414479 4.412996 4.398877 4.384804 4.383907</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [125,] 2.168053 4.409019 4.412630 4.414441 4.412959 4.398840 4.384766 4.383870</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [126,] 2.168072 4.409057 4.412667 4.414404 4.412921 4.398802 4.384729 4.383833</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [127,] 2.168090 4.409094 4.412705 4.414366 4.412883 4.398765 4.384692 4.383795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [128,] 2.168109 4.409132 4.412742 4.414328 4.412846 4.398727 4.384654 4.383758</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [129,] 2.168127 4.409169 4.412780 4.414291 4.412808 4.398690 4.384617 4.383721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [130,] 2.168145 4.409207 4.412817 4.414253 4.412771 4.398652 4.384580 4.383683</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [131,] 2.168164 4.409244 4.412855 4.414216 4.412733 4.398615 4.384542 4.383646</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [132,] 2.168182 4.409282 4.412893 4.414178 4.412696 4.398577 4.384505 4.383609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [133,] 2.168201 4.409319 4.412930 4.414141 4.412658 4.398540 4.384468 4.383571</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [134,] 2.168219 4.409357 4.412968 4.414103 4.412620 4.398502 4.384430 4.383534</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [135,] 2.168238 4.409395 4.413005 4.414065 4.412583 4.398465 4.384393 4.383497</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [136,] 2.168256 4.409432 4.413043 4.414028 4.412545 4.398428 4.384356 4.383460</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [137,] 2.168275 4.409470 4.413080 4.413990 4.412508 4.398390 4.384318 4.383422</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [138,] 2.168293 4.409507 4.413118 4.413953 4.412470 4.398353 4.384281 4.383385</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [139,] 2.168312 4.409545 4.413156 4.413915 4.412433 4.398315 4.384244 4.383348</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [140,] 2.168330 4.409582 4.413193 4.413877 4.412395 4.398278 4.384206 4.383310</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [141,] 2.168349 4.409620 4.413231 4.413840 4.412357 4.398240 4.384169 4.383273</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [142,] 2.168367 4.409657 4.413268 4.413802 4.412320 4.398203 4.384132 4.383236</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [143,] 2.168385 4.409695 4.413306 4.413765 4.412282 4.398165 4.384094 4.383198</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [144,] 2.168404 4.409732 4.413343 4.413727 4.412245 4.398128 4.384057 4.383161</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [145,] 2.168422 4.409770 4.413381 4.413690 4.412207 4.398091 4.384020 4.383124</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [146,] 2.168441 4.409808 4.413419 4.413652 4.412170 4.398053 4.383982 4.383086</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [147,] 2.168459 4.409845 4.413456 4.413614 4.412132 4.398016 4.383945 4.383049</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [148,] 2.168478 4.409883 4.413494 4.413577 4.412095 4.397978 4.383908 4.383012</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [149,] 2.168496 4.409920 4.413531 4.413539 4.412057 4.397941 4.383870 4.382974</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [150,] 2.168515 4.409958 4.413569 4.413502 4.412019 4.397903 4.383833 4.382937</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [151,] 2.168533 4.409995 4.413606 4.413464 4.411982 4.397866 4.383796 4.382900</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [152,] 2.168552 4.410033 4.413644 4.413427 4.411944 4.397828 4.383758 4.382862</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [153,] 2.168570 4.410070 4.413682 4.413389 4.411907 4.397791 4.383721 4.382825</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [154,] 2.168589 4.410108 4.413719 4.413351 4.411869 4.397754 4.383684 4.382788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [155,] 2.168607 4.410145 4.413757 4.413314 4.411832 4.397716 4.383647 4.382750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [156,] 2.168625 4.410183 4.413794 4.413276 4.411794 4.397679 4.383609 4.382713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [157,] 2.168644 4.410221 4.413832 4.413239 4.411756 4.397641 4.383572 4.382676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [158,] 2.168662 4.410258 4.413870 4.413201 4.411719 4.397604 4.383535 4.382639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [159,] 2.168681 4.410296 4.413907 4.413164 4.411681 4.397566 4.383497 4.382601</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [160,] 2.168699 4.410333 4.413945 4.413126 4.411644 4.397529 4.383460 4.382564</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [161,] 2.168718 4.410371 4.413982 4.413088 4.411606 4.397491 4.383423 4.382527</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [162,] 2.168736 4.410408 4.414020 4.413051 4.411569 4.397454 4.383385 4.382489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [163,] 2.168755 4.410446 4.414057 4.413013 4.411531 4.397417 4.383348 4.382452</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [164,] 2.168773 4.410483 4.414095 4.412976 4.411494 4.397379 4.383311 4.382415</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [165,] 2.168792 4.410521 4.414133 4.412938 4.411456 4.397342 4.383273 4.382377</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [166,] 2.168810 4.410558 4.414170 4.412900 4.411418 4.397304 4.383236 4.382340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [167,] 2.168829 4.410596 4.414208 4.412863 4.411381 4.397267 4.383199 4.382303</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [168,] 2.168847 4.410634 4.414245 4.412825 4.411343 4.397229 4.383161 4.382265</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [169,] 2.168866 4.410671 4.414283 4.412788 4.411306 4.397192 4.383124 4.382228</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [170,] 2.168884 4.410709 4.414320 4.412750 4.411268 4.397155 4.383087 4.382191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [171,] 2.168902 4.410746 4.414358 4.412713 4.411231 4.397117 4.383049 4.382153</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [172,] 2.168921 4.410784 4.414396 4.412675 4.411193 4.397080 4.383012 4.382116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [173,] 2.168939 4.410821 4.414433 4.412638 4.411155 4.397042 4.382975 4.382079</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [174,] 2.168958 4.410859 4.414471 4.412600 4.411118 4.397005 4.382937 4.382042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [175,] 2.168976 4.410896 4.414508 4.412562 4.411080 4.396967 4.382900 4.382004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [176,] 2.168995 4.410934 4.414546 4.412525 4.411043 4.396930 4.382863 4.381967</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [177,] 2.169013 4.410972 4.414584 4.412487 4.411005 4.396892 4.382825 4.381930</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [178,] 2.169032 4.411009 4.414621 4.412450 4.410968 4.396855 4.382788 4.381892</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [179,] 2.169050 4.411047 4.414659 4.412412 4.410930 4.396818 4.382751 4.381855</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [180,] 2.169069 4.411084 4.414696 4.412375 4.410893 4.396780 4.382714 4.381818</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [181,] 2.169087 4.411122 4.414734 4.412337 4.410855 4.396743 4.382676 4.381780</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [182,] 2.169106 4.411159 4.414772 4.412299 4.410818 4.396705 4.382639 4.381743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [183,] 2.169124 4.411197 4.414809 4.412262 4.410780 4.396668 4.382602 4.381706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [184,] 2.169143 4.411234 4.414847 4.412224 4.410742 4.396630 4.382564 4.381668</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [185,] 2.169161 4.411272 4.414884 4.412187 4.410705 4.396593 4.382527 4.381631</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [186,] 2.169179 4.411310 4.414922 4.412149 4.410667 4.396556 4.382490 4.381594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [187,] 2.169198 4.411347 4.414959 4.412112 4.410630 4.396518 4.382452 4.381557</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [188,] 2.169216 4.411385 4.414997 4.412074 4.410592 4.396481 4.382415 4.381519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [189,] 2.169235 4.411422 4.415035 4.412036 4.410555 4.396443 4.382378 4.381482</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [190,] 2.169253 4.411460 4.415072 4.411999 4.410517 4.396406 4.382340 4.381445</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [191,] 2.169272 4.411497 4.415110 4.411961 4.410480 4.396368 4.382303 4.381407</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [192,] 2.169290 4.411535 4.415147 4.411924 4.410442 4.396331 4.382266 4.381370</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [193,] 2.169309 4.411572 4.415185 4.411886 4.410404 4.396294 4.382228 4.381333</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [194,] 2.169327 4.411610 4.415223 4.411849 4.410367 4.396256 4.382191 4.381295</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [195,] 2.169346 4.411648 4.415260 4.411811 4.410329 4.396219 4.382154 4.381258</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [196,] 2.169364 4.411685 4.415298 4.411774 4.410292 4.396181 4.382117 4.381221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [197,] 2.169383 4.411723 4.415335 4.411736 4.410254 4.396144 4.382079 4.381184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [198,] 2.169401 4.411760 4.415373 4.411698 4.410217 4.396106 4.382042 4.381146</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [199,] 2.169420 4.411798 4.415411 4.411661 4.410179 4.396069 4.382005 4.381109</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [200,] 2.169438 4.411835 4.415448 4.411623 4.410142 4.396032 4.381967 4.381072</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [201,] 2.169456 4.411873 4.415453 4.411586 4.410104 4.395994 4.381930 4.381034</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [202,] 2.169475 4.411911 4.415415 4.411548 4.410066 4.395957 4.381893 4.380997</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [203,] 2.169493 4.411948 4.415378 4.411511 4.410029 4.395919 4.381855 4.380960</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [204,] 2.169512 4.411986 4.415340 4.411473 4.409991 4.395882 4.381818 4.380922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [205,] 2.169530 4.412023 4.415303 4.411435 4.409954 4.395844 4.381781 4.380885</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [206,] 2.169549 4.412061 4.415265 4.411398 4.409916 4.395807 4.381743 4.380848</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [207,] 2.169567 4.412098 4.415227 4.411360 4.409879 4.395770 4.381706 4.380811</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [208,] 2.169586 4.412136 4.415190 4.411323 4.409841 4.395732 4.381669 4.380773</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [209,] 2.169604 4.412173 4.415152 4.411285 4.409804 4.395695 4.381632 4.380736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [210,] 2.169623 4.412211 4.415115 4.411248 4.409766 4.395657 4.381594 4.380699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [211,] 2.169641 4.412249 4.415077 4.411210 4.409729 4.395620 4.381557 4.380661</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [212,] 2.169660 4.412286 4.415039 4.411173 4.409691 4.395582 4.381520 4.380624</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [213,] 2.169678 4.412324 4.415002 4.411135 4.409653 4.395545 4.381482 4.380587</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [214,] 2.169697 4.412361 4.414964 4.411097 4.409616 4.395508 4.381445 4.380549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [215,] 2.169715 4.412399 4.414927 4.411060 4.409578 4.395470 4.381408 4.380512</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [216,] 2.169734 4.412436 4.414889 4.411022 4.409541 4.395433 4.381370 4.380475</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [217,] 2.169752 4.412474 4.414852 4.410985 4.409503 4.395395 4.381333 4.380438</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [218,] 2.169771 4.412512 4.414814 4.410947 4.409466 4.395358 4.381296 4.380400</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [219,] 2.169789 4.412549 4.414776 4.410910 4.409428 4.395320 4.381259 4.380363</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [220,] 2.169807 4.412587 4.414739 4.410872 4.409391 4.395283 4.381221 4.380326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [221,] 2.169826 4.412624 4.414701 4.410835 4.409353 4.395246 4.381184 4.380288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [222,] 2.169844 4.412662 4.414664 4.410797 4.409316 4.395208 4.381147 4.380251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [223,] 2.169863 4.412699 4.414626 4.410759 4.409278 4.395171 4.381109 4.380214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [224,] 2.169881 4.412737 4.414588 4.410722 4.409241 4.395133 4.381072 4.380177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [225,] 2.169900 4.412775 4.414551 4.410684 4.409203 4.395096 4.381035 4.380139</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [226,] 2.169918 4.412812 4.414513 4.410647 4.409165 4.395059 4.380997 4.380102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [227,] 2.169937 4.412850 4.414476 4.410609 4.409128 4.395021 4.380960 4.380065</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [228,] 2.169955 4.412887 4.414438 4.410572 4.409090 4.394984 4.380923 4.380027</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [229,] 2.169974 4.412925 4.414401 4.410534 4.409053 4.394946 4.380886 4.379990</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [230,] 2.169992 4.412962 4.414363 4.410497 4.409015 4.394909 4.380848 4.379953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [231,] 2.170011 4.413000 4.414325 4.410459 4.408978 4.394871 4.380811 4.379915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [232,] 2.170029 4.413038 4.414288 4.410421 4.408940 4.394834 4.380774 4.379878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [233,] 2.170048 4.413075 4.414250 4.410384 4.408903 4.394797 4.380736 4.379841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [234,] 2.170066 4.413113 4.414213 4.410346 4.408865 4.394759 4.380699 4.379804</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [235,] 2.170085 4.413150 4.414175 4.410309 4.408828 4.394722 4.380662 4.379766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [236,] 2.170103 4.413188 4.414137 4.410271 4.408790 4.394684 4.380624 4.379729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [237,] 2.170122 4.413225 4.414100 4.410234 4.408753 4.394647 4.380587 4.379692</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [238,] 2.170140 4.413263 4.414062 4.410196 4.408715 4.394610 4.380550 4.379654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [239,] 2.170158 4.413301 4.414025 4.410159 4.408677 4.394572 4.380513 4.379617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [240,] 2.170177 4.413338 4.413987 4.410121 4.408640 4.394535 4.380475 4.379580</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [241,] 2.170195 4.413376 4.413950 4.410084 4.408602 4.394497 4.380438 4.379543</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [242,] 2.170214 4.413413 4.413912 4.410046 4.408565 4.394460 4.380401 4.379505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [243,] 2.170232 4.413451 4.413874 4.410008 4.408527 4.394422 4.380363 4.379468</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [244,] 2.170251 4.413488 4.413837 4.409971 4.408490 4.394385 4.380326 4.379431</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [245,] 2.170269 4.413526 4.413799 4.409933 4.408452 4.394348 4.380289 4.379393</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [246,] 2.170288 4.413564 4.413762 4.409896 4.408415 4.394310 4.380252 4.379356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [247,] 2.170306 4.413601 4.413724 4.409858 4.408377 4.394273 4.380214 4.379319</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [248,] 2.170325 4.413639 4.413686 4.409821 4.408340 4.394235 4.380177 4.379282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [249,] 2.170343 4.413676 4.413649 4.409783 4.408302 4.394198 4.380140 4.379244</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [250,] 2.170362 4.413714 4.413611 4.409746 4.408265 4.394161 4.380102 4.379207</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [251,] 2.170380 4.413751 4.413574 4.409708 4.408227 4.394123 4.380065 4.379170</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [252,] 2.170399 4.413789 4.413536 4.409671 4.408190 4.394086 4.380028 4.379132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [253,] 2.170417 4.413827 4.413499 4.409633 4.408152 4.394048 4.379990 4.379095</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [254,] 2.170436 4.413864 4.413461 4.409595 4.408114 4.394011 4.379953 4.379058</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [255,] 2.170454 4.413902 4.413423 4.409558 4.408077 4.393973 4.379916 4.379021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [256,] 2.170473 4.413939 4.413386 4.409520 4.408039 4.393936 4.379879 4.378983</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [257,] 2.170491 4.413977 4.413348 4.409483 4.408002 4.393899 4.379841 4.378946</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [258,] 2.170510 4.414015 4.413311 4.409445 4.407964 4.393861 4.379804 4.378909</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [259,] 2.170528 4.414052 4.413273 4.409408 4.407927 4.393824 4.379767 4.378871</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [260,] 2.170547 4.414090 4.413236 4.409370 4.407889 4.393786 4.379729 4.378834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [261,] 2.170565 4.414127 4.413198 4.409333 4.407852 4.393749 4.379692 4.378797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [262,] 2.170584 4.414165 4.413160 4.409295 4.407814 4.393712 4.379655 4.378760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [263,] 2.170602 4.414202 4.413123 4.409258 4.407777 4.393674 4.379618 4.378722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [264,] 2.170620 4.414240 4.413085 4.409220 4.407739 4.393637 4.379580 4.378685</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [265,] 2.170639 4.414278 4.413048 4.409183 4.407702 4.393599 4.379543 4.378648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [266,] 2.170657 4.414315 4.413010 4.409145 4.407664 4.393562 4.379506 4.378611</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [267,] 2.170676 4.414353 4.412973 4.409107 4.407627 4.393525 4.379468 4.378573</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [268,] 2.170694 4.414390 4.412935 4.409070 4.407589 4.393487 4.379431 4.378536</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [269,] 2.170713 4.414428 4.412897 4.409032 4.407552 4.393450 4.379394 4.378499</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [270,] 2.170731 4.414466 4.412860 4.408995 4.407514 4.393412 4.379357 4.378461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [271,] 2.170750 4.414503 4.412822 4.408957 4.407476 4.393375 4.379319 4.378424</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [272,] 2.170768 4.414541 4.412785 4.408920 4.407439 4.393338 4.379282 4.378387</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [273,] 2.170787 4.414578 4.412747 4.408882 4.407401 4.393300 4.379245 4.378350</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [274,] 2.170805 4.414616 4.412710 4.408845 4.407364 4.393263 4.379207 4.378312</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [275,] 2.170824 4.414653 4.412672 4.408807 4.407326 4.393225 4.379170 4.378275</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [276,] 2.170842 4.414691 4.412634 4.408770 4.407289 4.393188 4.379133 4.378238</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [277,] 2.170861 4.414729 4.412597 4.408732 4.407251 4.393151 4.379096 4.378200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [278,] 2.170879 4.414766 4.412559 4.408695 4.407214 4.393113 4.379058 4.378163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [279,] 2.170898 4.414804 4.412522 4.408657 4.407176 4.393076 4.379021 4.378126</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [280,] 2.170916 4.414841 4.412484 4.408619 4.407139 4.393038 4.378984 4.378089</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [281,] 2.170935 4.414879 4.412447 4.408582 4.407101 4.393001 4.378946 4.378051</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [282,] 2.170953 4.414917 4.412409 4.408544 4.407064 4.392964 4.378909 4.378014</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [283,] 2.170972 4.414954 4.412371 4.408507 4.407026 4.392926 4.378872 4.377977</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [284,] 2.170990 4.414992 4.412334 4.408469 4.406989 4.392889 4.378835 4.377940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [285,] 2.171009 4.415029 4.412296 4.408432 4.406951 4.392851 4.378797 4.377902</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [286,] 2.171027 4.415067 4.412259 4.408394 4.406914 4.392814 4.378760 4.377865</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [287,] 2.171046 4.415105 4.412221 4.408357 4.406876 4.392777 4.378723 4.377828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [288,] 2.171064 4.415142 4.412184 4.408319 4.406839 4.392739 4.378685 4.377790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [289,] 2.171083 4.415180 4.412146 4.408282 4.406801 4.392702 4.378648 4.377753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [290,] 2.171101 4.415217 4.412108 4.408244 4.406764 4.392664 4.378611 4.377716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [291,] 2.171119 4.415255 4.412071 4.408207 4.406726 4.392627 4.378574 4.377679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [292,] 2.171138 4.415292 4.412033 4.408169 4.406689 4.392590 4.378536 4.377641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [293,] 2.171156 4.415330 4.411996 4.408132 4.406651 4.392552 4.378499 4.377604</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [294,] 2.171175 4.415368 4.411958 4.408094 4.406614 4.392515 4.378462 4.377567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [295,] 2.171193 4.415405 4.411921 4.408056 4.406576 4.392477 4.378425 4.377530</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [296,] 2.171212 4.415443 4.411883 4.408019 4.406538 4.392440 4.378387 4.377492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [297,] 2.171230 4.415458 4.411846 4.407981 4.406501 4.392403 4.378350 4.377455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [298,] 2.171249 4.415421 4.411808 4.407944 4.406463 4.392365 4.378313 4.377418</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [299,] 2.171267 4.415383 4.411770 4.407906 4.406426 4.392328 4.378275 4.377380</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [300,] 2.171286 4.415345 4.411733 4.407869 4.406388 4.392290 4.378238 4.377343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [301,] 2.171304 4.415308 4.411695 4.407831 4.406351 4.392253 4.378201 4.377306</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [302,] 2.171323 4.415270 4.411658 4.407794 4.406313 4.392216 4.378164 4.377269</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [303,] 2.171341 4.415233 4.411620 4.407756 4.406276 4.392178 4.378126 4.377231</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [304,] 2.171360 4.415195 4.411583 4.407719 4.406238 4.392141 4.378089 4.377194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [305,] 2.171378 4.415158 4.411545 4.407681 4.406201 4.392103 4.378052 4.377157</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [306,] 2.171397 4.415120 4.411507 4.407644 4.406163 4.392066 4.378014 4.377120</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [307,] 2.171415 4.415082 4.411470 4.407606 4.406126 4.392029 4.377977 4.377082</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [308,] 2.171434 4.415045 4.411432 4.407569 4.406088 4.391991 4.377940 4.377045</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [309,] 2.160469 4.393596 4.397194 4.401049 4.402527 4.414281 4.400158 4.399259</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [310,] 2.160488 4.393634 4.397232 4.401086 4.402565 4.414243 4.400121 4.399221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [311,] 2.160506 4.393671 4.397269 4.401124 4.402602 4.414206 4.400083 4.399184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [312,] 2.160524 4.393709 4.397306 4.401161 4.402640 4.414168 4.400046 4.399146</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [313,] 2.160543 4.393746 4.397344 4.401199 4.402677 4.414130 4.400008 4.399109</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [314,] 2.160561 4.393783 4.397381 4.401236 4.402715 4.414093 4.399971 4.399071</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [315,] 2.160580 4.393821 4.397419 4.401274 4.402752 4.414055 4.399933 4.399034</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [316,] 2.160598 4.393858 4.397456 4.401311 4.402790 4.414018 4.399896 4.398997</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [317,] 2.160616 4.393896 4.397494 4.401349 4.402827 4.413980 4.399858 4.398959</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [318,] 2.160635 4.393933 4.397531 4.401386 4.402865 4.413942 4.399821 4.398922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [319,] 2.160653 4.393970 4.397569 4.401424 4.402902 4.413905 4.399784 4.398884</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [320,] 2.160672 4.394008 4.397606 4.401461 4.402940 4.413867 4.399746 4.398847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [321,] 2.160690 4.394045 4.397643 4.401498 4.402977 4.413830 4.399709 4.398809</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [322,] 2.159523 4.391672 4.395268 4.399121 4.400599 4.414724 4.402086 4.401186</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [323,] 2.159541 4.391709 4.395306 4.399159 4.400637 4.414762 4.402049 4.401149</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [324,] 2.159560 4.391747 4.395343 4.399196 4.400674 4.414799 4.402011 4.401111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [325,] 2.159578 4.391784 4.395381 4.399234 4.400712 4.414837 4.401974 4.401074</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [326,] 2.159597 4.391822 4.395418 4.399271 4.400749 4.414874 4.401936 4.401036</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [327,] 2.159615 4.391859 4.395455 4.399309 4.400787 4.414912 4.401899 4.400999</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [328,] 2.159633 4.391896 4.395493 4.399346 4.400824 4.414949 4.401861 4.400962</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [329,] 2.159652 4.391934 4.395530 4.399383 4.400861 4.414987 4.401824 4.400924</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [330,] 2.159670 4.391971 4.395568 4.399421 4.400899 4.415025 4.401786 4.400887</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [331,] 2.159689 4.392009 4.395605 4.399458 4.400936 4.415062 4.401749 4.400849</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [332,] 2.159707 4.392046 4.395642 4.399496 4.400974 4.415100 4.401711 4.400812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [333,] 2.159725 4.392083 4.395680 4.399533 4.401011 4.415137 4.401674 4.400774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [334,] 2.159744 4.392121 4.395717 4.399571 4.401049 4.415175 4.401636 4.400737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [335,] 2.159762 4.392158 4.395755 4.399608 4.401086 4.415213 4.401599 4.400699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [336,] 2.159780 4.392196 4.395792 4.399646 4.401124 4.415250 4.401561 4.400662</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [337,] 2.159799 4.392233 4.395830 4.399683 4.401161 4.415288 4.401524 4.400624</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [338,] 2.159817 4.392270 4.395867 4.399721 4.401199 4.415325 4.401487 4.400587</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [339,] 2.159836 4.392308 4.395904 4.399758 4.401236 4.415363 4.401449 4.400549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [340,] 2.159854 4.392345 4.395942 4.399795 4.401274 4.415401 4.401412 4.400512</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [341,] 2.159872 4.392383 4.395979 4.399833 4.401311 4.415438 4.401374 4.400474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [342,] 2.159891 4.392420 4.396017 4.399870 4.401349 4.415463 4.401337 4.400437</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [343,] 2.159909 4.392457 4.396054 4.399908 4.401386 4.415425 4.401299 4.400400</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [344,] 2.159928 4.392495 4.396092 4.399945 4.401424 4.415388 4.401262 4.400362</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [345,] 2.159946 4.392532 4.396129 4.399983 4.401461 4.415350 4.401224 4.400325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [346,] 2.159964 4.392569 4.396166 4.400020 4.401498 4.415313 4.401187 4.400287</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [347,] 2.159983 4.392607 4.396204 4.400058 4.401536 4.415275 4.401149 4.400250</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [348,] 2.160001 4.392644 4.396241 4.400095 4.401573 4.415237 4.401112 4.400212</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [349,] 2.160020 4.392682 4.396279 4.400133 4.401611 4.415200 4.401074 4.400175</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [350,] 2.160038 4.392719 4.396316 4.400170 4.401648 4.415162 4.401037 4.400137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [351,] 2.160056 4.392756 4.396354 4.400208 4.401686 4.415125 4.400999 4.400100</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [352,] 2.160075 4.392794 4.396391 4.400245 4.401723 4.415087 4.400962 4.400062</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [353,] 2.160093 4.392831 4.396428 4.400282 4.401761 4.415049 4.400924 4.400025</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [354,] 2.160111 4.392869 4.396466 4.400320 4.401798 4.415012 4.400887 4.399987</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [355,] 2.160130 4.392906 4.396503 4.400357 4.401836 4.414974 4.400850 4.399950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [356,] 2.160148 4.392943 4.396541 4.400395 4.401873 4.414937 4.400812 4.399912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [357,] 2.160167 4.392981 4.396578 4.400432 4.401911 4.414899 4.400775 4.399875</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [358,] 2.160185 4.393018 4.396616 4.400470 4.401948 4.414862 4.400737 4.399838</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [359,] 2.160203 4.393056 4.396653 4.400507 4.401986 4.414824 4.400700 4.399800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [360,] 2.160222 4.393093 4.396690 4.400545 4.402023 4.414786 4.400662 4.399763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [361,] 2.151164 4.374672 4.378254 4.382092 4.383564 4.397634 4.411749 4.412651</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [362,] 2.151182 4.374709 4.378291 4.382129 4.383602 4.397672 4.411787 4.412688</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [363,] 2.151200 4.374746 4.378329 4.382167 4.383639 4.397709 4.411824 4.412726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [364,] 2.151218 4.374784 4.378366 4.382204 4.383676 4.397747 4.411862 4.412764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [365,] 2.151237 4.374821 4.378403 4.382241 4.383714 4.397784 4.411899 4.412801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [366,] 2.151255 4.374858 4.378440 4.382279 4.383751 4.397822 4.411937 4.412839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [367,] 2.151273 4.374895 4.378478 4.382316 4.383788 4.397859 4.411974 4.412876</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [368,] 2.151292 4.374933 4.378515 4.382353 4.383826 4.397897 4.412012 4.412914</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [369,] 2.151310 4.374970 4.378552 4.382391 4.383863 4.397934 4.412049 4.412951</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [370,] 2.151328 4.375007 4.378590 4.382428 4.383900 4.397971 4.412087 4.412989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [371,] 2.151347 4.375044 4.378627 4.382465 4.383938 4.398009 4.412125 4.413027</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [372,] 2.151365 4.375082 4.378664 4.382503 4.383975 4.398046 4.412162 4.413064</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [373,] 2.151383 4.375119 4.378701 4.382540 4.384012 4.398084 4.412200 4.413102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [374,] 2.151402 4.375156 4.378739 4.382577 4.384050 4.398121 4.412237 4.413139</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [375,] 2.152629 4.377652 4.381237 4.385078 4.386551 4.400631 4.414755 4.415281</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [376,] 2.152648 4.377690 4.381275 4.385115 4.386589 4.400668 4.414792 4.415244</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [377,] 2.152666 4.377727 4.381312 4.385153 4.386626 4.400706 4.414830 4.415206</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [378,] 2.152684 4.377764 4.381349 4.385190 4.386663 4.400743 4.414868 4.415168</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [379,] 2.152703 4.377802 4.381386 4.385227 4.386701 4.400781 4.414905 4.415131</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [380,] 2.152721 4.377839 4.381424 4.385265 4.386738 4.400818 4.414943 4.415093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [381,] 2.152739 4.377876 4.381461 4.385302 4.386775 4.400856 4.414980 4.415056</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [382,] 2.152758 4.377913 4.381498 4.385339 4.386813 4.400893 4.415018 4.415018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [383,] 2.152776 4.377951 4.381536 4.385377 4.386850 4.400931 4.415056 4.414981</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [384,] 2.152794 4.377988 4.381573 4.385414 4.386887 4.400968 4.415093 4.414943</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [385,] 2.152812 4.378025 4.381610 4.385451 4.386925 4.401005 4.415131 4.414905</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [386,] 2.152831 4.378062 4.381648 4.385489 4.386962 4.401043 4.415168 4.414868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [387,] 2.152849 4.378100 4.381685 4.385526 4.386999 4.401080 4.415206 4.414830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [388,] 2.152867 4.378137 4.381722 4.385563 4.387037 4.401118 4.415244 4.414793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [389,] 2.152886 4.378174 4.381759 4.385601 4.387074 4.401155 4.415281 4.414755</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [390,] 2.152904 4.378212 4.381797 4.385638 4.387111 4.401193 4.415319 4.414717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [391,] 2.152922 4.378249 4.381834 4.385675 4.387149 4.401230 4.415356 4.414680</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [392,] 2.152941 4.378286 4.381871 4.385713 4.387186 4.401268 4.415394 4.414642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [393,] 2.152959 4.378323 4.381909 4.385750 4.387223 4.401305 4.415432 4.414605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [394,] 2.152977 4.378361 4.381946 4.385787 4.387261 4.401343 4.415469 4.414567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [395,] 2.152996 4.378398 4.381983 4.385825 4.387298 4.401380 4.415432 4.414529</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [396,] 2.153014 4.378435 4.382021 4.385862 4.387336 4.401418 4.415394 4.414492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [397,] 2.153032 4.378473 4.382058 4.385899 4.387373 4.401455 4.415357 4.414454</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [398,] 2.153051 4.378510 4.382095 4.385937 4.387410 4.401493 4.415319 4.414417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [399,] 2.153069 4.378547 4.382133 4.385974 4.387448 4.401530 4.415282 4.414379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [400,] 2.153087 4.378584 4.382170 4.386011 4.387485 4.401568 4.415244 4.414342</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [401,] 2.153106 4.378622 4.382207 4.386049 4.387522 4.401605 4.415206 4.414304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [402,] 2.153124 4.378659 4.382244 4.386086 4.387560 4.401643 4.415169 4.414266</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [403,] 2.153142 4.378696 4.382282 4.386123 4.387597 4.401680 4.415131 4.414229</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [404,] 2.153161 4.378733 4.382319 4.386161 4.387634 4.401717 4.415094 4.414191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [405,] 2.153179 4.378771 4.382356 4.386198 4.387672 4.401755 4.415056 4.414154</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [406,] 2.153197 4.378808 4.382394 4.386235 4.387709 4.401792 4.415019 4.414116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [407,] 2.153216 4.378845 4.382431 4.386273 4.387746 4.401830 4.414981 4.414078</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [408,] 2.153234 4.378883 4.382468 4.386310 4.387784 4.401867 4.414943 4.414041</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [409,] 2.153252 4.378920 4.382506 4.386347 4.387821 4.401905 4.414906 4.414003</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [410,] 2.153271 4.378957 4.382543 4.386385 4.387858 4.401942 4.414868 4.413966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [411,] 2.153289 4.378994 4.382580 4.386422 4.387896 4.401980 4.414831 4.413928</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [412,] 2.153307 4.379032 4.382618 4.386460 4.387933 4.402017 4.414793 4.413891</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [413,] 2.153326 4.379069 4.382655 4.386497 4.387971 4.402055 4.414755 4.413853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [414,] 2.153344 4.379106 4.382692 4.386534 4.388008 4.402092 4.414718 4.413815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [415,] 2.153362 4.379144 4.382730 4.386572 4.388045 4.402130 4.414680 4.413778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [416,] 2.153381 4.379181 4.382767 4.386609 4.388083 4.402167 4.414643 4.413740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [417,] 2.153399 4.379218 4.382804 4.386646 4.388120 4.402205 4.414605 4.413703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [418,] 2.153417 4.379255 4.382841 4.386684 4.388157 4.402242 4.414567 4.413665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [419,] 2.153436 4.379293 4.382879 4.386721 4.388195 4.402280 4.414530 4.413628</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [420,] 2.153454 4.379330 4.382916 4.386758 4.388232 4.402317 4.414492 4.413590</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [421,] 2.153472 4.379367 4.382953 4.386796 4.388269 4.402355 4.414455 4.413552</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [422,] 2.153491 4.379405 4.382991 4.386833 4.388307 4.402392 4.414417 4.413515</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [423,] 2.153509 4.379442 4.383028 4.386870 4.388344 4.402430 4.414380 4.413477</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [424,] 2.153527 4.379479 4.383065 4.386908 4.388382 4.402467 4.414342 4.413440</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [425,] 2.153546 4.379516 4.383103 4.386945 4.388419 4.402505 4.414304 4.413402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [426,] 2.153564 4.379554 4.383140 4.386982 4.388456 4.402542 4.414267 4.413364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [427,] 2.153582 4.379591 4.383177 4.387020 4.388494 4.402579 4.414229 4.413327</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [428,] 2.153601 4.379628 4.383215 4.387057 4.388531 4.402617 4.414192 4.413289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [429,] 2.153619 4.379666 4.383252 4.387094 4.388568 4.402654 4.414154 4.413252</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [430,] 2.153637 4.379703 4.383289 4.387132 4.388606 4.402692 4.414116 4.413214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [431,] 2.153656 4.379740 4.383327 4.387169 4.388643 4.402729 4.414079 4.413177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [432,] 2.153674 4.379777 4.383364 4.387206 4.388680 4.402767 4.414041 4.413139</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [433,] 2.153692 4.379815 4.383401 4.387244 4.388718 4.402804 4.414004 4.413101</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [434,] 2.153711 4.379852 4.383439 4.387281 4.388755 4.402842 4.413966 4.413064</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [435,] 2.153729 4.379889 4.383476 4.387319 4.388793 4.402879 4.413929 4.413026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [436,] 2.153747 4.379927 4.383513 4.387356 4.388830 4.402917 4.413891 4.412989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [437,] 2.153766 4.379964 4.383551 4.387393 4.388867 4.402954 4.413853 4.412951</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [438,] 2.153784 4.380001 4.383588 4.387431 4.388905 4.402992 4.413816 4.412914</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [439,] 2.153802 4.380038 4.383625 4.387468 4.388942 4.403029 4.413778 4.412876</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [440,] 2.153821 4.380076 4.383663 4.387505 4.388979 4.403067 4.413741 4.412838</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [441,] 2.153839 4.380113 4.383700 4.387543 4.389017 4.403104 4.413703 4.412801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [442,] 2.153858 4.380150 4.383737 4.387580 4.389054 4.403142 4.413666 4.412763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [443,] 2.153876 4.380188 4.383774 4.387617 4.389091 4.403179 4.413628 4.412726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [444,] 2.153894 4.380225 4.383812 4.387655 4.389129 4.403217 4.413590 4.412688</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [445,] 2.153913 4.380262 4.383849 4.387692 4.389166 4.403254 4.413553 4.412651</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [446,] 2.153931 4.380300 4.383886 4.387729 4.389204 4.403292 4.413515 4.412613</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [447,] 2.153949 4.380337 4.383924 4.387767 4.389241 4.403329 4.413478 4.412575</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [448,] 2.153968 4.380374 4.383961 4.387804 4.389278 4.403367 4.413440 4.412538</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [449,] 2.153986 4.380411 4.383998 4.387842 4.389316 4.403404 4.413402 4.412500</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [450,] 2.154004 4.380449 4.384036 4.387879 4.389353 4.403442 4.413365 4.412463</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [451,] 2.154023 4.380486 4.384073 4.387916 4.389390 4.403479 4.413327 4.412425</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [452,] 2.154041 4.380523 4.384110 4.387954 4.389428 4.403517 4.413290 4.412388</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [453,] 2.154059 4.380561 4.384148 4.387991 4.389465 4.403554 4.413252 4.412350</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [454,] 2.154078 4.380598 4.384185 4.388028 4.389503 4.403592 4.413215 4.412313</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [455,] 2.154096 4.380635 4.384222 4.388066 4.389540 4.403629 4.413177 4.412275</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [456,] 2.154114 4.380672 4.384260 4.388103 4.389577 4.403667 4.413139 4.412237</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [457,] 2.154133 4.380710 4.384297 4.388140 4.389615 4.403704 4.413102 4.412200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [458,] 2.154151 4.380747 4.384334 4.388178 4.389652 4.403742 4.413064 4.412162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [459,] 2.154169 4.380784 4.384372 4.388215 4.389689 4.403779 4.413027 4.412125</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [460,] 2.127075 4.325685 4.329227 4.333022 4.334478 4.348390 4.362347 4.363239</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [461,] 2.127093 4.325722 4.329264 4.333059 4.334515 4.348427 4.362384 4.363276</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [462,] 2.118559 4.308367 4.311895 4.315675 4.317125 4.330982 4.344882 4.345771</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [463,] 2.118577 4.308404 4.311932 4.315712 4.317162 4.331019 4.344919 4.345808</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [464,] 2.118595 4.308440 4.311968 4.315748 4.317198 4.331055 4.344956 4.345845</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [465,] 2.118613 4.308477 4.312005 4.315785 4.317235 4.331092 4.344993 4.345882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [466,] 2.118632 4.308514 4.312042 4.315822 4.317272 4.331129 4.345030 4.345919</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [467,] 2.118650 4.308550 4.312079 4.315859 4.317309 4.331166 4.345067 4.345956</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [468,] 2.154188 4.380822 4.384409 4.388252 4.389727 4.403817 4.412989 4.412087</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [469,] 2.154206 4.380859 4.384446 4.388290 4.389764 4.403854 4.412952 4.412050</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [470,] 2.154224 4.380896 4.384484 4.388327 4.389802 4.403892 4.412914 4.412012</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [471,] 2.118668 4.308587 4.312115 4.315895 4.317345 4.331203 4.345104 4.345993</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [472,] 2.118686 4.308624 4.312152 4.315932 4.317382 4.331240 4.345141 4.346030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [473,] 2.118704 4.308660 4.312189 4.315969 4.317419 4.331277 4.345178 4.346067</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [474,] 2.118722 4.308697 4.312225 4.316006 4.317456 4.331314 4.345215 4.346104</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [475,] 2.118740 4.308734 4.312262 4.316042 4.317492 4.331350 4.345252 4.346141</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [476,] 2.118758 4.308771 4.312299 4.316079 4.317529 4.331387 4.345289 4.346178</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [477,] 2.118776 4.308807 4.312336 4.316116 4.317566 4.331424 4.345326 4.346215</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [478,] 2.118794 4.308844 4.312372 4.316153 4.317603 4.331461 4.345363 4.346252</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [479,] 2.118812 4.308881 4.312409 4.316189 4.317639 4.331498 4.345400 4.346289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [480,] 2.118830 4.308917 4.312446 4.316226 4.317676 4.331535 4.345437 4.346326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [481,] 2.118848 4.308954 4.312482 4.316263 4.317713 4.331572 4.345474 4.346363</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [482,] 2.118866 4.308991 4.312519 4.316300 4.317750 4.331609 4.345511 4.346400</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [483,] 2.118884 4.309027 4.312556 4.316336 4.317787 4.331645 4.345548 4.346437</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [484,] 2.118902 4.309064 4.312593 4.316373 4.317823 4.331682 4.345585 4.346474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [485,] 2.118920 4.309101 4.312629 4.316410 4.317860 4.331719 4.345622 4.346511</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [486,] 2.118938 4.309137 4.312666 4.316447 4.317897 4.331756 4.345659 4.346548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [487,] 2.118956 4.309174 4.312703 4.316483 4.317934 4.331793 4.345696 4.346585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [488,] 2.118974 4.309211 4.312739 4.316520 4.317970 4.331830 4.345733 4.346622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [489,] 2.118992 4.309247 4.312776 4.316557 4.318007 4.331867 4.345770 4.346659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [490,] 2.119010 4.309284 4.312813 4.316594 4.318044 4.331904 4.345807 4.346696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [491,] 2.119028 4.309321 4.312850 4.316630 4.318081 4.331940 4.345844 4.346733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [492,] 2.119046 4.309358 4.312886 4.316667 4.318117 4.331977 4.345881 4.346770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [493,] 2.119064 4.309394 4.312923 4.316704 4.318154 4.332014 4.345918 4.346807</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [494,] 2.119083 4.309431 4.312960 4.316741 4.318191 4.332051 4.345955 4.346844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [495,] 2.119101 4.309468 4.312997 4.316777 4.318228 4.332088 4.345992 4.346881</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [496,] 2.119119 4.309504 4.313033 4.316814 4.318264 4.332125 4.346029 4.346918</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [497,] 2.119137 4.309541 4.313070 4.316851 4.318301 4.332162 4.346066 4.346955</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [498,] 2.119155 4.309578 4.313107 4.316888 4.318338 4.332199 4.346103 4.346992</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [499,] 2.119173 4.309614 4.313143 4.316924 4.318375 4.332236 4.346140 4.347029</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [500,] 2.119191 4.309651 4.313180 4.316961 4.318412 4.332272 4.346177 4.347066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [501,] 2.119209 4.309688 4.313217 4.316998 4.318448 4.332309 4.346214 4.347103</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [502,] 2.119227 4.309724 4.313254 4.317035 4.318485 4.332346 4.346251 4.347140</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [503,] 2.119245 4.309761 4.313290 4.317071 4.318522 4.332383 4.346288 4.347177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [504,] 2.119263 4.309798 4.313327 4.317108 4.318559 4.332420 4.346325 4.347214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [505,] 2.119281 4.309835 4.313364 4.317145 4.318595 4.332457 4.346362 4.347251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [506,] 2.119299 4.309871 4.313400 4.317182 4.318632 4.332494 4.346399 4.347288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [507,] 2.119317 4.309908 4.313437 4.317218 4.318669 4.332531 4.346436 4.347325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [508,] 2.119335 4.309945 4.313474 4.317255 4.318706 4.332568 4.346473 4.347362</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [509,] 2.171452 4.415007 4.411395 4.407531 4.406051 4.391954 4.377903 4.377008</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [510,] 2.171471 4.414970 4.411357 4.407494 4.406013 4.391916 4.377865 4.376971</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [511,] 2.171489 4.414932 4.411320 4.407456 4.405976 4.391879 4.377828 4.376933</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [512,] 2.171508 4.414894 4.411282 4.407418 4.405938 4.391842 4.377791 4.376896</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [513,] 2.171526 4.414857 4.411245 4.407381 4.405901 4.391804 4.377754 4.376859</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [514,] 2.171545 4.414819 4.411207 4.407343 4.405863 4.391767 4.377716 4.376821</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [515,] 2.171563 4.414782 4.411169 4.407306 4.405826 4.391729 4.377679 4.376784</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [516,] 2.171582 4.414744 4.411132 4.407268 4.405788 4.391692 4.377642 4.376747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [517,] 2.171600 4.414706 4.411094 4.407231 4.405751 4.391655 4.377604 4.376710</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [518,] 2.171619 4.414669 4.411057 4.407193 4.405713 4.391617 4.377567 4.376672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [519,] 2.160249 4.393147 4.396745 4.400599 4.402078 4.414732 4.400608 4.399708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [520,] 2.160267 4.393185 4.396782 4.400637 4.402115 4.414694 4.400570 4.399671</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [521,] 2.160285 4.393222 4.396820 4.400674 4.402153 4.414657 4.400533 4.399633</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [522,] 2.160304 4.393260 4.396857 4.400712 4.402190 4.414619 4.400495 4.399596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [523,] 2.160322 4.393297 4.396895 4.400749 4.402228 4.414581 4.400458 4.399558</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [524,] 2.160341 4.393335 4.396932 4.400787 4.402265 4.414544 4.400420 4.399521</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [525,] 2.160359 4.393372 4.396970 4.400824 4.402303 4.414506 4.400383 4.399483</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [526,] 2.160377 4.393409 4.397007 4.400861 4.402340 4.414469 4.400345 4.399446</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [527,] 2.160396 4.393447 4.397044 4.400899 4.402377 4.414431 4.400308 4.399409</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [528,] 2.160414 4.393484 4.397082 4.400936 4.402415 4.414393 4.400271 4.399371</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [529,] 2.160433 4.393522 4.397119 4.400974 4.402452 4.414356 4.400233 4.399334</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [530,] 2.160451 4.393559 4.397157 4.401011 4.402490 4.414318 4.400196 4.399296</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [531,] 2.151420 4.375193 4.378776 4.382615 4.384087 4.398159 4.412275 4.413177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [532,] 2.151438 4.375231 4.378813 4.382652 4.384124 4.398196 4.412312 4.413214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [533,] 2.151457 4.375268 4.378851 4.382689 4.384162 4.398234 4.412350 4.413252</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [534,] 2.151475 4.375305 4.378888 4.382726 4.384199 4.398271 4.412388 4.413290</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [535,] 2.151493 4.375342 4.378925 4.382764 4.384236 4.398308 4.412425 4.413327</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [536,] 2.151512 4.375380 4.378962 4.382801 4.384274 4.398346 4.412463 4.413365</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [537,] 2.151530 4.375417 4.379000 4.382838 4.384311 4.398383 4.412500 4.413402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [538,] 2.151548 4.375454 4.379037 4.382876 4.384348 4.398421 4.412538 4.413440</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [539,] 2.151566 4.375491 4.379074 4.382913 4.384386 4.398458 4.412575 4.413478</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [540,] 2.151585 4.375529 4.379112 4.382950 4.384423 4.398496 4.412613 4.413515</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [541,] 2.151603 4.375566 4.379149 4.382988 4.384460 4.398533 4.412651 4.413553</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [542,] 2.151621 4.375603 4.379186 4.383025 4.384498 4.398571 4.412688 4.413590</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [543,] 2.151640 4.375640 4.379223 4.383062 4.384535 4.398608 4.412726 4.413628</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [544,] 2.151658 4.375678 4.379261 4.383100 4.384572 4.398645 4.412763 4.413665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [545,] 2.151676 4.375715 4.379298 4.383137 4.384610 4.398683 4.412801 4.413703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [546,] 2.151695 4.375752 4.379335 4.383174 4.384647 4.398720 4.412838 4.413741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [547,] 2.151713 4.375789 4.379373 4.383212 4.384684 4.398758 4.412876 4.413778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [548,] 2.151731 4.375827 4.379410 4.383249 4.384722 4.398795 4.412914 4.413816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [549,] 2.151750 4.375864 4.379447 4.383286 4.384759 4.398833 4.412951 4.413853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [550,] 2.151768 4.375901 4.379484 4.383324 4.384796 4.398870 4.412989 4.413891</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [551,] 2.151786 4.375938 4.379522 4.383361 4.384834 4.398908 4.413026 4.413928</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [552,] 2.151805 4.375976 4.379559 4.383398 4.384871 4.398945 4.413064 4.413966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [553,] 2.151823 4.376013 4.379596 4.383435 4.384908 4.398983 4.413101 4.414004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [554,] 2.151841 4.376050 4.379634 4.383473 4.384946 4.399020 4.413139 4.414041</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [555,] 2.151860 4.376087 4.379671 4.383510 4.384983 4.399057 4.413177 4.414079</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [556,] 2.151878 4.376125 4.379708 4.383547 4.385020 4.399095 4.413214 4.414116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [557,] 2.151896 4.376162 4.379745 4.383585 4.385058 4.399132 4.413252 4.414154</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [558,] 2.151915 4.376199 4.379783 4.383622 4.385095 4.399170 4.413289 4.414192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [559,] 2.151933 4.376236 4.379820 4.383659 4.385132 4.399207 4.413327 4.414229</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [560,] 2.151951 4.376274 4.379857 4.383697 4.385170 4.399245 4.413364 4.414267</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [561,] 2.151970 4.376311 4.379895 4.383734 4.385207 4.399282 4.413402 4.414304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [562,] 2.151988 4.376348 4.379932 4.383771 4.385244 4.399320 4.413440 4.414342</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [563,] 2.152006 4.376385 4.379969 4.383809 4.385282 4.399357 4.413477 4.414379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [564,] 2.152024 4.376423 4.380006 4.383846 4.385319 4.399395 4.413515 4.414417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [565,] 2.152043 4.376460 4.380044 4.383883 4.385356 4.399432 4.413552 4.414455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [566,] 2.152061 4.376497 4.380081 4.383921 4.385394 4.399469 4.413590 4.414492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [567,] 2.152079 4.376534 4.380118 4.383958 4.385431 4.399507 4.413627 4.414530</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [568,] 2.152098 4.376572 4.380156 4.383995 4.385468 4.399544 4.413665 4.414567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [569,] 2.152116 4.376609 4.380193 4.384033 4.385506 4.399582 4.413703 4.414605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [570,] 2.152134 4.376646 4.380230 4.384070 4.385543 4.399619 4.413740 4.414643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [571,] 2.152153 4.376684 4.380267 4.384107 4.385580 4.399657 4.413778 4.414680</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [572,] 2.152171 4.376721 4.380305 4.384145 4.385618 4.399694 4.413815 4.414718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [573,] 2.152189 4.376758 4.380342 4.384182 4.385655 4.399732 4.413853 4.414755</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [574,] 2.152208 4.376795 4.380379 4.384219 4.385692 4.399769 4.413890 4.414793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [575,] 2.152226 4.376833 4.380417 4.384257 4.385730 4.399807 4.413928 4.414830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [576,] 2.152244 4.376870 4.380454 4.384294 4.385767 4.399844 4.413966 4.414868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [577,] 2.152263 4.376907 4.380491 4.384331 4.385804 4.399882 4.414003 4.414906</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [578,] 2.152281 4.376944 4.380529 4.384369 4.385842 4.399919 4.414041 4.414943</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [579,] 2.152299 4.376982 4.380566 4.384406 4.385879 4.399956 4.414078 4.414981</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [580,] 2.152318 4.377019 4.380603 4.384443 4.385916 4.399994 4.414116 4.415018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [581,] 2.152336 4.377056 4.380640 4.384481 4.385954 4.400031 4.414154 4.415056</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [582,] 2.152354 4.377093 4.380678 4.384518 4.385991 4.400069 4.414191 4.415094</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [583,] 2.152373 4.377131 4.380715 4.384555 4.386028 4.400106 4.414229 4.415131</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [584,] 2.152391 4.377168 4.380752 4.384593 4.386066 4.400144 4.414266 4.415169</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [585,] 2.152409 4.377205 4.380790 4.384630 4.386103 4.400181 4.414304 4.415206</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [586,] 2.152428 4.377243 4.380827 4.384667 4.386140 4.400219 4.414341 4.415244</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [587,] 2.152446 4.377280 4.380864 4.384705 4.386178 4.400256 4.414379 4.415282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [588,] 2.152464 4.377317 4.380902 4.384742 4.386215 4.400294 4.414417 4.415319</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [589,] 2.152483 4.377354 4.380939 4.384779 4.386252 4.400331 4.414454 4.415357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [590,] 2.152501 4.377392 4.380976 4.384817 4.386290 4.400369 4.414492 4.415394</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [591,] 2.152519 4.377429 4.381013 4.384854 4.386327 4.400406 4.414529 4.415432</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [592,] 2.152538 4.377466 4.381051 4.384891 4.386364 4.400443 4.414567 4.415469</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [593,] 2.152556 4.377503 4.381088 4.384929 4.386402 4.400481 4.414605 4.415432</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [594,] 2.152574 4.377541 4.381125 4.384966 4.386439 4.400518 4.414642 4.415394</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [595,] 2.152593 4.377578 4.381163 4.385003 4.386476 4.400556 4.414680 4.415356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [596,] 2.152611 4.377615 4.381200 4.385041 4.386514 4.400593 4.414717 4.415319</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [597,] 2.154243 4.380934 4.384521 4.388365 4.389839 4.403929 4.412876 4.411974</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [598,] 2.154261 4.380971 4.384558 4.388402 4.389876 4.403967 4.412839 4.411937</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [599,] 2.154279 4.381008 4.384596 4.388439 4.389914 4.404004 4.412801 4.411899</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [600,] 2.154298 4.381045 4.384633 4.388477 4.389951 4.404042 4.412764 4.411862</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [601,] 2.154316 4.381083 4.384670 4.388514 4.389988 4.404079 4.412726 4.411824</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [602,] 2.154334 4.381120 4.384708 4.388551 4.390026 4.404117 4.412689 4.411787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [603,] 2.154353 4.381157 4.384745 4.388589 4.390063 4.404154 4.412651 4.411749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [604,] 2.154371 4.381195 4.384782 4.388626 4.390101 4.404192 4.412613 4.411711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [605,] 2.154389 4.381232 4.384820 4.388663 4.390138 4.404229 4.412576 4.411674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [606,] 2.154408 4.381269 4.384857 4.388701 4.390175 4.404267 4.412538 4.411636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [607,] 2.154426 4.381307 4.384894 4.388738 4.390213 4.404304 4.412501 4.411599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [608,] 2.154444 4.381344 4.384932 4.388776 4.390250 4.404342 4.412463 4.411561</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [609,] 2.154463 4.381381 4.384969 4.388813 4.390287 4.404379 4.412426 4.411524</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [610,] 2.154481 4.381419 4.385006 4.388850 4.390325 4.404417 4.412388 4.411486</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [611,] 2.154499 4.381456 4.385044 4.388888 4.390362 4.404454 4.412350 4.411449</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [612,] 2.154518 4.381493 4.385081 4.388925 4.390400 4.404492 4.412313 4.411411</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [613,] 2.154536 4.381530 4.385118 4.388962 4.390437 4.404529 4.412275 4.411373</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [614,] 2.154554 4.381568 4.385156 4.389000 4.390474 4.404567 4.412238 4.411336</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [615,] 2.154573 4.381605 4.385193 4.389037 4.390512 4.404604 4.412200 4.411298</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [616,] 2.126369 4.324249 4.327790 4.331584 4.333039 4.346947 4.360898 4.361790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [617,] 2.126387 4.324285 4.327827 4.331620 4.333076 4.346984 4.360936 4.361827</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [618,] 2.126405 4.324322 4.327863 4.331657 4.333113 4.347021 4.360973 4.361864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [619,] 2.126423 4.324359 4.327900 4.331694 4.333149 4.347058 4.361010 4.361901</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [620,] 2.126441 4.324396 4.327937 4.331731 4.333186 4.347095 4.361047 4.361939</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [621,] 2.126459 4.324433 4.327974 4.331768 4.333223 4.347132 4.361084 4.361976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [622,] 2.126478 4.324470 4.328011 4.331805 4.333260 4.347169 4.361121 4.362013</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [623,] 2.126496 4.324506 4.328048 4.331842 4.333297 4.347206 4.361158 4.362050</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [624,] 2.126514 4.324543 4.328084 4.331879 4.333334 4.347243 4.361195 4.362087</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [625,] 2.126532 4.324580 4.328121 4.331915 4.333371 4.347280 4.361233 4.362124</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [626,] 2.126550 4.324617 4.328158 4.331952 4.333408 4.347317 4.361270 4.362161</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [627,] 2.126568 4.324654 4.328195 4.331989 4.333445 4.347354 4.361307 4.362199</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [628,] 2.126586 4.324690 4.328232 4.332026 4.333482 4.347391 4.361344 4.362236</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [629,] 2.126599 4.324717 4.328258 4.332053 4.333508 4.347417 4.361371 4.362262</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [630,] 2.126622 4.324764 4.328306 4.332100 4.333555 4.347465 4.361418 4.362310</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [631,] 2.126640 4.324801 4.328342 4.332137 4.333592 4.347502 4.361455 4.362347</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [632,] 2.126659 4.324838 4.328379 4.332174 4.333629 4.347539 4.361493 4.362384</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [633,] 2.126677 4.324875 4.328416 4.332210 4.333666 4.347576 4.361530 4.362421</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [634,] 2.126695 4.324911 4.328453 4.332247 4.333703 4.347613 4.361567 4.362459</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [635,] 2.126713 4.324948 4.328490 4.332284 4.333740 4.347650 4.361604 4.362496</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [636,] 2.126731 4.324985 4.328527 4.332321 4.333777 4.347687 4.361641 4.362533</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [637,] 2.126749 4.325022 4.328564 4.332358 4.333814 4.347724 4.361678 4.362570</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [638,] 2.126767 4.325059 4.328600 4.332395 4.333850 4.347761 4.361715 4.362607</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [639,] 2.126785 4.325096 4.328637 4.332432 4.333887 4.347798 4.361752 4.362644</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [640,] 2.126803 4.325132 4.328674 4.332469 4.333924 4.347835 4.361790 4.362681</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [641,] 2.126822 4.325169 4.328711 4.332506 4.333961 4.347872 4.361827 4.362719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [642,] 2.126840 4.325206 4.328748 4.332542 4.333998 4.347909 4.361864 4.362756</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [643,] 2.126858 4.325243 4.328785 4.332579 4.334035 4.347946 4.361901 4.362793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [644,] 2.126876 4.325280 4.328822 4.332616 4.334072 4.347983 4.361938 4.362830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [645,] 2.126894 4.325316 4.328858 4.332653 4.334109 4.348020 4.361975 4.362867</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [646,] 2.126912 4.325353 4.328895 4.332690 4.334146 4.348057 4.362012 4.362904</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [647,] 2.126930 4.325390 4.328932 4.332727 4.334183 4.348094 4.362050 4.362941</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [648,] 2.126948 4.325427 4.328969 4.332764 4.334219 4.348131 4.362087 4.362979</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [649,] 2.126966 4.325464 4.329006 4.332801 4.334256 4.348168 4.362124 4.363016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [650,] 2.126984 4.325501 4.329043 4.332838 4.334293 4.348205 4.362161 4.363053</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [651,] 2.127003 4.325537 4.329079 4.332874 4.334330 4.348242 4.362198 4.363090</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [652,] 2.127021 4.325574 4.329116 4.332911 4.334367 4.348279 4.362235 4.363127</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [653,] 2.127039 4.325611 4.329153 4.332948 4.334404 4.348316 4.362272 4.363164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [654,] 2.127057 4.325648 4.329190 4.332985 4.334441 4.348353 4.362310 4.363201</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19]</span>
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+<span class="r-out co"><span class="r-pr">#&gt;</span> [580,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [581,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [582,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [583,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [584,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [585,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [586,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [587,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
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+<span class="r-out co"><span class="r-pr">#&gt;</span> [589,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [590,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [591,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [592,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [593,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [594,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [595,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [596,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [597,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [598,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [599,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [600,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [601,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [602,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [603,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [604,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [605,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [606,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [607,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [608,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [609,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [610,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [611,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [612,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [613,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [614,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [615,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [616,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [617,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [618,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [619,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [620,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [621,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [622,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [623,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [624,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [625,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [626,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [627,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [628,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [629,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [630,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [631,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [632,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [633,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [634,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [635,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [636,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [637,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [638,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [639,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [640,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [641,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [642,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [643,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [644,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [645,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [646,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [647,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [648,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [649,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [650,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [651,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [652,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [653,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [654,]    0    0    0    0    0    0    0    0    0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>            [,14]    [,15]    [,16]    [,17]    [,18]    [,19]    [,20]    [,21]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1,] 0.7430226 1.359172 1.369214 1.380046 1.384221 1.332830 1.282513 1.280158</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [2,] 0.7430795 1.359276 1.369319 1.380152 1.384327 1.332728 1.282415 1.280060</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [3,] 0.7431364 1.359380 1.369423 1.380257 1.384433 1.332626 1.282317 1.279962</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [4,] 0.7431933 1.359484 1.369528 1.380363 1.384539 1.332524 1.282219 1.279864</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [5,] 0.7432502 1.359588 1.369633 1.380469 1.384645 1.332422 1.282121 1.279766</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [6,] 0.7433071 1.359692 1.369738 1.380574 1.384751 1.332320 1.282022 1.279668</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [7,] 0.7433640 1.359796 1.369843 1.380680 1.384857 1.332218 1.281924 1.279570</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [8,] 0.7434209 1.359900 1.369948 1.380786 1.384963 1.332116 1.281826 1.279472</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [9,] 0.7434779 1.360004 1.370053 1.380892 1.385069 1.332014 1.281728 1.279374</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [10,] 0.7435348 1.360109 1.370158 1.380997 1.385176 1.331912 1.281630 1.279276</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [11,] 0.7435917 1.360213 1.370263 1.381103 1.385282 1.331810 1.281532 1.279178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [12,] 0.7436487 1.360317 1.370367 1.381209 1.385388 1.331708 1.281434 1.279080</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [13,] 0.7437056 1.360421 1.370472 1.381315 1.385494 1.331606 1.281335 1.278982</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [14,] 0.7437626 1.360525 1.370577 1.381420 1.385600 1.331504 1.281237 1.278884</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [15,] 0.7438195 1.360629 1.370682 1.381526 1.385706 1.331402 1.281139 1.278786</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [16,] 0.7438765 1.360734 1.370787 1.381632 1.385812 1.331300 1.281041 1.278688</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [17,] 0.7439334 1.360838 1.370892 1.381738 1.385918 1.331199 1.280943 1.278590</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [18,] 0.7439904 1.360942 1.370997 1.381844 1.386024 1.331097 1.280845 1.278492</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [19,] 0.7440473 1.361046 1.371102 1.381949 1.386130 1.330995 1.280747 1.278394</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [20,] 0.7441043 1.361150 1.371207 1.382055 1.386237 1.330893 1.280649 1.278297</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [21,] 0.7441613 1.361255 1.371312 1.382161 1.386343 1.330791 1.280551 1.278199</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [22,] 0.7442183 1.361359 1.371417 1.382267 1.386449 1.330689 1.280453 1.278101</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [23,] 0.7442753 1.361463 1.371522 1.382373 1.386555 1.330587 1.280355 1.278003</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [24,] 0.7443323 1.361567 1.371627 1.382478 1.386661 1.330485 1.280257 1.277905</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25,] 0.7443892 1.361672 1.371732 1.382584 1.386767 1.330383 1.280159 1.277807</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26,] 0.7444462 1.361776 1.371837 1.382690 1.386874 1.330282 1.280061 1.277709</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [27,] 0.7445032 1.361880 1.371942 1.382796 1.386980 1.330180 1.279963 1.277612</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [28,] 0.7445603 1.361984 1.372047 1.382902 1.387086 1.330078 1.279865 1.277514</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [29,] 0.7446173 1.362089 1.372152 1.383008 1.387192 1.329976 1.279767 1.277416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [30,] 0.7446743 1.362193 1.372257 1.383114 1.387298 1.329874 1.279669 1.277318</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [31,] 0.7447313 1.362297 1.372362 1.383220 1.387405 1.329772 1.279571 1.277220</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [32,] 0.7447883 1.362402 1.372468 1.383326 1.387511 1.329671 1.279473 1.277123</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [33,] 0.7448454 1.362506 1.372573 1.383431 1.387617 1.329569 1.279375 1.277025</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [34,] 0.7449024 1.362610 1.372678 1.383537 1.387723 1.329467 1.279277 1.276927</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [35,] 0.7449594 1.362715 1.372783 1.383643 1.387830 1.329365 1.279179 1.276829</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [36,] 0.7450165 1.362819 1.372888 1.383749 1.387936 1.329263 1.279081 1.276732</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [37,] 0.7450735 1.362923 1.372993 1.383855 1.388042 1.329162 1.278983 1.276634</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [38,] 0.7451306 1.363028 1.373098 1.383961 1.388148 1.329060 1.278885 1.276536</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [39,] 0.7451876 1.363132 1.373203 1.384067 1.388255 1.328958 1.278787 1.276438</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [40,] 0.7452447 1.363236 1.373309 1.384173 1.388361 1.328856 1.278689 1.276341</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [41,] 0.7453017 1.363341 1.373414 1.384279 1.388467 1.328755 1.278591 1.276243</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [42,] 0.7453588 1.363445 1.373519 1.384385 1.388574 1.328653 1.278493 1.276145</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [43,] 0.7454159 1.363550 1.373624 1.384491 1.388680 1.328551 1.278396 1.276047</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [44,] 0.7454730 1.363654 1.373729 1.384597 1.388786 1.328449 1.278298 1.275950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [45,] 0.7455300 1.363758 1.373834 1.384703 1.388893 1.328348 1.278200 1.275852</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [46,] 0.7455871 1.363863 1.373940 1.384809 1.388999 1.328246 1.278102 1.275754</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [47,] 0.7456442 1.363967 1.374045 1.384915 1.389105 1.328144 1.278004 1.275657</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [48,] 0.7457013 1.364072 1.374150 1.385021 1.389212 1.328043 1.277906 1.275559</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [49,] 0.7457584 1.364176 1.374255 1.385127 1.389318 1.327941 1.277808 1.275461</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [50,] 0.7458155 1.364281 1.374360 1.385233 1.389424 1.327839 1.277711 1.275364</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51,] 0.7458726 1.364385 1.374466 1.385339 1.389531 1.327738 1.277613 1.275266</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [52,] 0.7459297 1.364490 1.374571 1.385446 1.389637 1.327636 1.277515 1.275168</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [53,] 0.7459869 1.364594 1.374676 1.385552 1.389744 1.327534 1.277417 1.275071</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [54,] 0.7460440 1.364698 1.374781 1.385658 1.389850 1.327433 1.277319 1.274973</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [55,] 0.7461011 1.364803 1.374887 1.385764 1.389956 1.327331 1.277221 1.274876</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [56,] 0.7461582 1.364907 1.374992 1.385870 1.390063 1.327229 1.277124 1.274778</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [57,] 0.7462154 1.365012 1.375097 1.385976 1.390169 1.327128 1.277026 1.274680</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [58,] 0.7462725 1.365117 1.375203 1.386082 1.390276 1.327026 1.276928 1.274583</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [59,] 0.7463296 1.365221 1.375308 1.386188 1.390210 1.326924 1.276830 1.274485</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [60,] 0.7463868 1.365326 1.375413 1.386294 1.390104 1.326823 1.276733 1.274388</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61,] 0.7464439 1.365430 1.375518 1.386401 1.389997 1.326721 1.276635 1.274290</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [62,] 0.7465011 1.365535 1.375624 1.386507 1.389891 1.326620 1.276537 1.274192</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [63,] 0.7465583 1.365639 1.375729 1.386613 1.389785 1.326518 1.276439 1.274095</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [64,] 0.7466154 1.365744 1.375834 1.386719 1.389678 1.326417 1.276342 1.273997</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [65,] 0.7466726 1.365848 1.375940 1.386825 1.389572 1.326315 1.276244 1.273900</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [66,] 0.7467298 1.365953 1.376045 1.386931 1.389465 1.326213 1.276146 1.273802</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [67,] 0.7467869 1.366058 1.376151 1.387038 1.389359 1.326112 1.276048 1.273705</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [68,] 0.7468441 1.366162 1.376256 1.387144 1.389253 1.326010 1.275951 1.273607</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [69,] 0.7469013 1.366267 1.376361 1.387250 1.389146 1.325909 1.275853 1.273510</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [70,] 0.7469585 1.366371 1.376467 1.387356 1.389040 1.325807 1.275755 1.273412</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [71,] 0.7470157 1.366476 1.376572 1.387463 1.388934 1.325706 1.275658 1.273315</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [72,] 0.7470729 1.366581 1.376677 1.387569 1.388827 1.325604 1.275560 1.273217</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [73,] 0.7471301 1.366685 1.376783 1.387675 1.388721 1.325503 1.275462 1.273120</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [74,] 0.7471873 1.366790 1.376888 1.387781 1.388615 1.325401 1.275365 1.273022</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [75,] 0.7472445 1.366895 1.376994 1.387888 1.388508 1.325300 1.275267 1.272925</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76,] 0.7473017 1.366999 1.377099 1.387994 1.388402 1.325198 1.275169 1.272827</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [77,] 0.7473590 1.367104 1.377205 1.388100 1.388296 1.325097 1.275072 1.272730</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [78,] 0.7474162 1.367209 1.377310 1.388206 1.388189 1.324995 1.274974 1.272632</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [79,] 0.7474734 1.367313 1.377416 1.388313 1.388083 1.324894 1.274877 1.272535</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [80,] 0.7475306 1.367418 1.377521 1.388419 1.387977 1.324793 1.274779 1.272437</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [81,] 0.7475879 1.367523 1.377626 1.388525 1.387871 1.324691 1.274681 1.272340</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [82,] 0.7476451 1.367627 1.377732 1.388632 1.387764 1.324590 1.274584 1.272243</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [83,] 0.7477024 1.367732 1.377837 1.388738 1.387658 1.324488 1.274486 1.272145</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [84,] 0.7477596 1.367837 1.377943 1.388844 1.387552 1.324387 1.274389 1.272048</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85,] 0.7478169 1.367942 1.378048 1.388951 1.387446 1.324286 1.274291 1.271950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [86,] 0.7478741 1.368046 1.378154 1.389057 1.387339 1.324184 1.274193 1.271853</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [87,] 0.7479314 1.368151 1.378260 1.389163 1.387233 1.324083 1.274096 1.271756</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [88,] 0.7479887 1.368256 1.378365 1.389270 1.387127 1.323981 1.273998 1.271658</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [89,] 0.7480460 1.368361 1.378471 1.389376 1.387021 1.323880 1.273901 1.271561</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [90,] 0.7481032 1.368465 1.378576 1.389482 1.386914 1.323779 1.273803 1.271464</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [91,] 0.7481605 1.368570 1.378682 1.389589 1.386808 1.323677 1.273706 1.271366</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [92,] 0.7482178 1.368675 1.378787 1.389695 1.386702 1.323576 1.273608 1.271269</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [93,] 0.7482751 1.368780 1.378893 1.389802 1.386596 1.323475 1.273511 1.271172</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [94,] 0.7483324 1.368885 1.378998 1.389908 1.386490 1.323373 1.273413 1.271074</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [95,] 0.7483897 1.368989 1.379104 1.390015 1.386384 1.323272 1.273316 1.270977</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [96,] 0.7484470 1.369094 1.379210 1.390121 1.386277 1.323171 1.273218 1.270880</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [97,] 0.7485043 1.369199 1.379315 1.390227 1.386171 1.323069 1.273121 1.270782</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [98,] 0.7485616 1.369304 1.379421 1.390259 1.386065 1.322968 1.273023 1.270685</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [99,] 0.7486189 1.369409 1.379526 1.390152 1.385959 1.322867 1.272926 1.270588</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [100,] 0.7486763 1.369514 1.379632 1.390046 1.385853 1.322765 1.272828 1.270490</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [101,] 0.7487336 1.369618 1.379738 1.389939 1.385747 1.322664 1.272731 1.270393</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [102,] 0.7487909 1.369723 1.379843 1.389833 1.385641 1.322563 1.272633 1.270296</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [103,] 0.7488483 1.369828 1.379949 1.389727 1.385535 1.322462 1.272536 1.270199</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [104,] 0.7489056 1.369933 1.380055 1.389620 1.385429 1.322360 1.272439 1.270101</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [105,] 0.7489629 1.370038 1.380160 1.389514 1.385322 1.322259 1.272341 1.270004</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [106,] 0.7490203 1.370143 1.380266 1.389407 1.385216 1.322158 1.272244 1.269907</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [107,] 0.7490776 1.370248 1.380372 1.389301 1.385110 1.322057 1.272146 1.269810</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [108,] 0.7491350 1.370353 1.380477 1.389195 1.385004 1.321955 1.272049 1.269712</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [109,] 0.7491924 1.370458 1.380583 1.389088 1.384898 1.321854 1.271951 1.269615</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [110,] 0.7492497 1.370563 1.380689 1.388982 1.384792 1.321753 1.271854 1.269518</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [111,] 0.7493071 1.370668 1.380795 1.388876 1.384686 1.321652 1.271757 1.269421</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [112,] 0.7493645 1.370772 1.380900 1.388769 1.384580 1.321551 1.271659 1.269324</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [113,] 0.7494219 1.370877 1.381006 1.388663 1.384474 1.321449 1.271562 1.269226</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [114,] 0.7494792 1.370982 1.381112 1.388557 1.384368 1.321348 1.271465 1.269129</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [115,] 0.7495366 1.371087 1.381218 1.388450 1.384262 1.321247 1.271367 1.269032</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [116,] 0.7495940 1.371192 1.381323 1.388344 1.384156 1.321146 1.271270 1.268935</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [117,] 0.7496514 1.371297 1.381429 1.388238 1.384050 1.321045 1.271173 1.268838</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [118,] 0.7497088 1.371402 1.381535 1.388131 1.383944 1.320944 1.271075 1.268741</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [119,] 0.7497662 1.371507 1.381641 1.388025 1.383838 1.320842 1.270978 1.268643</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [120,] 0.7498236 1.371612 1.381746 1.387919 1.383732 1.320741 1.270881 1.268546</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [121,] 0.7498810 1.371717 1.381852 1.387813 1.383626 1.320640 1.270783 1.268449</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [122,] 0.7499385 1.371822 1.381958 1.387706 1.383520 1.320539 1.270686 1.268352</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [123,] 0.7499959 1.371927 1.382064 1.387600 1.383415 1.320438 1.270589 1.268255</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [124,] 0.7500533 1.372033 1.382170 1.387494 1.383309 1.320337 1.270491 1.268158</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [125,] 0.7501107 1.372138 1.382276 1.387388 1.383203 1.320236 1.270394 1.268061</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [126,] 0.7501682 1.372243 1.382381 1.387281 1.383097 1.320135 1.270297 1.267964</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [127,] 0.7502256 1.372348 1.382487 1.387175 1.382991 1.320034 1.270200 1.267867</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [128,] 0.7502831 1.372453 1.382593 1.387069 1.382885 1.319933 1.270102 1.267770</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [129,] 0.7503405 1.372558 1.382699 1.386963 1.382779 1.319832 1.270005 1.267672</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [130,] 0.7503980 1.372663 1.382805 1.386857 1.382673 1.319730 1.269908 1.267575</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [131,] 0.7504554 1.372768 1.382911 1.386750 1.382567 1.319629 1.269811 1.267478</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [132,] 0.7505129 1.372873 1.383017 1.386644 1.382462 1.319528 1.269713 1.267381</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [133,] 0.7505704 1.372978 1.383122 1.386538 1.382356 1.319427 1.269616 1.267284</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [134,] 0.7506278 1.373083 1.383228 1.386432 1.382250 1.319326 1.269519 1.267187</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [135,] 0.7506853 1.373189 1.383334 1.386326 1.382144 1.319225 1.269422 1.267090</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [136,] 0.7507428 1.373294 1.383440 1.386220 1.382038 1.319124 1.269325 1.266993</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [137,] 0.7508003 1.373399 1.383546 1.386113 1.381932 1.319023 1.269227 1.266896</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [138,] 0.7508578 1.373504 1.383652 1.386007 1.381827 1.318922 1.269130 1.266799</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [139,] 0.7509153 1.373609 1.383758 1.385901 1.381721 1.318821 1.269033 1.266702</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [140,] 0.7509728 1.373714 1.383864 1.385795 1.381615 1.318720 1.268936 1.266605</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [141,] 0.7510303 1.373820 1.383970 1.385689 1.381509 1.318619 1.268839 1.266508</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [142,] 0.7510878 1.373925 1.384076 1.385583 1.381403 1.318518 1.268742 1.266411</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [143,] 0.7511453 1.374030 1.384182 1.385477 1.381298 1.318417 1.268645 1.266314</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [144,] 0.7512028 1.374135 1.384288 1.385371 1.381192 1.318317 1.268547 1.266217</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [145,] 0.7512603 1.374240 1.384394 1.385265 1.381086 1.318216 1.268450 1.266120</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [146,] 0.7513178 1.374346 1.384500 1.385159 1.380980 1.318115 1.268353 1.266023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [147,] 0.7513754 1.374451 1.384606 1.385052 1.380875 1.318014 1.268256 1.265927</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [148,] 0.7514329 1.374556 1.384712 1.384946 1.380769 1.317913 1.268159 1.265830</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [149,] 0.7514904 1.374661 1.384818 1.384840 1.380663 1.317812 1.268062 1.265733</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [150,] 0.7515480 1.374767 1.384924 1.384734 1.380558 1.317711 1.267965 1.265636</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [151,] 0.7516055 1.374872 1.385030 1.384628 1.380452 1.317610 1.267868 1.265539</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [152,] 0.7516631 1.374977 1.385136 1.384522 1.380346 1.317509 1.267771 1.265442</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [153,] 0.7517206 1.375082 1.385242 1.384416 1.380240 1.317408 1.267674 1.265345</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [154,] 0.7517782 1.375188 1.385348 1.384310 1.380135 1.317308 1.267576 1.265248</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [155,] 0.7518358 1.375293 1.385454 1.384204 1.380029 1.317207 1.267479 1.265151</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [156,] 0.7518933 1.375398 1.385560 1.384098 1.379923 1.317106 1.267382 1.265054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [157,] 0.7519509 1.375504 1.385666 1.383992 1.379818 1.317005 1.267285 1.264958</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [158,] 0.7520085 1.375609 1.385773 1.383886 1.379712 1.316904 1.267188 1.264861</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [159,] 0.7520661 1.375714 1.385879 1.383780 1.379606 1.316803 1.267091 1.264764</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [160,] 0.7521236 1.375820 1.385985 1.383675 1.379501 1.316702 1.266994 1.264667</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [161,] 0.7521812 1.375925 1.386091 1.383569 1.379395 1.316602 1.266897 1.264570</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [162,] 0.7522388 1.376030 1.386197 1.383463 1.379290 1.316501 1.266800 1.264473</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [163,] 0.7522964 1.376136 1.386303 1.383357 1.379184 1.316400 1.266703 1.264377</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [164,] 0.7523540 1.376241 1.386409 1.383251 1.379078 1.316299 1.266606 1.264280</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [165,] 0.7524116 1.376347 1.386516 1.383145 1.378973 1.316198 1.266509 1.264183</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [166,] 0.7524693 1.376452 1.386622 1.383039 1.378867 1.316098 1.266412 1.264086</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [167,] 0.7525269 1.376557 1.386728 1.382933 1.378762 1.315997 1.266315 1.263989</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [168,] 0.7525845 1.376663 1.386834 1.382827 1.378656 1.315896 1.266218 1.263893</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [169,] 0.7526421 1.376768 1.386940 1.382721 1.378551 1.315795 1.266121 1.263796</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [170,] 0.7526997 1.376874 1.387046 1.382615 1.378445 1.315695 1.266025 1.263699</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [171,] 0.7527574 1.376979 1.387153 1.382510 1.378339 1.315594 1.265928 1.263602</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [172,] 0.7528150 1.377084 1.387259 1.382404 1.378234 1.315493 1.265831 1.263506</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [173,] 0.7528727 1.377190 1.387365 1.382298 1.378128 1.315393 1.265734 1.263409</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [174,] 0.7529303 1.377295 1.387471 1.382192 1.378023 1.315292 1.265637 1.263312</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [175,] 0.7529880 1.377401 1.387578 1.382086 1.377917 1.315191 1.265540 1.263215</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [176,] 0.7530456 1.377506 1.387684 1.381980 1.377812 1.315090 1.265443 1.263119</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [177,] 0.7531033 1.377612 1.387790 1.381875 1.377706 1.314990 1.265346 1.263022</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [178,] 0.7531610 1.377717 1.387896 1.381769 1.377601 1.314889 1.265249 1.262925</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [179,] 0.7532186 1.377823 1.388003 1.381663 1.377495 1.314788 1.265152 1.262829</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [180,] 0.7532763 1.377928 1.388109 1.381557 1.377390 1.314688 1.265056 1.262732</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [181,] 0.7533340 1.378034 1.388215 1.381451 1.377285 1.314587 1.264959 1.262635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [182,] 0.7533917 1.378139 1.388321 1.381346 1.377179 1.314486 1.264862 1.262539</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [183,] 0.7534493 1.378245 1.388428 1.381240 1.377074 1.314386 1.264765 1.262442</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [184,] 0.7535070 1.378350 1.388534 1.381134 1.376968 1.314285 1.264668 1.262345</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [185,] 0.7535647 1.378456 1.388640 1.381028 1.376863 1.314184 1.264571 1.262249</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [186,] 0.7536224 1.378561 1.388747 1.380923 1.376757 1.314084 1.264474 1.262152</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [187,] 0.7536801 1.378667 1.388853 1.380817 1.376652 1.313983 1.264378 1.262055</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [188,] 0.7537379 1.378772 1.388959 1.380711 1.376547 1.313883 1.264281 1.261959</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [189,] 0.7537956 1.378878 1.389066 1.380606 1.376441 1.313782 1.264184 1.261862</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [190,] 0.7538533 1.378984 1.389172 1.380500 1.376336 1.313681 1.264087 1.261765</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [191,] 0.7539110 1.379089 1.389278 1.380394 1.376230 1.313581 1.263990 1.261669</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [192,] 0.7539687 1.379195 1.389385 1.380288 1.376125 1.313480 1.263894 1.261572</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [193,] 0.7540265 1.379300 1.389491 1.380183 1.376020 1.313380 1.263797 1.261476</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [194,] 0.7540842 1.379406 1.389598 1.380077 1.375914 1.313279 1.263700 1.261379</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [195,] 0.7541419 1.379512 1.389704 1.379971 1.375809 1.313179 1.263603 1.261282</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [196,] 0.7541997 1.379617 1.389810 1.379866 1.375704 1.313078 1.263507 1.261186</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [197,] 0.7542574 1.379723 1.389917 1.379760 1.375598 1.312978 1.263410 1.261089</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [198,] 0.7543152 1.379829 1.390023 1.379654 1.375493 1.312877 1.263313 1.260993</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [199,] 0.7543730 1.379934 1.390130 1.379549 1.375388 1.312776 1.263216 1.260896</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [200,] 0.7544307 1.380040 1.390236 1.379443 1.375282 1.312676 1.263120 1.260800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [201,] 0.7544885 1.380146 1.390250 1.379338 1.375177 1.312575 1.263023 1.260703</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [202,] 0.7545463 1.380251 1.390143 1.379232 1.375072 1.312475 1.262926 1.260607</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [203,] 0.7546040 1.380357 1.390037 1.379126 1.374966 1.312374 1.262830 1.260510</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [204,] 0.7546618 1.380463 1.389931 1.379021 1.374861 1.312274 1.262733 1.260414</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [205,] 0.7547196 1.380568 1.389824 1.378915 1.374756 1.312174 1.262636 1.260317</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [206,] 0.7547774 1.380674 1.389718 1.378810 1.374651 1.312073 1.262540 1.260221</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [207,] 0.7548352 1.380780 1.389611 1.378704 1.374545 1.311973 1.262443 1.260124</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [208,] 0.7548930 1.380885 1.389505 1.378598 1.374440 1.311872 1.262346 1.260028</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [209,] 0.7549508 1.380991 1.389399 1.378493 1.374335 1.311772 1.262250 1.259931</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [210,] 0.7550086 1.381097 1.389292 1.378387 1.374230 1.311671 1.262153 1.259835</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [211,] 0.7550664 1.381203 1.389186 1.378282 1.374124 1.311571 1.262056 1.259738</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [212,] 0.7551242 1.381308 1.389080 1.378176 1.374019 1.311470 1.261960 1.259642</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [213,] 0.7551820 1.381414 1.388973 1.378071 1.373914 1.311370 1.261863 1.259545</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [214,] 0.7552399 1.381520 1.388867 1.377965 1.373809 1.311270 1.261766 1.259449</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [215,] 0.7552977 1.381626 1.388760 1.377860 1.373704 1.311169 1.261670 1.259352</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [216,] 0.7553555 1.381732 1.388654 1.377754 1.373598 1.311069 1.261573 1.259256</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [217,] 0.7554134 1.381837 1.388548 1.377649 1.373493 1.310968 1.261477 1.259160</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [218,] 0.7554712 1.381943 1.388441 1.377543 1.373388 1.310868 1.261380 1.259063</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [219,] 0.7555290 1.382049 1.388335 1.377438 1.373283 1.310768 1.261284 1.258967</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [220,] 0.7555869 1.382155 1.388229 1.377332 1.373178 1.310667 1.261187 1.258870</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [221,] 0.7556448 1.382261 1.388123 1.377227 1.373073 1.310567 1.261090 1.258774</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [222,] 0.7557026 1.382367 1.388016 1.377121 1.372968 1.310467 1.260994 1.258678</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [223,] 0.7557605 1.382472 1.387910 1.377016 1.372862 1.310366 1.260897 1.258581</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [224,] 0.7558183 1.382578 1.387804 1.376911 1.372757 1.310266 1.260801 1.258485</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [225,] 0.7558762 1.382684 1.387698 1.376805 1.372652 1.310166 1.260704 1.258389</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [226,] 0.7559341 1.382790 1.387591 1.376700 1.372547 1.310065 1.260608 1.258292</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [227,] 0.7559920 1.382896 1.387485 1.376594 1.372442 1.309965 1.260511 1.258196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [228,] 0.7560499 1.383002 1.387379 1.376489 1.372337 1.309865 1.260415 1.258100</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [229,] 0.7561078 1.383108 1.387273 1.376384 1.372232 1.309764 1.260318 1.258003</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [230,] 0.7561657 1.383214 1.387166 1.376278 1.372127 1.309664 1.260222 1.257907</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [231,] 0.7562236 1.383319 1.387060 1.376173 1.372022 1.309564 1.260125 1.257811</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [232,] 0.7562815 1.383425 1.386954 1.376067 1.371917 1.309464 1.260029 1.257714</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [233,] 0.7563394 1.383531 1.386848 1.375962 1.371812 1.309363 1.259932 1.257618</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [234,] 0.7563973 1.383637 1.386742 1.375857 1.371707 1.309263 1.259836 1.257522</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [235,] 0.7564552 1.383743 1.386635 1.375751 1.371602 1.309163 1.259739 1.257425</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [236,] 0.7565131 1.383849 1.386529 1.375646 1.371497 1.309063 1.259643 1.257329</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [237,] 0.7565711 1.383955 1.386423 1.375541 1.371392 1.308962 1.259546 1.257233</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [238,] 0.7566290 1.384061 1.386317 1.375435 1.371287 1.308862 1.259450 1.257137</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [239,] 0.7566869 1.384167 1.386211 1.375330 1.371182 1.308762 1.259354 1.257040</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [240,] 0.7567449 1.384273 1.386105 1.375225 1.371077 1.308662 1.259257 1.256944</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [241,] 0.7568028 1.384379 1.385999 1.375120 1.370972 1.308562 1.259161 1.256848</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [242,] 0.7568608 1.384485 1.385892 1.375014 1.370867 1.308461 1.259064 1.256752</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [243,] 0.7569187 1.384591 1.385786 1.374909 1.370762 1.308361 1.258968 1.256655</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [244,] 0.7569767 1.384697 1.385680 1.374804 1.370657 1.308261 1.258871 1.256559</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [245,] 0.7570346 1.384803 1.385574 1.374698 1.370552 1.308161 1.258775 1.256463</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [246,] 0.7570926 1.384909 1.385468 1.374593 1.370447 1.308061 1.258679 1.256367</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [247,] 0.7571506 1.385015 1.385362 1.374488 1.370342 1.307961 1.258582 1.256271</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [248,] 0.7572085 1.385121 1.385256 1.374383 1.370237 1.307860 1.258486 1.256174</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [249,] 0.7572665 1.385227 1.385150 1.374277 1.370132 1.307760 1.258390 1.256078</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [250,] 0.7573245 1.385333 1.385044 1.374172 1.370027 1.307660 1.258293 1.255982</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [251,] 0.7573825 1.385439 1.384938 1.374067 1.369922 1.307560 1.258197 1.255886</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [252,] 0.7574405 1.385546 1.384832 1.373962 1.369817 1.307460 1.258101 1.255790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [253,] 0.7574985 1.385652 1.384726 1.373857 1.369713 1.307360 1.258004 1.255694</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [254,] 0.7575565 1.385758 1.384620 1.373751 1.369608 1.307260 1.257908 1.255598</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [255,] 0.7576145 1.385864 1.384514 1.373646 1.369503 1.307160 1.257812 1.255501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [256,] 0.7576725 1.385970 1.384408 1.373541 1.369398 1.307060 1.257715 1.255405</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [257,] 0.7577305 1.386076 1.384302 1.373436 1.369293 1.306959 1.257619 1.255309</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [258,] 0.7577885 1.386182 1.384196 1.373331 1.369188 1.306859 1.257523 1.255213</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [259,] 0.7578466 1.386288 1.384090 1.373226 1.369083 1.306759 1.257426 1.255117</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [260,] 0.7579046 1.386394 1.383984 1.373120 1.368979 1.306659 1.257330 1.255021</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [261,] 0.7579626 1.386501 1.383878 1.373015 1.368874 1.306559 1.257234 1.254925</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [262,] 0.7580207 1.386607 1.383772 1.372910 1.368769 1.306459 1.257138 1.254829</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [263,] 0.7580787 1.386713 1.383666 1.372805 1.368664 1.306359 1.257041 1.254733</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [264,] 0.7581368 1.386819 1.383560 1.372700 1.368559 1.306259 1.256945 1.254636</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [265,] 0.7581948 1.386925 1.383454 1.372595 1.368455 1.306159 1.256849 1.254540</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [266,] 0.7582529 1.387032 1.383348 1.372490 1.368350 1.306059 1.256753 1.254444</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [267,] 0.7583109 1.387138 1.383242 1.372385 1.368245 1.305959 1.256656 1.254348</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [268,] 0.7583690 1.387244 1.383136 1.372280 1.368140 1.305859 1.256560 1.254252</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [269,] 0.7584271 1.387350 1.383030 1.372175 1.368036 1.305759 1.256464 1.254156</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [270,] 0.7584851 1.387456 1.382924 1.372070 1.367931 1.305659 1.256368 1.254060</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [271,] 0.7585432 1.387563 1.382818 1.371964 1.367826 1.305559 1.256272 1.253964</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [272,] 0.7586013 1.387669 1.382713 1.371859 1.367721 1.305459 1.256175 1.253868</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [273,] 0.7586594 1.387775 1.382607 1.371754 1.367617 1.305359 1.256079 1.253772</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [274,] 0.7587175 1.387881 1.382501 1.371649 1.367512 1.305259 1.255983 1.253676</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [275,] 0.7587756 1.387988 1.382395 1.371544 1.367407 1.305159 1.255887 1.253580</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [276,] 0.7588337 1.388094 1.382289 1.371439 1.367303 1.305059 1.255791 1.253484</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [277,] 0.7588918 1.388200 1.382183 1.371334 1.367198 1.304960 1.255695 1.253388</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [278,] 0.7589499 1.388307 1.382078 1.371229 1.367093 1.304860 1.255599 1.253292</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [279,] 0.7590080 1.388413 1.381972 1.371124 1.366989 1.304760 1.255502 1.253196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [280,] 0.7590661 1.388519 1.381866 1.371019 1.366884 1.304660 1.255406 1.253100</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [281,] 0.7591242 1.388625 1.381760 1.370914 1.366779 1.304560 1.255310 1.253004</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [282,] 0.7591824 1.388732 1.381654 1.370809 1.366675 1.304460 1.255214 1.252909</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [283,] 0.7592405 1.388838 1.381549 1.370704 1.366570 1.304360 1.255118 1.252813</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [284,] 0.7592986 1.388944 1.381443 1.370600 1.366465 1.304260 1.255022 1.252717</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [285,] 0.7593568 1.389051 1.381337 1.370495 1.366361 1.304160 1.254926 1.252621</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [286,] 0.7594149 1.389157 1.381231 1.370390 1.366256 1.304061 1.254830 1.252525</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [287,] 0.7594731 1.389264 1.381125 1.370285 1.366151 1.303961 1.254734 1.252429</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [288,] 0.7595312 1.389370 1.381020 1.370180 1.366047 1.303861 1.254638 1.252333</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [289,] 0.7595894 1.389476 1.380914 1.370075 1.365942 1.303761 1.254541 1.252237</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [290,] 0.7596475 1.389583 1.380808 1.369970 1.365838 1.303661 1.254445 1.252141</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [291,] 0.7597057 1.389689 1.380703 1.369865 1.365733 1.303561 1.254349 1.252045</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [292,] 0.7597639 1.389796 1.380597 1.369760 1.365629 1.303462 1.254253 1.251950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [293,] 0.7598220 1.389902 1.380491 1.369655 1.365524 1.303362 1.254157 1.251854</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [294,] 0.7598802 1.390008 1.380385 1.369550 1.365419 1.303262 1.254061 1.251758</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [295,] 0.7599384 1.390115 1.380280 1.369446 1.365315 1.303162 1.253965 1.251662</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [296,] 0.7599966 1.390221 1.380174 1.369341 1.365210 1.303063 1.253869 1.251566</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [297,] 0.7600548 1.390265 1.380068 1.369236 1.365106 1.302963 1.253773 1.251470</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [298,] 0.7601130 1.390158 1.379963 1.369131 1.365001 1.302863 1.253677 1.251375</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [299,] 0.7601712 1.390052 1.379857 1.369026 1.364897 1.302763 1.253581 1.251279</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [300,] 0.7602294 1.389946 1.379751 1.368921 1.364792 1.302663 1.253485 1.251183</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [301,] 0.7602876 1.389839 1.379646 1.368817 1.364688 1.302564 1.253389 1.251087</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [302,] 0.7603458 1.389733 1.379540 1.368712 1.364583 1.302464 1.253293 1.250991</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [303,] 0.7604040 1.389626 1.379434 1.368607 1.364479 1.302364 1.253197 1.250896</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [304,] 0.7604623 1.389520 1.379329 1.368502 1.364374 1.302265 1.253101 1.250800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [305,] 0.7605205 1.389414 1.379223 1.368397 1.364270 1.302165 1.253005 1.250704</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [306,] 0.7605787 1.389307 1.379118 1.368293 1.364165 1.302065 1.252910 1.250608</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [307,] 0.7606370 1.389201 1.379012 1.368188 1.364061 1.301965 1.252814 1.250512</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [308,] 0.7606952 1.389094 1.378906 1.368083 1.363957 1.301866 1.252718 1.250417</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [309,] 0.7608117 1.388882 1.378695 1.367874 1.363748 1.301666 1.252526 1.250225</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [310,] 0.7608700 1.388775 1.378590 1.367769 1.363643 1.301567 1.252430 1.250130</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [311,] 0.7609282 1.388669 1.378484 1.367664 1.363539 1.301467 1.252334 1.250034</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [312,] 0.7609865 1.388563 1.378379 1.367560 1.363434 1.301367 1.252238 1.249938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [313,] 0.7610448 1.388456 1.378273 1.367455 1.363330 1.301268 1.252142 1.249842</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [314,] 0.7611030 1.388350 1.378168 1.367350 1.363226 1.301168 1.252046 1.249747</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [315,] 0.7611613 1.388244 1.378062 1.367245 1.363121 1.301069 1.251951 1.249651</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [316,] 0.7612196 1.388138 1.377957 1.367141 1.363017 1.300969 1.251855 1.249555</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [317,] 0.7612779 1.388031 1.377851 1.367036 1.362913 1.300869 1.251759 1.249460</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [318,] 0.7607535 1.388988 1.378801 1.367978 1.363852 1.301766 1.252622 1.250321</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [319,] 0.7145846 1.307151 1.316809 1.327227 1.331242 1.385872 1.333553 1.331104</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [320,] 0.7146393 1.307252 1.316910 1.327328 1.331344 1.385766 1.333451 1.331002</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [321,] 0.7146940 1.307352 1.317011 1.327430 1.331446 1.385660 1.333349 1.330900</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [322,] 0.7145298 1.307051 1.316708 1.327125 1.331140 1.385979 1.333655 1.331205</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [323,] 0.7136550 1.305451 1.315096 1.325500 1.329511 1.387678 1.335290 1.332837</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [324,] 0.7137097 1.305551 1.315197 1.325602 1.329612 1.387571 1.335188 1.332735</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [325,] 0.7137643 1.305651 1.315298 1.325703 1.329714 1.387465 1.335085 1.332633</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [326,] 0.7138190 1.305751 1.315398 1.325805 1.329816 1.387359 1.334983 1.332531</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [327,] 0.7138736 1.305851 1.315499 1.325906 1.329918 1.387253 1.334881 1.332429</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [328,] 0.7139283 1.305951 1.315600 1.326008 1.330020 1.387146 1.334779 1.332327</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [329,] 0.7139829 1.306051 1.315701 1.326109 1.330122 1.387040 1.334677 1.332225</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [330,] 0.7144204 1.306851 1.316507 1.326922 1.330937 1.386191 1.333859 1.331409</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [331,] 0.7144751 1.306951 1.316608 1.327024 1.331039 1.386085 1.333757 1.331307</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [332,] 0.7133819 1.304951 1.314593 1.324993 1.329002 1.388209 1.335801 1.333348</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [333,] 0.7134365 1.305051 1.314694 1.325095 1.329104 1.388103 1.335699 1.333246</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [334,] 0.7134911 1.305151 1.314794 1.325196 1.329205 1.387996 1.335597 1.333143</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [335,] 0.7136004 1.305351 1.314996 1.325399 1.329409 1.387784 1.335392 1.332939</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [336,] 0.7140376 1.306151 1.315801 1.326211 1.330223 1.386934 1.334574 1.332123</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [337,] 0.7140923 1.306251 1.315902 1.326313 1.330325 1.386828 1.334472 1.332021</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [338,] 0.7141470 1.306351 1.316003 1.326414 1.330427 1.386722 1.334370 1.331919</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [339,] 0.7142017 1.306451 1.316104 1.326516 1.330529 1.386615 1.334268 1.331817</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [340,] 0.7142563 1.306551 1.316204 1.326617 1.330631 1.386509 1.334166 1.331715</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [341,] 0.7143110 1.306651 1.316305 1.326719 1.330733 1.386403 1.334064 1.331613</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [342,] 0.7143657 1.306751 1.316406 1.326820 1.330835 1.386297 1.333961 1.331511</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [343,] 0.7135457 1.305251 1.314895 1.325297 1.329307 1.387890 1.335494 1.333041</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [344,] 0.7116658 1.301812 1.311431 1.321806 1.325805 1.389037 1.339022 1.336563</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [345,] 0.7117748 1.302012 1.311632 1.322008 1.326008 1.389250 1.338817 1.336358</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [346,] 0.7118293 1.302111 1.311732 1.322109 1.326110 1.389357 1.338715 1.336256</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [347,] 0.7118838 1.302211 1.311832 1.322211 1.326211 1.389463 1.338612 1.336153</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [348,] 0.7117203 1.301912 1.311531 1.321907 1.325906 1.389144 1.338920 1.336460</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [349,] 0.7119929 1.302411 1.312033 1.322413 1.326414 1.389676 1.338407 1.335949</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [350,] 0.7120474 1.302510 1.312134 1.322514 1.326516 1.389782 1.338305 1.335847</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [351,] 0.7121019 1.302610 1.312234 1.322616 1.326617 1.389889 1.338202 1.335744</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [352,] 0.7121564 1.302710 1.312335 1.322717 1.326719 1.389995 1.338100 1.335642</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [353,] 0.7122110 1.302810 1.312435 1.322818 1.326820 1.390101 1.337997 1.335540</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [354,] 0.7122655 1.302909 1.312536 1.322920 1.326922 1.390208 1.337895 1.335437</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [355,] 0.7123200 1.303009 1.312636 1.323021 1.327024 1.390278 1.337792 1.335335</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [356,] 0.7123746 1.303109 1.312737 1.323122 1.327125 1.390172 1.337690 1.335233</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [357,] 0.7124291 1.303209 1.312837 1.323224 1.327227 1.390065 1.337588 1.335131</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [358,] 0.7124837 1.303308 1.312938 1.323325 1.327329 1.389959 1.337485 1.335029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [359,] 0.7125382 1.303408 1.313038 1.323426 1.327430 1.389853 1.337383 1.334926</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [360,] 0.7125928 1.303508 1.313139 1.323527 1.327532 1.389746 1.337280 1.334824</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [361,] 0.7119384 1.302311 1.311933 1.322312 1.326313 1.389569 1.338510 1.336051</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [362,] 0.7112301 1.301015 1.310628 1.320996 1.324993 1.388187 1.339843 1.337382</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [363,] 0.7112845 1.301115 1.310728 1.321098 1.325095 1.388293 1.339740 1.337279</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [364,] 0.7113390 1.301214 1.310828 1.321199 1.325196 1.388399 1.339637 1.337177</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [365,] 0.7113934 1.301314 1.310929 1.321300 1.325297 1.388506 1.339535 1.337074</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [366,] 0.7114479 1.301414 1.311029 1.321401 1.325399 1.388612 1.339432 1.336972</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [367,] 0.7115024 1.301513 1.311130 1.321502 1.325500 1.388718 1.339330 1.336870</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [368,] 0.7115569 1.301613 1.311230 1.321603 1.325602 1.388825 1.339227 1.336767</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [369,] 0.7116114 1.301713 1.311330 1.321705 1.325703 1.388931 1.339125 1.336665</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [370,] 0.7130840 1.304407 1.314044 1.324440 1.328447 1.388789 1.336359 1.333905</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [371,] 0.7131386 1.304506 1.314145 1.324541 1.328549 1.388682 1.336257 1.333802</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [372,] 0.7131932 1.304606 1.314245 1.324643 1.328650 1.388576 1.336155 1.333700</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [373,] 0.7132478 1.304706 1.314346 1.324744 1.328752 1.388470 1.336052 1.333598</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [374,] 0.7133024 1.304806 1.314447 1.324846 1.328854 1.388363 1.335950 1.333496</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [375,] 0.7126474 1.303608 1.313239 1.323629 1.327633 1.389640 1.337178 1.334722</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [376,] 0.7127019 1.303708 1.313340 1.323730 1.327735 1.389533 1.337076 1.334620</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [377,] 0.7127565 1.303807 1.313441 1.323832 1.327837 1.389427 1.336973 1.334518</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [378,] 0.7128111 1.303907 1.313541 1.323933 1.327938 1.389321 1.336871 1.334415</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [379,] 0.7128657 1.304007 1.313642 1.324034 1.328040 1.389214 1.336768 1.334313</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [380,] 0.7129202 1.304107 1.313742 1.324136 1.328142 1.389108 1.336666 1.334211</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [381,] 0.7129748 1.304207 1.313843 1.324237 1.328244 1.389001 1.336564 1.334109</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [382,] 0.7130294 1.304307 1.313943 1.324338 1.328345 1.388895 1.336461 1.334007</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [383,] 0.6807122 1.245191 1.254390 1.264314 1.268140 1.328622 1.380748 1.383289</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [384,] 0.6807643 1.245286 1.254487 1.264411 1.268237 1.328723 1.380854 1.383394</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [385,] 0.6808164 1.245381 1.254583 1.264508 1.268334 1.328825 1.380959 1.383500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [386,] 0.6808686 1.245477 1.254679 1.264605 1.268431 1.328927 1.381065 1.383606</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [387,] 0.6809207 1.245572 1.254775 1.264702 1.268528 1.329029 1.381171 1.383712</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [388,] 0.6844228 1.251978 1.261228 1.271206 1.275052 1.335864 1.388274 1.389764</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [389,] 0.6844752 1.252074 1.261325 1.271304 1.275150 1.335967 1.388381 1.389657</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [390,] 0.6845277 1.252170 1.261421 1.271401 1.275248 1.336069 1.388487 1.389551</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [391,] 0.6845801 1.252266 1.261518 1.271498 1.275345 1.336171 1.388593 1.389445</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [392,] 0.6846325 1.252362 1.261615 1.271596 1.275443 1.336273 1.388700 1.389338</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [393,] 0.6846849 1.252458 1.261711 1.271693 1.275541 1.336376 1.388806 1.389232</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [394,] 0.6847373 1.252554 1.261808 1.271790 1.275638 1.336478 1.388912 1.389125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [395,] 0.6847898 1.252649 1.261904 1.271888 1.275736 1.336580 1.389019 1.389019</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [396,] 0.6848422 1.252745 1.262001 1.271985 1.275834 1.336683 1.389125 1.388913</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [397,] 0.6848946 1.252841 1.262098 1.272083 1.275931 1.336785 1.389231 1.388806</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [398,] 0.6849471 1.252937 1.262194 1.272180 1.276029 1.336887 1.389338 1.388700</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [399,] 0.6849995 1.253033 1.262291 1.272277 1.276127 1.336990 1.389444 1.388594</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [400,] 0.6850520 1.253129 1.262388 1.272375 1.276224 1.337092 1.389551 1.388487</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [401,] 0.6802433 1.244333 1.253526 1.263443 1.267266 1.327706 1.379797 1.382336</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [402,] 0.6802954 1.244428 1.253622 1.263540 1.267363 1.327808 1.379902 1.382442</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [403,] 0.6803474 1.244523 1.253718 1.263637 1.267460 1.327910 1.380008 1.382547</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [404,] 0.6803995 1.244619 1.253814 1.263734 1.267557 1.328012 1.380114 1.382653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [405,] 0.6804516 1.244714 1.253910 1.263830 1.267654 1.328113 1.380219 1.382759</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [406,] 0.6805037 1.244809 1.254006 1.263927 1.267751 1.328215 1.380325 1.382865</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [407,] 0.6805558 1.244905 1.254102 1.264024 1.267848 1.328317 1.380431 1.382971</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [408,] 0.6806080 1.245000 1.254198 1.264121 1.267945 1.328418 1.380536 1.383077</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [409,] 0.6806601 1.245095 1.254294 1.264218 1.268042 1.328520 1.380642 1.383183</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [410,] 0.6862595 1.255338 1.264613 1.274618 1.278474 1.339449 1.388595 1.386044</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [411,] 0.6863120 1.255434 1.264710 1.274715 1.278572 1.339552 1.388489 1.385938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [412,] 0.6863646 1.255530 1.264806 1.274813 1.278670 1.339654 1.388382 1.385832</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [413,] 0.6864171 1.255626 1.264903 1.274910 1.278768 1.339757 1.388276 1.385726</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [414,] 0.6864697 1.255722 1.265000 1.275008 1.278865 1.339859 1.388170 1.385620</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [415,] 0.6865222 1.255819 1.265097 1.275106 1.278963 1.339962 1.388063 1.385514</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [416,] 0.6865748 1.255915 1.265194 1.275203 1.279061 1.340064 1.387957 1.385408</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [417,] 0.6866274 1.256011 1.265291 1.275301 1.279159 1.340167 1.387851 1.385302</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [418,] 0.6866800 1.256107 1.265388 1.275399 1.279257 1.340270 1.387745 1.385196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [419,] 0.6867325 1.256203 1.265485 1.275496 1.279355 1.340372 1.387638 1.385090</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [420,] 0.6867851 1.256299 1.265581 1.275594 1.279453 1.340475 1.387532 1.384983</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [421,] 0.6868377 1.256396 1.265678 1.275692 1.279551 1.340578 1.387426 1.384877</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [422,] 0.6868903 1.256492 1.265775 1.275789 1.279649 1.340680 1.387320 1.384771</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [423,] 0.6869429 1.256588 1.265872 1.275887 1.279747 1.340783 1.387213 1.384665</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [424,] 0.6869955 1.256684 1.265969 1.275985 1.279845 1.340886 1.387107 1.384559</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [425,] 0.6870481 1.256780 1.266066 1.276082 1.279943 1.340988 1.387001 1.384453</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [426,] 0.6871007 1.256877 1.266163 1.276180 1.280041 1.341091 1.386895 1.384347</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [427,] 0.6871533 1.256973 1.266260 1.276278 1.280139 1.341194 1.386789 1.384241</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [428,] 0.6872059 1.257069 1.266357 1.276375 1.280237 1.341296 1.386682 1.384135</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [429,] 0.6872585 1.257165 1.266454 1.276473 1.280335 1.341399 1.386576 1.384029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [430,] 0.6873112 1.257262 1.266551 1.276571 1.280433 1.341502 1.386470 1.383923</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [431,] 0.6873638 1.257358 1.266648 1.276669 1.280531 1.341604 1.386364 1.383817</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [432,] 0.6874164 1.257454 1.266745 1.276766 1.280629 1.341707 1.386258 1.383712</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [433,] 0.6874691 1.257551 1.266842 1.276864 1.280727 1.341810 1.386152 1.383606</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [434,] 0.6875217 1.257647 1.266939 1.276962 1.280825 1.341913 1.386045 1.383500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [435,] 0.6875744 1.257743 1.267036 1.277060 1.280923 1.342015 1.385939 1.383394</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [436,] 0.6876270 1.257839 1.267133 1.277158 1.281022 1.342118 1.385833 1.383288</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [437,] 0.6876797 1.257936 1.267230 1.277255 1.281120 1.342221 1.385727 1.383182</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [438,] 0.6877323 1.258032 1.267327 1.277353 1.281218 1.342324 1.385621 1.383076</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [439,] 0.6877850 1.258128 1.267424 1.277451 1.281316 1.342426 1.385515 1.382970</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [440,] 0.6878376 1.258225 1.267521 1.277549 1.281414 1.342529 1.385409 1.382864</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [441,] 0.6878903 1.258321 1.267618 1.277647 1.281512 1.342632 1.385303 1.382758</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [442,] 0.6879430 1.258417 1.267715 1.277744 1.281610 1.342735 1.385197 1.382652</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [443,] 0.6879956 1.258514 1.267812 1.277842 1.281708 1.342838 1.385091 1.382547</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [444,] 0.6880483 1.258610 1.267909 1.277940 1.281806 1.342941 1.384985 1.382441</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [445,] 0.6881010 1.258707 1.268006 1.278038 1.281905 1.343043 1.384879 1.382335</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [446,] 0.6881537 1.258803 1.268103 1.278136 1.282003 1.343146 1.384773 1.382229</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [447,] 0.6882064 1.258899 1.268201 1.278234 1.282101 1.343249 1.384666 1.382123</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [448,] 0.6882591 1.258996 1.268298 1.278332 1.282199 1.343352 1.384560 1.382017</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [449,] 0.6883118 1.259092 1.268395 1.278429 1.282297 1.343455 1.384454 1.381912</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [450,] 0.6883645 1.259188 1.268492 1.278527 1.282395 1.343558 1.384348 1.381806</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [451,] 0.6884172 1.259285 1.268589 1.278625 1.282494 1.343660 1.384242 1.381700</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [452,] 0.6884699 1.259381 1.268686 1.278723 1.282592 1.343763 1.384136 1.381594</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [453,] 0.6885226 1.259478 1.268783 1.278821 1.282690 1.343866 1.384031 1.381488</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [454,] 0.6885754 1.259574 1.268880 1.278919 1.282788 1.343969 1.383925 1.381383</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [455,] 0.6886281 1.259671 1.268978 1.279017 1.282887 1.344072 1.383819 1.381277</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [456,] 0.6886808 1.259767 1.269075 1.279115 1.282985 1.344175 1.383713 1.381171</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [457,] 0.6887335 1.259864 1.269172 1.279213 1.283083 1.344278 1.383607 1.381065</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [458,] 0.6887863 1.259960 1.269269 1.279311 1.283181 1.344381 1.383501 1.380960</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [459,] 0.6888390 1.260056 1.269366 1.279409 1.283279 1.344484 1.383395 1.380854</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [460,] 0.6835849 1.250445 1.259684 1.269650 1.273491 1.334229 1.386575 1.389126</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [461,] 0.6836372 1.250541 1.259781 1.269747 1.273589 1.334331 1.386681 1.389233</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [462,] 0.6836896 1.250637 1.259877 1.269844 1.273686 1.334433 1.386787 1.389339</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [463,] 0.6837419 1.250733 1.259974 1.269942 1.273784 1.334535 1.386893 1.389445</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [464,] 0.6837943 1.250828 1.260070 1.270039 1.273881 1.334637 1.386999 1.389552</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [465,] 0.6838466 1.250924 1.260167 1.270136 1.273979 1.334740 1.387106 1.389658</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [466,] 0.6838990 1.251020 1.260263 1.270233 1.274076 1.334842 1.387212 1.389765</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [467,] 0.6839514 1.251116 1.260360 1.270331 1.274174 1.334944 1.387318 1.389871</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [468,] 0.6888918 1.260153 1.269464 1.279507 1.283378 1.344587 1.383289 1.380748</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [469,] 0.6889445 1.260249 1.269561 1.279605 1.283476 1.344690 1.383183 1.380642</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [470,] 0.6889973 1.260346 1.269658 1.279703 1.283574 1.344793 1.383077 1.380537</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [471,] 0.6841609 1.251499 1.260746 1.270720 1.274564 1.335353 1.387743 1.390296</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [472,] 0.6842132 1.251595 1.260842 1.270817 1.274662 1.335455 1.387849 1.390189</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [473,] 0.6842656 1.251691 1.260939 1.270914 1.274759 1.335557 1.387956 1.390083</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [474,] 0.6843180 1.251786 1.261035 1.271012 1.274857 1.335660 1.388062 1.389977</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [475,] 0.6843704 1.251882 1.261132 1.271109 1.274955 1.335762 1.388168 1.389870</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [476,] 0.6890500 1.260442 1.269755 1.279801 1.283673 1.344896 1.382971 1.380431</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [477,] 0.6891028 1.260539 1.269852 1.279899 1.283771 1.344999 1.382865 1.380325</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [478,] 0.6891555 1.260636 1.269950 1.279997 1.283869 1.345102 1.382759 1.380220</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [479,] 0.6892083 1.260732 1.270047 1.280095 1.283967 1.345205 1.382654 1.380114</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [480,] 0.6892611 1.260829 1.270144 1.280193 1.284066 1.345308 1.382548 1.380008</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [481,] 0.6893139 1.260925 1.270241 1.280291 1.284164 1.345411 1.382442 1.379903</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [482,] 0.6893666 1.261022 1.270339 1.280389 1.284262 1.345514 1.382336 1.379797</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [483,] 0.6894194 1.261118 1.270436 1.280487 1.284361 1.345617 1.382230 1.379691</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [484,] 0.6894722 1.261215 1.270533 1.280585 1.284459 1.345720 1.382124 1.379586</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [485,] 0.6895250 1.261311 1.270630 1.280683 1.284557 1.345823 1.382019 1.379480</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [486,] 0.6895778 1.261408 1.270728 1.280781 1.284656 1.345926 1.381913 1.379374</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [487,] 0.6896306 1.261505 1.270825 1.280879 1.284754 1.346029 1.381807 1.379269</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [488,] 0.6896834 1.261601 1.270922 1.280977 1.284853 1.346132 1.381701 1.379163</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [489,] 0.6897362 1.261698 1.271020 1.281075 1.284951 1.346235 1.381595 1.379058</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [490,] 0.6897890 1.261794 1.271117 1.281173 1.285049 1.346338 1.381490 1.378952</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [491,] 0.6898419 1.261891 1.271214 1.281271 1.285148 1.346441 1.381384 1.378846</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [492,] 0.6898947 1.261988 1.271312 1.281369 1.285246 1.346544 1.381278 1.378741</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [493,] 0.6899475 1.262084 1.271409 1.281467 1.285345 1.346647 1.381172 1.378635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [494,] 0.6900003 1.262181 1.271506 1.281566 1.285443 1.346750 1.381066 1.378530</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [495,] 0.6811814 1.246049 1.255255 1.265186 1.269014 1.329538 1.381700 1.384242</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [496,] 0.6812336 1.246144 1.255351 1.265283 1.269111 1.329639 1.381805 1.384348</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [497,] 0.6812858 1.246240 1.255447 1.265380 1.269208 1.329741 1.381911 1.384454</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [498,] 0.6813379 1.246335 1.255544 1.265477 1.269305 1.329843 1.382017 1.384560</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [499,] 0.6813901 1.246431 1.255640 1.265573 1.269402 1.329945 1.382123 1.384666</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [500,] 0.6814423 1.246526 1.255736 1.265670 1.269500 1.330047 1.382229 1.384772</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [501,] 0.6814945 1.246621 1.255832 1.265767 1.269597 1.330149 1.382335 1.384878</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [502,] 0.6815466 1.246717 1.255928 1.265864 1.269694 1.330250 1.382440 1.384984</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [503,] 0.6815988 1.246812 1.256024 1.265961 1.269791 1.330352 1.382546 1.385090</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [504,] 0.6816510 1.246908 1.256121 1.266058 1.269889 1.330454 1.382652 1.385196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [505,] 0.6817032 1.247003 1.256217 1.266155 1.269986 1.330556 1.382758 1.385302</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [506,] 0.6817554 1.247099 1.256313 1.266252 1.270083 1.330658 1.382864 1.385409</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [507,] 0.6818076 1.247194 1.256409 1.266349 1.270180 1.330760 1.382970 1.385515</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [508,] 0.6818598 1.247290 1.256505 1.266446 1.270278 1.330862 1.383076 1.385621</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [509,] 0.6851044 1.253225 1.262484 1.272472 1.276322 1.337195 1.389657 1.388381</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [510,] 0.6851569 1.253321 1.262581 1.272570 1.276420 1.337297 1.389763 1.388275</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [511,] 0.6852094 1.253417 1.262678 1.272667 1.276518 1.337399 1.389870 1.388168</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [512,] 0.6852618 1.253513 1.262774 1.272765 1.276615 1.337502 1.389976 1.388062</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [513,] 0.6853143 1.253609 1.262871 1.272862 1.276713 1.337604 1.390083 1.387956</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [514,] 0.6853668 1.253705 1.262968 1.272959 1.276811 1.337707 1.390189 1.387850</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [515,] 0.6854192 1.253801 1.263064 1.273057 1.276909 1.337809 1.390296 1.387743</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [516,] 0.6854717 1.253897 1.263161 1.273154 1.277006 1.337911 1.390191 1.387637</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [517,] 0.6855242 1.253993 1.263258 1.273252 1.277104 1.338014 1.390084 1.387531</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [518,] 0.6855767 1.254089 1.263355 1.273349 1.277202 1.338116 1.389978 1.387425</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [519,] 0.6856292 1.254185 1.263451 1.273447 1.277300 1.338219 1.389871 1.387318</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [520,] 0.6856817 1.254281 1.263548 1.273544 1.277398 1.338321 1.389765 1.387212</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [521,] 0.6857342 1.254377 1.263645 1.273642 1.277495 1.338424 1.389658 1.387106</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [522,] 0.6857867 1.254473 1.263742 1.273739 1.277593 1.338526 1.389552 1.387000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [523,] 0.6858392 1.254569 1.263838 1.273837 1.277691 1.338629 1.389446 1.386894</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [524,] 0.6858917 1.254665 1.263935 1.273935 1.277789 1.338731 1.389339 1.386787</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [525,] 0.6859443 1.254761 1.264032 1.274032 1.277887 1.338834 1.389233 1.386681</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [526,] 0.6859968 1.254857 1.264129 1.274130 1.277985 1.338936 1.389127 1.386575</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [527,] 0.6860493 1.254953 1.264226 1.274227 1.278082 1.339039 1.389020 1.386469</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [528,] 0.6861018 1.255050 1.264322 1.274325 1.278180 1.339141 1.388914 1.386363</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [529,] 0.6861544 1.255146 1.264419 1.274422 1.278278 1.339244 1.388807 1.386257</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [530,] 0.6862069 1.255242 1.264516 1.274520 1.278376 1.339346 1.388701 1.386150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [531,] 0.6809728 1.245667 1.254871 1.264798 1.268625 1.329130 1.381277 1.383818</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [532,] 0.6810250 1.245763 1.254967 1.264895 1.268722 1.329232 1.381382 1.383924</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [533,] 0.6810771 1.245858 1.255063 1.264992 1.268819 1.329334 1.381488 1.384030</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [534,] 0.6811293 1.245953 1.255159 1.265089 1.268917 1.329436 1.381594 1.384136</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [535,] 0.6832709 1.249871 1.259106 1.269067 1.272906 1.333616 1.385938 1.388488</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [536,] 0.6833232 1.249967 1.259202 1.269164 1.273004 1.333718 1.386044 1.388594</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [537,] 0.6833755 1.250062 1.259298 1.269261 1.273101 1.333820 1.386150 1.388701</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [538,] 0.6834279 1.250158 1.259395 1.269358 1.273199 1.333922 1.386256 1.388807</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [539,] 0.6834802 1.250254 1.259491 1.269455 1.273296 1.334024 1.386362 1.388913</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [540,] 0.6835325 1.250350 1.259588 1.269553 1.273394 1.334127 1.386469 1.389020</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [541,] 0.6821731 1.247863 1.257083 1.267028 1.270861 1.331473 1.383711 1.386257</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [542,] 0.6822254 1.247959 1.257179 1.267125 1.270959 1.331575 1.383817 1.386364</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [543,] 0.6822776 1.248054 1.257275 1.267222 1.271056 1.331677 1.383923 1.386470</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [544,] 0.6823299 1.248150 1.257371 1.267319 1.271153 1.331779 1.384029 1.386576</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [545,] 0.6823821 1.248245 1.257468 1.267416 1.271250 1.331881 1.384135 1.386682</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [546,] 0.6824343 1.248341 1.257564 1.267513 1.271348 1.331983 1.384241 1.386788</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [547,] 0.6824866 1.248436 1.257660 1.267610 1.271445 1.332085 1.384347 1.386894</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [548,] 0.6825389 1.248532 1.257757 1.267707 1.271543 1.332187 1.384453 1.387001</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [549,] 0.6819120 1.247385 1.256602 1.266543 1.270375 1.330964 1.383182 1.385727</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [550,] 0.6819642 1.247481 1.256698 1.266640 1.270472 1.331066 1.383287 1.385833</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [551,] 0.6820165 1.247576 1.256794 1.266737 1.270569 1.331167 1.383393 1.385939</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [552,] 0.6820687 1.247672 1.256890 1.266834 1.270667 1.331269 1.383499 1.386045</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [553,] 0.6821209 1.247767 1.256986 1.266931 1.270764 1.331371 1.383605 1.386151</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [554,] 0.6828525 1.249106 1.258335 1.268290 1.272127 1.332799 1.385089 1.387638</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [555,] 0.6829048 1.249201 1.258431 1.268387 1.272224 1.332901 1.385195 1.387744</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [556,] 0.6829571 1.249297 1.258527 1.268484 1.272322 1.333003 1.385301 1.387850</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [557,] 0.6830094 1.249393 1.258624 1.268581 1.272419 1.333105 1.385407 1.387957</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [558,] 0.6830617 1.249488 1.258720 1.268678 1.272517 1.333207 1.385513 1.388063</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [559,] 0.6831140 1.249584 1.258816 1.268775 1.272614 1.333310 1.385620 1.388169</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [560,] 0.6831663 1.249680 1.258913 1.268872 1.272711 1.333412 1.385726 1.388276</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [561,] 0.6832186 1.249775 1.259009 1.268970 1.272809 1.333514 1.385832 1.388382</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [562,] 0.6825911 1.248628 1.257853 1.267804 1.271640 1.332289 1.384559 1.387107</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [563,] 0.6826434 1.248723 1.257949 1.267901 1.271737 1.332391 1.384665 1.387213</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [564,] 0.6826957 1.248819 1.258046 1.267998 1.271835 1.332493 1.384771 1.387319</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [565,] 0.6827479 1.248914 1.258142 1.268095 1.271932 1.332595 1.384877 1.387425</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [566,] 0.6828002 1.249010 1.258238 1.268193 1.272029 1.332697 1.384983 1.387532</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [567,] 0.6840037 1.251212 1.260456 1.270428 1.274272 1.335046 1.387424 1.389977</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [568,] 0.6840561 1.251307 1.260553 1.270525 1.274369 1.335148 1.387531 1.390084</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [569,] 0.6841085 1.251403 1.260649 1.270622 1.274467 1.335251 1.387637 1.390190</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [570,] 0.6080655 1.112302 1.120520 1.129385 1.132802 1.186829 1.233392 1.235662</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [571,] 0.6082518 1.112643 1.120863 1.129731 1.133149 1.187193 1.233770 1.236040</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [572,] 0.6081121 1.112387 1.120606 1.129471 1.132888 1.186920 1.233487 1.235756</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [573,] 0.6081586 1.112472 1.120692 1.129558 1.132975 1.187011 1.233581 1.235851</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [574,] 0.6082052 1.112557 1.120777 1.129644 1.133062 1.187102 1.233676 1.235946</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [575,] 0.6077862 1.111791 1.120005 1.128866 1.132281 1.186284 1.232826 1.235094</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [576,] 0.6082983 1.112728 1.120949 1.129817 1.133235 1.187284 1.233864 1.236135</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [577,] 0.6083449 1.112813 1.121035 1.129904 1.133322 1.187374 1.233959 1.236230</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [578,] 0.6083915 1.112898 1.121121 1.129990 1.133409 1.187465 1.234053 1.236324</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [579,] 0.6079724 1.112131 1.120348 1.129212 1.132628 1.186647 1.233203 1.235473</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [580,] 0.6080189 1.112217 1.120434 1.129298 1.132715 1.186738 1.233298 1.235567</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [581,] 0.6096039 1.115116 1.123355 1.132242 1.135668 1.189832 1.236513 1.238788</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [582,] 0.6096505 1.115201 1.123441 1.132329 1.135755 1.189923 1.236607 1.238883</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [583,] 0.6087177 1.113495 1.121722 1.130596 1.134017 1.188102 1.234715 1.236987</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [584,] 0.6087643 1.113580 1.121808 1.130683 1.134103 1.188193 1.234810 1.237082</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [585,] 0.6088109 1.113665 1.121894 1.130769 1.134190 1.188284 1.234904 1.237177</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [586,] 0.6088575 1.113751 1.121979 1.130856 1.134277 1.188375 1.234999 1.237271</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [587,] 0.6089041 1.113836 1.122065 1.130942 1.134364 1.188466 1.235093 1.237366</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [588,] 0.6089508 1.113921 1.122151 1.131029 1.134451 1.188557 1.235188 1.237461</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [589,] 0.6078328 1.111876 1.120091 1.128952 1.132368 1.186375 1.232920 1.235189</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [590,] 0.6078793 1.111961 1.120177 1.129039 1.132455 1.186466 1.233015 1.235283</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [591,] 0.6079258 1.112046 1.120263 1.129125 1.132541 1.186557 1.233109 1.235378</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [592,] 0.6085313 1.113154 1.121378 1.130250 1.133669 1.187738 1.234337 1.236608</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [593,] 0.6085779 1.113239 1.121464 1.130336 1.133756 1.187829 1.234431 1.236703</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [594,] 0.6086245 1.113324 1.121550 1.130423 1.133843 1.187920 1.234526 1.236798</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [595,] 0.6086711 1.113410 1.121636 1.130509 1.133930 1.188011 1.234621 1.236892</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [596,] 0.6093239 1.114604 1.122839 1.131722 1.135146 1.189285 1.235945 1.238219</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [597,] 0.6093705 1.114689 1.122925 1.131809 1.135233 1.189376 1.236039 1.238314</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [598,] 0.6094172 1.114774 1.123011 1.131895 1.135320 1.189467 1.236134 1.238409</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [599,] 0.6094639 1.114860 1.123097 1.131982 1.135407 1.189558 1.236229 1.238503</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [600,] 0.6095105 1.114945 1.123183 1.132069 1.135494 1.189650 1.236323 1.238598</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [601,] 0.6095572 1.115030 1.123269 1.132155 1.135581 1.189741 1.236418 1.238693</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [602,] 0.6089974 1.114006 1.122237 1.131116 1.134538 1.188648 1.235282 1.237556</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [603,] 0.6090440 1.114092 1.122323 1.131202 1.134625 1.188739 1.235377 1.237650</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [604,] 0.6090906 1.114177 1.122409 1.131289 1.134711 1.188830 1.235472 1.237745</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [605,] 0.6091373 1.114262 1.122495 1.131375 1.134798 1.188921 1.235566 1.237840</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [606,] 0.6091839 1.114348 1.122581 1.131462 1.134885 1.189012 1.235661 1.237935</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [607,] 0.6092306 1.114433 1.122667 1.131549 1.134972 1.189103 1.235755 1.238029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [608,] 0.6092772 1.114518 1.122753 1.131635 1.135059 1.189194 1.235850 1.238124</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [609,] 0.6084381 1.112983 1.121207 1.130077 1.133496 1.187556 1.234148 1.236419</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [610,] 0.6084847 1.113069 1.121292 1.130163 1.133583 1.187647 1.234242 1.236514</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [611,] 0.5790742 1.059270 1.067096 1.075538 1.078792 1.130244 1.174587 1.176748</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [612,] 0.5791186 1.059351 1.067178 1.075620 1.078875 1.130330 1.174677 1.176838</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [613,] 0.5789856 1.059107 1.066933 1.075373 1.078627 1.130071 1.174407 1.176568</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [614,] 0.5790299 1.059189 1.067014 1.075456 1.078710 1.130157 1.174497 1.176658</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [615,] 0.5802726 1.061462 1.069304 1.077764 1.081025 1.132583 1.177017 1.179183</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [616,] 0.5803171 1.061543 1.069386 1.077846 1.081107 1.132669 1.177108 1.179274</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [617,] 0.5791629 1.059432 1.067259 1.075703 1.078957 1.130417 1.174767 1.176928</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [618,] 0.5792073 1.059513 1.067341 1.075785 1.079040 1.130503 1.174857 1.177018</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [619,] 0.5804504 1.061787 1.069632 1.078094 1.081356 1.132930 1.177378 1.179545</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [620,] 0.5804948 1.061868 1.069714 1.078177 1.081439 1.133016 1.177468 1.179635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [621,] 0.5805393 1.061950 1.069796 1.078259 1.081521 1.133103 1.177558 1.179725</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [622,] 0.5805837 1.062031 1.069878 1.078342 1.081604 1.133190 1.177649 1.179816</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [623,] 0.5806282 1.062112 1.069959 1.078424 1.081687 1.133277 1.177739 1.179906</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [624,] 0.5806726 1.062193 1.070041 1.078507 1.081770 1.133363 1.177829 1.179996</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [625,] 0.5807171 1.062275 1.070123 1.078589 1.081853 1.133450 1.177919 1.180087</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [626,] 0.5807616 1.062356 1.070205 1.078672 1.081936 1.133537 1.178009 1.180177</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [627,] 0.5808060 1.062438 1.070287 1.078755 1.082018 1.133624 1.178099 1.180267</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [628,] 0.5808505 1.062519 1.070369 1.078837 1.082101 1.133711 1.178190 1.180358</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [629,] 0.5808950 1.062600 1.070451 1.078920 1.082184 1.133797 1.178280 1.180448</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [630,] 0.5803615 1.061624 1.069468 1.077929 1.081190 1.132756 1.177198 1.179364</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [631,] 0.5804059 1.061706 1.069550 1.078011 1.081273 1.132843 1.177288 1.179454</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [632,] 0.5792516 1.059594 1.067423 1.075868 1.079123 1.130590 1.174947 1.177109</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [633,] 0.5792960 1.059675 1.067505 1.075950 1.079205 1.130676 1.175036 1.177199</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [634,] 0.5793403 1.059756 1.067586 1.076032 1.079288 1.130763 1.175126 1.177289</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [635,] 0.5793847 1.059838 1.067668 1.076115 1.079370 1.130850 1.175216 1.177379</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [636,] 0.5794291 1.059919 1.067750 1.076197 1.079453 1.130936 1.175306 1.177469</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [637,] 0.5794734 1.060000 1.067832 1.076280 1.079536 1.131023 1.175396 1.177559</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [638,] 0.5795178 1.060081 1.067913 1.076362 1.079618 1.131109 1.175486 1.177649</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [639,] 0.5795622 1.060162 1.067995 1.076444 1.079701 1.131196 1.175576 1.177740</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [640,] 0.5796066 1.060243 1.068077 1.076527 1.079784 1.131283 1.175666 1.177830</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [641,] 0.5796509 1.060325 1.068159 1.076609 1.079866 1.131369 1.175756 1.177920</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [642,] 0.5796953 1.060406 1.068240 1.076692 1.079949 1.131456 1.175846 1.178010</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [643,] 0.5797397 1.060487 1.068322 1.076774 1.080032 1.131543 1.175937 1.178100</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [644,] 0.5797841 1.060568 1.068404 1.076857 1.080115 1.131629 1.176027 1.178191</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [645,] 0.5798285 1.060649 1.068486 1.076939 1.080197 1.131716 1.176117 1.178281</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [646,] 0.5798729 1.060731 1.068568 1.077021 1.080280 1.131802 1.176207 1.178371</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [647,] 0.5799173 1.060812 1.068649 1.077104 1.080363 1.131889 1.176297 1.178461</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [648,] 0.5799617 1.060893 1.068731 1.077186 1.080445 1.131976 1.176387 1.178552</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [649,] 0.5800061 1.060974 1.068813 1.077269 1.080528 1.132062 1.176477 1.178642</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [650,] 0.5800505 1.061055 1.068895 1.077351 1.080611 1.132149 1.176567 1.178732</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [651,] 0.5800949 1.061137 1.068977 1.077434 1.080694 1.132236 1.176657 1.178822</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [652,] 0.5801393 1.061218 1.069059 1.077516 1.080776 1.132323 1.176747 1.178913</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [653,] 0.5801838 1.061299 1.069141 1.077599 1.080859 1.132409 1.176837 1.179003</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [654,] 0.5802282 1.061381 1.069222 1.077681 1.080942 1.132496 1.176927 1.179093</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1,] 1.085189     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [2,] 1.085106     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [3,] 1.085023     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [4,] 1.084940     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [5,] 1.084857     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [6,] 1.084773     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [7,] 1.084690     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [8,] 1.084607     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [9,] 1.084524     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [10,] 1.084441     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [11,] 1.084358     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [12,] 1.084275     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [13,] 1.084192     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [14,] 1.084109     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [15,] 1.084026     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [16,] 1.083943     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [17,] 1.083860     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [18,] 1.083777     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [19,] 1.083694     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [20,] 1.083611     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [21,] 1.083528     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [22,] 1.083445     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [23,] 1.083362     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [24,] 1.083279     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25,] 1.083196     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26,] 1.083114     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [27,] 1.083031     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [28,] 1.082948     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [29,] 1.082865     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [30,] 1.082782     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [31,] 1.082699     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [32,] 1.082616     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [33,] 1.082533     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [34,] 1.082450     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [35,] 1.082367     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [36,] 1.082285     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [37,] 1.082202     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [38,] 1.082119     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [39,] 1.082036     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [40,] 1.081953     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [41,] 1.081870     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [42,] 1.081787     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [43,] 1.081705     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [44,] 1.081622     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [45,] 1.081539     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [46,] 1.081456     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [47,] 1.081373     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [48,] 1.081291     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [49,] 1.081208     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [50,] 1.081125     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51,] 1.081042     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [52,] 1.080959     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [53,] 1.080877     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [54,] 1.080794     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [55,] 1.080711     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [56,] 1.080628     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [57,] 1.080546     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [58,] 1.080463     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [59,] 1.080380     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [60,] 1.080298     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61,] 1.080215     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [62,] 1.080132     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [63,] 1.080049     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [64,] 1.079967     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [65,] 1.079884     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [66,] 1.079801     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [67,] 1.079719     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [68,] 1.079636     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [69,] 1.079553     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [70,] 1.079471     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [71,] 1.079388     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [72,] 1.079305     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [73,] 1.079223     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [74,] 1.079140     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [75,] 1.079058     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76,] 1.078975     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [77,] 1.078892     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [78,] 1.078810     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [79,] 1.078727     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [80,] 1.078644     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [81,] 1.078562     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [82,] 1.078479     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [83,] 1.078397     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [84,] 1.078314     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85,] 1.078232     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [86,] 1.078149     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [87,] 1.078067     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [88,] 1.077984     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [89,] 1.077901     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [90,] 1.077819     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [91,] 1.077736     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [92,] 1.077654     0     0     0     0     0     0     0     0     0     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [93,] 1.077571     0     0     0     0     0     0     0     0     0     0</span>
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+<span class="r-out co"><span class="r-pr">#&gt;</span> [544,] 0.8964778     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [545,] 0.8964092     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [546,] 0.8963405     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [547,] 0.8962719     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [548,] 0.8962033     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [549,] 0.8970271     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [550,] 0.8969584     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [551,] 0.8968898     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [552,] 0.8968211     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [553,] 0.8967524     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [554,] 0.8957917     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [555,] 0.8957231     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [556,] 0.8956545     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [557,] 0.8955859     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [558,] 0.8955174     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [559,] 0.8954488     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [560,] 0.8953802     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [561,] 0.8953117     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [562,] 0.8961347     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [563,] 0.8960661     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [564,] 0.8959975     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [565,] 0.8959289     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [566,] 0.8958603     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [567,] 0.8942840     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [568,] 0.8942155     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [569,] 0.8941470     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [570,] 1.0059666     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [571,] 1.0056585     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [572,] 1.0058896     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [573,] 1.0058125     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [574,] 1.0057355     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [575,] 1.0064288     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [576,] 1.0055815     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [577,] 1.0055045     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [578,] 1.0054276     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [579,] 1.0061206     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [580,] 1.0060436     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [581,] 1.0034280     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [582,] 1.0033512     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [583,] 1.0048888     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [584,] 1.0048119     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [585,] 1.0047349     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [586,] 1.0046580     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [587,] 1.0045811     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [588,] 1.0045042     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [589,] 1.0063518     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [590,] 1.0062747     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [591,] 1.0061977     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [592,] 1.0051966     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [593,] 1.0051197     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [594,] 1.0050427     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [595,] 1.0049658     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [596,] 1.0038891     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [597,] 1.0038122     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [598,] 1.0037353     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [599,] 1.0036585     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [600,] 1.0035817     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [601,] 1.0035048     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [602,] 1.0044273     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [603,] 1.0043504     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [604,] 1.0042735     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [605,] 1.0041966     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [606,] 1.0041197     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [607,] 1.0040428     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [608,] 1.0039659     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [609,] 1.0053506     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [610,] 1.0052736     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [611,] 1.0563301     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [612,] 1.0562493     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [613,] 1.0564919     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [614,] 1.0564110     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [615,] 1.0541486     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [616,] 1.0540679     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [617,] 1.0561684     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [618,] 1.0560875     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [619,] 1.0538258     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [620,] 1.0537451     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [621,] 1.0536644     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [622,] 1.0535838     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [623,] 1.0535031     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [624,] 1.0534224     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [625,] 1.0533418     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [626,] 1.0532611     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [627,] 1.0531805     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [628,] 1.0530998     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [629,] 1.0530192     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [630,] 1.0539872     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [631,] 1.0539065     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [632,] 1.0560067     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [633,] 1.0559258     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [634,] 1.0558450     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [635,] 1.0557641     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [636,] 1.0556833     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [637,] 1.0556025     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [638,] 1.0555216     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [639,] 1.0554408     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [640,] 1.0553600     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [641,] 1.0552792     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [642,] 1.0551984     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [643,] 1.0551176     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [644,] 1.0550368     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [645,] 1.0549561     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [646,] 1.0548753     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [647,] 1.0547945     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [648,] 1.0547138     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [649,] 1.0546330     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [650,] 1.0545523     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [651,] 1.0544715     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [652,] 1.0543908     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [653,] 1.0543100     0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [654,] 1.0542293     0</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/create_netgeom.html b/docs/reference/create_netgeom.html
new file mode 100644
index 0000000..8daf6ca
--- /dev/null
+++ b/docs/reference/create_netgeom.html
@@ -0,0 +1,145 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create netgeom column in SSN object — create_netgeom • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create netgeom column in SSN object — create_netgeom"><meta name="description" content="Create netgeom column for edges, observed sites,
+and/or prediction sites in a Landscape Network (LSN)."><meta property="og:description" content="Create netgeom column for edges, observed sites,
+and/or prediction sites in a Landscape Network (LSN)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SSN2</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Create netgeom column in SSN object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/create_netgeom.R" class="external-link"><code>R/create_netgeom.R</code></a></small>
+      <div class="d-none name"><code>create_netgeom.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Create netgeom column for edges, observed sites,
+and/or prediction sites in a Landscape Network (LSN).</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">create_netgeom</span><span class="op">(</span><span class="va">sf_data</span>, type <span class="op">=</span> <span class="cn">NULL</span>, overwrite <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-sf-data">sf_data<a class="anchor" aria-label="anchor" href="#arg-sf-data"></a></dt>
+<dd><p>An <code>sf</code> object with LINESTING or POINT geometry
+created using <code>link{lsn_to_ssn}</code> (see Details).</p></dd>
+
+
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
+<dd><p>Character string defining geometry type of
+<code>sf_data</code>. Default = <code>NULL</code>.</p></dd>
+
+
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
+<dd><p>Logical indicating whether existing data should be
+overwritten if present. Default = <code>FALSE</code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An <code>sf</code> object containing the original data from
+<code>sf_data</code> and an additional column named netgeom.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Most users will not need to run <code>create_netgeom</code>
+themselves because it is called internally when <code>lsn_to_ssn</code>
+is run or an <code>SSN</code> is imported using
+<code>link[SSN2]{ssn_import}</code> found in the <code>SSN2</code>
+package. For users who do wish to run <code>create_netgeom</code>, the
+<code>sf_data</code> object must represent edges, observed sites, or
+prediction sites in a <code>SSN</code> object created using
+<code>link{lsn_to_ssn}</code>.</p>
+<p>The netgeom column contains information in character format used
+to describe the topology of the LSN. The format and content of
+the netgeom column differs depending on whether <code>sf_data</code>
+contains LINESTRING (edges) or POINT (observed or prediction
+sites) geometry. For edges, the netgeom format is:</p><ul><li><p><code>'ENETWORK (netID, rid, upDist)'</code></p></li>
+</ul><p>For observed or prediction sites, the netgeom format is:</p><ul><li><p><code>'SNETWORK (netID, rid, upDist, ratio, pid, locID)'</code></p></li>
+</ul><p>The rid, ratio, upDist, netID, pid, and locID columns must be
+present in <code>sf_data</code> before netgeom is added.</p>
+<p>If <code>overwrite = TRUE</code> and a column named netgeom is present in
+<code>sf_data</code>, the data will be overwritten. Default = FALSE.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Create local temporary copy of MiddleFork04.ssn found in</span></span></span>
+<span class="r-in"><span><span class="co">## the SSN2 package. Only necessary for this example.</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="copy_lsn_to_temp.html">copy_lsn_to_temp</a></span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># Import the SSN object with prediction points, pred1km</span></span></span>
+<span class="r-in"><span><span class="va">mf04</span><span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>   <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>   predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>   overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># Recalculate the netgeom column for the observed sites</span></span></span>
+<span class="r-in"><span><span class="va">sf_obs</span> <span class="op">&lt;-</span> <span class="fu">create_netgeom</span><span class="op">(</span></span></span>
+<span class="r-in"><span>    <span class="va">mf04</span><span class="op">$</span><span class="va">obs</span>,</span></span>
+<span class="r-in"><span>    type <span class="op">=</span> <span class="st">"POINT"</span>,</span></span>
+<span class="r-in"><span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># Recalculate the netgeom column for the edges</span></span></span>
+<span class="r-in"><span><span class="va">sf_edges</span> <span class="op">&lt;-</span> <span class="fu">create_netgeom</span><span class="op">(</span></span></span>
+<span class="r-in"><span>    <span class="va">mf04</span><span class="op">$</span><span class="va">edges</span>,</span></span>
+<span class="r-in"><span>    type <span class="op">=</span> <span class="st">"LINESTRING"</span>,</span></span>
+<span class="r-in"><span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/deviance.SSN2.html b/docs/reference/deviance.SSN2.html
index d7306a4..18fea64 100644
--- a/docs/reference/deviance.SSN2.html
+++ b/docs/reference/deviance.SSN2.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Returns the deviance of a fitted model object."><title>Fitted model deviance — deviance.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fitted model deviance — deviance.SSN2"><meta property="og:description" content="Returns the deviance of a fitted model object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fitted model deviance — deviance.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fitted model deviance — deviance.SSN2"><meta name="description" content="Returns the deviance of a fitted model object."><meta property="og:description" content="Returns the deviance of a fitted model object."></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fitted model deviance</h1>
+
+      <h1>Fitted model deviance</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/deviance.R" class="external-link"><code>R/deviance.R</code></a></small>
       <div class="d-none name"><code>deviance.SSN2.Rd</code></div>
     </div>
@@ -57,28 +43,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/deviance.html" class="external-link">deviance</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/deviance.html" class="external-link">deviance</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The deviance.</p>
+    <p>The deviance.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -103,26 +89,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/deviance.html" class="external-link">deviance</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.00200104</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 9.832514e-05</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/docs/reference/fitted.SSN2.html b/docs/reference/fitted.SSN2.html
index 582f040..954a5c9 100644
--- a/docs/reference/fitted.SSN2.html
+++ b/docs/reference/fitted.SSN2.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Extract fitted values from fitted model objects. fitted.values
-is an alias."><title>Extract model fitted values — fitted.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract model fitted values — fitted.SSN2"><meta property="og:description" content="Extract fitted values from fitted model objects. fitted.values
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Extract model fitted values — fitted.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract model fitted values — fitted.SSN2"><meta name="description" content="Extract fitted values from fitted model objects. fitted.values
+is an alias."><meta property="og:description" content="Extract fitted values from fitted model objects. fitted.values
+is an alias."></head><body>
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-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Extract model fitted values</h1>
+
+      <h1>Extract model fitted values</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/fitted.R" class="external-link"><code>R/fitted.R</code></a></small>
       <div class="d-none name"><code>fitted.SSN2.Rd</code></div>
     </div>
@@ -60,26 +46,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html" class="external-link">fitted</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html" class="external-link">fitted.values</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html" class="external-link">fitted</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html" class="external-link">fitted.values</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p><code>"response"</code> for fitted values of the response,
 <code>"tailup"</code> for fitted values of the tailup random errors,
 <code>"taildown"</code> for fitted values of the taildown random errors,
@@ -90,15 +78,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 The default is <code>"response"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The fitted values according to <code>type</code>.</p>
+    <p>The fitted values according to <code>type</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -135,50 +121,50 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html" class="external-link">fitted</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        1        2        3        4        5        6        7        8 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.34158 14.13982 13.88042 13.47691 13.41927 13.33280 13.01576 13.01576 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        9       10       11       12       13       14       15       16 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.92929 14.02453 13.30398 13.24633 12.58342 12.17991 11.77640 11.22878 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       17       18       19       20       21       22       23       24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.39922 14.19747 13.70749 13.62102 13.76513 13.50573 12.92929 12.64107 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       25       26       27       28       29       30       31       32 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.35284 12.23755 11.63229 13.21751 12.69871 12.46813 14.88920 15.20625 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       33       34       35       36       37       38       39       40 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15.06213 15.89798 15.63858 15.40800 15.40800 15.26389 15.17742 15.11978 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       41       42       43       44       45 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15.14860 15.11978 15.09096 13.24633 11.45935 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>         1         2         3         4         5         6         7         8 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.417439 14.246817 14.042070 15.236427 14.929306 14.656310 14.656310 14.485688 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>         9        10        11        12        13        14        15        16 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.383315 14.315066 14.349190 14.315066 14.280941 13.393704 13.154833 12.847712 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        17        18        19        20        21        22        23        24 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.369969 12.301720 12.199347 11.823977 11.823977 11.721604 13.018335 12.165222 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        25        26        27        28        29        30        31        32 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.096973 12.096973 11.312110 10.834367 10.356624  9.708258 13.461953 13.223082 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        33        34        35        36        37        38        39        40 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.642965 12.540592 12.711214 12.404094 11.721604 11.380359 11.039114 10.902616 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        41        42        43        44        45 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10.186001  9.981254 12.062849 11.448608 11.175612 </span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html" class="external-link">fitted.values</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        1        2        3        4        5        6        7        8 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.34158 14.13982 13.88042 13.47691 13.41927 13.33280 13.01576 13.01576 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        9       10       11       12       13       14       15       16 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.92929 14.02453 13.30398 13.24633 12.58342 12.17991 11.77640 11.22878 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       17       18       19       20       21       22       23       24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.39922 14.19747 13.70749 13.62102 13.76513 13.50573 12.92929 12.64107 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       25       26       27       28       29       30       31       32 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.35284 12.23755 11.63229 13.21751 12.69871 12.46813 14.88920 15.20625 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       33       34       35       36       37       38       39       40 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15.06213 15.89798 15.63858 15.40800 15.40800 15.26389 15.17742 15.11978 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       41       42       43       44       45 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15.14860 15.11978 15.09096 13.24633 11.45935 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>         1         2         3         4         5         6         7         8 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.417439 14.246817 14.042070 15.236427 14.929306 14.656310 14.656310 14.485688 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>         9        10        11        12        13        14        15        16 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.383315 14.315066 14.349190 14.315066 14.280941 13.393704 13.154833 12.847712 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        17        18        19        20        21        22        23        24 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.369969 12.301720 12.199347 11.823977 11.823977 11.721604 13.018335 12.165222 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        25        26        27        28        29        30        31        32 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.096973 12.096973 11.312110 10.834367 10.356624  9.708258 13.461953 13.223082 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        33        34        35        36        37        38        39        40 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.642965 12.540592 12.711214 12.404094 11.721604 11.380359 11.039114 10.902616 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        41        42        43        44        45 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10.186001  9.981254 12.062849 11.448608 11.175612 </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/formula.SSN2.html b/docs/reference/formula.SSN2.html
index 1a66c31..6b2aa49 100644
--- a/docs/reference/formula.SSN2.html
+++ b/docs/reference/formula.SSN2.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Model formulae</h1>
+
+      <h1>Model formulae</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/formula.R" class="external-link"><code>R/formula.R</code></a></small>
       <div class="d-none name"><code>formula.SSN2.Rd</code></div>
     </div>
@@ -57,28 +43,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/formula.html" class="external-link">formula</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/formula.html" class="external-link">formula</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The formula used by a fitted model object.</p>
+    <p>The formula used by a fitted model object.</p>
     </div>
 
     <div class="section level2">
@@ -98,26 +84,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/formula.html" class="external-link">formula</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Summer_mn ~ ELEV_DEM</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x000001546ff7a278&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x000002369df92a90&gt;</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/docs/reference/glance.SSN2.html b/docs/reference/glance.SSN2.html
index bc9310c..e226027 100644
--- a/docs/reference/glance.SSN2.html
+++ b/docs/reference/glance.SSN2.html
@@ -1,56 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Returns a row of model
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Glance at a fitted model object — glance.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Glance at a fitted model object — glance.SSN2"><meta name="description" content="Returns a row of model
 summaries from a fitted model object. Glance returns the same number of columns for all models
-and estimation methods."><title>Glance at a fitted model object — glance.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Glance at a fitted model object — glance.SSN2"><meta property="og:description" content="Returns a row of model
+and estimation methods."><meta property="og:description" content="Returns a row of model
 summaries from a fitted model object. Glance returns the same number of columns for all models
-and estimation methods."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+and estimation methods."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Glance at a fitted model object</h1>
+
+      <h1>Glance at a fitted model object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/glance.R" class="external-link"><code>R/glance.R</code></a></small>
       <div class="d-none name"><code>glance.SSN2.Rd</code></div>
     </div>
@@ -63,28 +49,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">glance</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">glance</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A single-row tibble with columns</p><ul><li><p><code>n</code> The sample size.</p></li>
+    <p>A single-row tibble with columns</p><ul><li><p><code>n</code> The sample size.</p></li>
 <li><p><code>p</code> The number of fixed effects.</p></li>
 <li><p><code>npar</code> The number of estimated covariance parameters.</p></li>
 <li><p><code>value</code> The optimized value of the fitting function</p></li>
@@ -114,26 +100,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 1 × 9</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span>    45     2     3  54.2  60.2  60.8  -<span style="color: #BB0000;">27.1</span>     43.5            0.669</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span>    45     2     3  76.9  82.9  83.5  -<span style="color: #BB0000;">38.4</span>     43.0            0.585</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/glances.SSN2.html b/docs/reference/glances.SSN2.html
index d735b25..435a7f7 100644
--- a/docs/reference/glances.SSN2.html
+++ b/docs/reference/glances.SSN2.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="glances() repeatedly calls glance() on several
-fitted model objects and binds the output together, sorted by a column of interest."><title>Glance at many fitted model objects — glances.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Glance at many fitted model objects — glances.SSN2"><meta property="og:description" content="glances() repeatedly calls glance() on several
-fitted model objects and binds the output together, sorted by a column of interest."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+fitted model objects and binds the output together, sorted by a column of interest."><meta property="og:description" content="glances() repeatedly calls glance() on several
+fitted model objects and binds the output together, sorted by a column of interest."></head><body>
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-      <img src="" class="logo" alt=""><h1>Glance at many fitted model objects</h1>
+
+      <h1>Glance at many fitted model objects</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/glances.R" class="external-link"><code>R/glances.R</code></a></small>
       <div class="d-none name"><code>glances.SSN2.Rd</code></div>
     </div>
@@ -60,38 +46,38 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/glances.html" class="external-link">glances</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, sort_by <span class="op">=</span> <span class="st">"AICc"</span>, decreasing <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/glances.html" class="external-link">glances</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, sort_by <span class="op">=</span> <span class="st">"AICc"</span>, decreasing <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>Fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional fitted model objects from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>sort_by</dt>
+<dt id="arg-sort-by">sort_by<a class="anchor" aria-label="anchor" href="#arg-sort-by"></a></dt>
 <dd><p>Sort by a <code>glance</code> statistic (i.e., the name of a column
 output from <code><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">glance()</a></code> or the order of model input (<code>sort_by = "order"</code>).
 The default is <code>"AICc"</code>.</p></dd>
 
 
-<dt>decreasing</dt>
+<dt id="arg-decreasing">decreasing<a class="anchor" aria-label="anchor" href="#arg-decreasing"></a></dt>
 <dd><p>Should <code>sort_by</code> be decreasing or not? The default is <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A tibble where each row represents the output of <code><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">glance()</a></code> for
+    <p>A tibble where each row represents the output of <code><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">glance()</a></code> for
 each fitted model object.</p>
     </div>
 
@@ -121,27 +107,27 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 2 × 10</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   model        n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> ssn_mod1    45     2     3  54.2  60.2  60.8  -<span style="color: #BB0000;">27.1</span>     43.5           0.669 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> ssn_mod1    45     2     3  76.9  82.9  83.5  -<span style="color: #BB0000;">38.4</span>     43.0           0.585 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ssn_mod2    45     2     3 123.  129.  129.   -<span style="color: #BB0000;">61.4</span>     43.4           0.091<span style="text-decoration: underline;">7</span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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@@ -1,54 +1,40 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compute leverage (hat) values</h1>
+
+      <h1>Compute leverage (hat) values</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/hatvalues.R" class="external-link"><code>R/hatvalues.R</code></a></small>
       <div class="d-none name"><code>hatvalues.SSN2.Rd</code></div>
     </div>
@@ -60,28 +46,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">hatvalues</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">hatvalues</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>model</dt>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A vector of leverage (hat) values for each observation from the
+    <p>A vector of leverage (hat) values for each observation from the
 fitted model object.</p>
     </div>
     <div class="section level2">
@@ -113,41 +99,41 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">hatvalues</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            1            2            3            4            5            6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.565733e-01 9.237419e-02 3.014500e-02 7.981493e-03 1.046372e-02 3.495971e-03 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            7            8            9           10           11           12 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.793629e-02 1.693222e-02 8.311967e-03 5.612972e-02 1.031498e-01 6.860116e-02 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           13           14           15           16           17           18 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.109540e-02 4.019243e-02 1.253507e-01 9.102368e-02 7.023914e-02 2.202866e-02 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           19           20           21           22           23           24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4.509632e-03 2.293475e-03 3.162038e-03 2.476739e-03 1.356383e-02 5.100491e-02 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           25           26           27           28           29           30 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.395086e-02 2.156846e-02 9.548471e-02 1.553130e-01 8.095946e-03 3.568117e-02 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           31           32           33           34           35           36 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.536055e-02 1.067164e-01 4.896040e-02 2.389906e-02 7.092277e-05 1.122144e-02 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           37           38           39           40           41           42 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2.463285e-02 1.287318e-01 8.437105e-02 6.881851e-02 1.810541e-02 1.149500e-02 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           43           44           45 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9.801228e-03 7.212308e-02 1.656265e-02 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           1           2           3           4           5           6 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.111004780 0.055738061 0.033706428 0.074359366 0.020156027 0.005693127 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           7           8           9          10          11          12 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.001624725 0.057398164 0.073923742 0.058051176 0.025562432 0.027726546 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          13          14          15          16          17          18 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.025641370 0.060692330 0.083505795 0.029915738 0.015064681 0.017762234 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          19          20          21          22          23          24 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.007203224 0.014860920 0.014280350 0.012121476 0.073987730 0.030984461 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          25          26          27          28          29          30 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.051576193 0.054619566 0.013623967 0.043036889 0.120814555 0.112522931 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          31          32          33          34          35          36 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.058861422 0.019483321 0.005954595 0.004592189 0.009847750 0.005559533 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          37          38          39          40          41          42 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.002381727 0.033445107 0.035807368 0.046264890 0.057583822 0.065328710 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          43          44          45 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.138148339 0.030372326 0.159209917 </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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-      <img src="" class="logo" alt=""><h1>Function reference</h1>
+
+      <h1>Package index</h1>
     </div>
 
     <div class="section level2">
       <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="#all-functions"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="AIC.SSN2.html">AIC(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="AIC.SSN2.html">AIC(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="AIC.SSN2.html">AICc(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="AIC.SSN2.html">AICc(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="AIC.SSN2.html">AIC(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="AIC.SSN2.html">AIC(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="AIC.SSN2.html">AICc(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="AIC.SSN2.html">AICc(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Compute AIC and AICc of fitted model objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="anova.SSN2.html">anova(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="anova.SSN2.html">anova(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="anova.SSN2.html">tidy(<i>&lt;anova.ssn_lm&gt;</i>)</a></code> <code><a href="anova.SSN2.html">tidy(<i>&lt;anova.ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="anova.SSN2.html">anova(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="anova.SSN2.html">anova(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="anova.SSN2.html">tidy(<i>&lt;anova.ssn_lm&gt;</i>)</a></code> <code><a href="anova.SSN2.html">tidy(<i>&lt;anova.ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Compute analysis of variance and likelihood ratio tests of fitted model objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="augment.SSN2.html">augment(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="augment.SSN2.html">augment(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="augment.SSN2.html">augment(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="augment.SSN2.html">augment(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Augment data with information from fitted model objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="coef.SSN2.html">coef(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="coef.SSN2.html">coefficients(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="coef.SSN2.html">coef(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="coef.SSN2.html">coefficients(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="coef.SSN2.html">coef(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="coef.SSN2.html">coefficients(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="coef.SSN2.html">coef(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="coef.SSN2.html">coefficients(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract fitted model coefficients</dd>
       </dl><dl><dt>
-          
-          <code><a href="confint.SSN2.html">confint(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="confint.SSN2.html">confint(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="confint.SSN2.html">confint(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="confint.SSN2.html">confint(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Confidence intervals for fitted model parameters</dd>
       </dl><dl><dt>
-          
-          <code><a href="cooks.distance.SSN2.html">cooks.distance(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="cooks.distance.SSN2.html">cooks.distance(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="cooks.distance.SSN2.html">cooks.distance(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="cooks.distance.SSN2.html">cooks.distance(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Compute Cook's distance</dd>
       </dl><dl><dt>
-          
-          <code><a href="copy_lsn_to_temp.html">copy_lsn_to_temp()</a></code> 
+
+          <code><a href="copy_lsn_to_temp.html">copy_lsn_to_temp()</a></code>
+
         </dt>
         <dd>Copy LSN to temporary directory</dd>
       </dl><dl><dt>
-          
-          <code><a href="covmatrix.SSN2.html">covmatrix(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="covmatrix.SSN2.html">covmatrix(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="covmatrix.SSN2.html">covmatrix(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="covmatrix.SSN2.html">covmatrix(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Create a covariance matrix</dd>
       </dl><dl><dt>
-          
-          <code><a href="deviance.SSN2.html">deviance(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="deviance.SSN2.html">deviance(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="create_netgeom.html">create_netgeom()</a></code>
+
+        </dt>
+        <dd>Create netgeom column in SSN object</dd>
+      </dl><dl><dt>
+
+          <code><a href="deviance.SSN2.html">deviance(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="deviance.SSN2.html">deviance(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Fitted model deviance</dd>
       </dl><dl><dt>
-          
-          <code><a href="fitted.SSN2.html">fitted(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="fitted.SSN2.html">fitted.values(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="fitted.SSN2.html">fitted(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="fitted.SSN2.html">fitted.values(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="fitted.SSN2.html">fitted(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="fitted.SSN2.html">fitted.values(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="fitted.SSN2.html">fitted(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="fitted.SSN2.html">fitted.values(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract model fitted values</dd>
       </dl><dl><dt>
-          
-          <code><a href="formula.SSN2.html">formula(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="formula.SSN2.html">formula(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="formula.SSN2.html">formula(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="formula.SSN2.html">formula(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Model formulae</dd>
       </dl><dl><dt>
-          
-          <code><a href="glance.SSN2.html">glance(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="glance.SSN2.html">glance(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="glance.SSN2.html">glance(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="glance.SSN2.html">glance(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Glance at a fitted model object</dd>
       </dl><dl><dt>
-          
-          <code><a href="glances.SSN2.html">glances(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="glances.SSN2.html">glances(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="glances.SSN2.html">glances(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="glances.SSN2.html">glances(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Glance at many fitted model objects</dd>
       </dl><dl><dt>
-          
-          <code><a href="hatvalues.SSN2.html">hatvalues(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="hatvalues.SSN2.html">hatvalues(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="hatvalues.SSN2.html">hatvalues(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="hatvalues.SSN2.html">hatvalues(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Compute leverage (hat) values</dd>
       </dl><dl><dt>
-          
-          <code><a href="influence.SSN2.html">influence(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="influence.SSN2.html">influence(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="influence.SSN2.html">influence(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="influence.SSN2.html">influence(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Regression diagnostics</dd>
       </dl><dl><dt>
-          
-          <code><a href="labels.SSN2.html">labels(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="labels.SSN2.html">labels(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="labels.SSN2.html">labels(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="labels.SSN2.html">labels(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Find labels from object</dd>
       </dl><dl><dt>
-          
-          <code><a href="logLik.SSN2.html">logLik(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="logLik.SSN2.html">logLik(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="logLik.SSN2.html">logLik(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="logLik.SSN2.html">logLik(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract log-likelihood</dd>
       </dl><dl><dt>
-          
-          <code><a href="loocv.SSN2.html">loocv(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="loocv.SSN2.html">loocv(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="loocv.SSN2.html">loocv(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="loocv.SSN2.html">loocv(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Perform leave-one-out cross validation</dd>
       </dl><dl><dt>
-          
-          <code><a href="mf04p.html">mf04p</a></code> 
+
+          <code><a href="mf04p.html">mf04p</a></code>
+
         </dt>
         <dd>Imported SSN object from the MiddleFork04.ssn data folder</dd>
       </dl><dl><dt>
-          
-          <code><a href="MiddleFork04.ssn.html">MiddleFork04.ssn</a></code> 
+
+          <code><a href="MiddleFork04.ssn.html">MiddleFork04.ssn</a></code>
+
         </dt>
         <dd>MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset</dd>
       </dl><dl><dt>
-          
-          <code><a href="model.frame.SSN2.html">model.frame(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="model.frame.SSN2.html">model.frame(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="model.frame.SSN2.html">model.frame(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="model.frame.SSN2.html">model.frame(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract the model frame from a fitted model object</dd>
       </dl><dl><dt>
-          
-          <code><a href="model.matrix.SSN2.html">model.matrix(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="model.matrix.SSN2.html">model.matrix(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="model.matrix.SSN2.html">model.matrix(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="model.matrix.SSN2.html">model.matrix(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract the model matrix from a fitted model object</dd>
       </dl><dl><dt>
-          
-          <code><a href="names.SSN.html">names(<i>&lt;SSN&gt;</i>)</a></code> 
-        </dt>
-        <dd>names SSN object</dd>
-      </dl><dl><dt>
-          
-          <code><a href="plot.SSN2.html">plot(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="plot.SSN2.html">plot(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="plot.SSN2.html">plot(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="plot.SSN2.html">plot(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Plot fitted model diagnostics</dd>
       </dl><dl><dt>
-          
-          <code><a href="plot.Torgegram.html">plot(<i>&lt;Torgegram&gt;</i>)</a></code> 
+
+          <code><a href="plot.Torgegram.html">plot(<i>&lt;Torgegram&gt;</i>)</a></code>
+
         </dt>
         <dd>Plot Torgegram</dd>
       </dl><dl><dt>
-          
-          <code><a href="predict.SSN2.html">predict(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="predict.SSN2.html">predict(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="predict.SSN2.html">predict(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="predict.SSN2.html">predict(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Model predictions (Kriging)</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.SSN.html">print(<i>&lt;SSN&gt;</i>)</a></code> 
+
+          <code><a href="print.SSN.html">print(<i>&lt;SSN&gt;</i>)</a></code>
+
         </dt>
         <dd>Print SSN object</dd>
       </dl><dl><dt>
-          
-          <code><a href="print.SSN2.html">print(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;summary.ssn_lm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;summary.ssn_glm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;anova.ssn_lm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;anova.ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="print.SSN2.html">print(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;summary.ssn_lm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;summary.ssn_glm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;anova.ssn_lm&gt;</i>)</a></code> <code><a href="print.SSN2.html">print(<i>&lt;anova.ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Print values</dd>
       </dl><dl><dt>
-          
-          <code><a href="pseudoR2.SSN2.html">pseudoR2(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="pseudoR2.SSN2.html">pseudoR2(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="pseudoR2.SSN2.html">pseudoR2(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="pseudoR2.SSN2.html">pseudoR2(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Compute a pseudo r-squared</dd>
       </dl><dl><dt>
-          
-          <code><a href="residuals.SSN2.html">residuals(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">resid(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">rstandard(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">residuals(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">resid(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">rstandard(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="residuals.SSN2.html">residuals(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">resid(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">rstandard(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">residuals(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">resid(<i>&lt;ssn_glm&gt;</i>)</a></code> <code><a href="residuals.SSN2.html">rstandard(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Extract fitted model residuals</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_create_distmat.html">ssn_create_distmat()</a></code> 
+
+          <code><a href="ssn_create_distmat.html">ssn_create_distmat()</a></code>
+
         </dt>
         <dd>Calculate Hydrologic Distances for an <code>SSN</code> object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_get_data.html">ssn_get_data()</a></code> 
+
+          <code><a href="ssn_get_data.html">ssn_get_data()</a></code>
+
         </dt>
         <dd>Get a data.frame from an SSN, ssn_lm, or ssn_glm object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_get_netgeom.html">ssn_get_netgeom()</a></code> 
+
+          <code><a href="ssn_get_netgeom.html">ssn_get_netgeom()</a></code>
+
         </dt>
         <dd>Extract netgeom column</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_get_stream_distmat.html">ssn_get_stream_distmat()</a></code> 
+
+          <code><a href="ssn_get_stream_distmat.html">ssn_get_stream_distmat()</a></code>
+
         </dt>
         <dd>Get stream distance matrices from an <code>SSN</code> object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_glm.html">ssn_glm()</a></code> 
+
+          <code><a href="ssn_glm.html">ssn_glm()</a></code>
+
         </dt>
         <dd>Fitting Generalized Linear Models for Spatial Stream Networks</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_import.html">ssn_import()</a></code> 
+
+          <code><a href="ssn_import.html">ssn_import()</a></code>
+
         </dt>
         <dd>Import <code>SSN</code> object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_import_predpts.html">ssn_import_predpts()</a></code> 
+
+          <code><a href="ssn_import_predpts.html">ssn_import_predpts()</a></code>
+
         </dt>
         <dd>Import prediction points into an SSN, ssn_lm, or ssn_glm object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_initial.html">tailup_initial()</a></code> <code><a href="ssn_initial.html">taildown_initial()</a></code> <code><a href="ssn_initial.html">euclid_initial()</a></code> <code><a href="ssn_initial.html">nugget_initial()</a></code> 
+
+          <code><a href="ssn_initial.html">tailup_initial()</a></code> <code><a href="ssn_initial.html">taildown_initial()</a></code> <code><a href="ssn_initial.html">euclid_initial()</a></code> <code><a href="ssn_initial.html">nugget_initial()</a></code>
+
         </dt>
         <dd>Create a covariance parameter initial object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_lm.html">ssn_lm()</a></code> 
+
+          <code><a href="ssn_lm.html">ssn_lm()</a></code>
+
         </dt>
         <dd>Fitting Linear Models for Spatial Stream Networks</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_params.html">tailup_params()</a></code> <code><a href="ssn_params.html">taildown_params()</a></code> <code><a href="ssn_params.html">euclid_params()</a></code> <code><a href="ssn_params.html">nugget_params()</a></code> 
+
+          <code><a href="ssn_names.html">ssn_names()</a></code>
+
+        </dt>
+        <dd>Return names of data in an SSN object</dd>
+      </dl><dl><dt>
+
+          <code><a href="ssn_params.html">tailup_params()</a></code> <code><a href="ssn_params.html">taildown_params()</a></code> <code><a href="ssn_params.html">euclid_params()</a></code> <code><a href="ssn_params.html">nugget_params()</a></code>
+
         </dt>
         <dd>Create covariance parameter objects.</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_put_data.html">ssn_put_data()</a></code> 
+
+          <code><a href="ssn_put_data.html">ssn_put_data()</a></code>
+
         </dt>
         <dd>Put an sf data.frame in an SSN object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_simulate.html">ssn_simulate()</a></code> <code><a href="ssn_simulate.html">ssn_rbeta()</a></code> <code><a href="ssn_simulate.html">ssn_rbinom()</a></code> <code><a href="ssn_simulate.html">ssn_rgamma()</a></code> <code><a href="ssn_simulate.html">ssn_rinvgauss()</a></code> <code><a href="ssn_simulate.html">ssn_rnbinom()</a></code> <code><a href="ssn_simulate.html">ssn_rnorm()</a></code> <code><a href="ssn_simulate.html">ssn_rpois()</a></code> 
+
+          <code><a href="ssn_simulate.html">ssn_simulate()</a></code> <code><a href="ssn_simulate.html">ssn_rbeta()</a></code> <code><a href="ssn_simulate.html">ssn_rbinom()</a></code> <code><a href="ssn_simulate.html">ssn_rgamma()</a></code> <code><a href="ssn_simulate.html">ssn_rinvgauss()</a></code> <code><a href="ssn_simulate.html">ssn_rnbinom()</a></code> <code><a href="ssn_simulate.html">ssn_rnorm()</a></code> <code><a href="ssn_simulate.html">ssn_rpois()</a></code>
+
         </dt>
         <dd>Simulate random variables on a stream network</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_split_predpts.html">ssn_split_predpts()</a></code> 
+
+          <code><a href="ssn_split_predpts.html">ssn_split_predpts()</a></code>
+
         </dt>
         <dd>Split a prediction dataset in an <code>SSN</code> object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_subset.html">ssn_subset()</a></code> 
+
+          <code><a href="ssn_subset.html">ssn_subset()</a></code>
+
         </dt>
         <dd>Subset an <code>SSN</code> object</dd>
       </dl><dl><dt>
-          
-          <code><a href="SSN_to_SSN2.html">SSN_to_SSN2()</a></code> 
+
+          <code><a href="SSN_to_SSN2.html">SSN_to_SSN2()</a></code>
+
         </dt>
         <dd>Convert object from <code>SpatialStreamNetwork</code> class to <code>SSN</code> class</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_update_path.html">ssn_update_path()</a></code> 
+
+          <code><a href="ssn_update_path.html">ssn_update_path()</a></code>
+
         </dt>
         <dd>Update path in an SSN object</dd>
       </dl><dl><dt>
-          
-          <code><a href="ssn_write.html">ssn_write()</a></code> 
+
+          <code><a href="ssn_write.html">ssn_write()</a></code>
+
         </dt>
         <dd>write an SSN object</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.SSN.html">summary(<i>&lt;SSN&gt;</i>)</a></code> 
+
+          <code><a href="summary.SSN.html">summary(<i>&lt;SSN&gt;</i>)</a></code>
+
         </dt>
         <dd>Summarize an SSN object</dd>
       </dl><dl><dt>
-          
-          <code><a href="summary.SSN2.html">summary(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="summary.SSN2.html">summary(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="summary.SSN2.html">summary(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="summary.SSN2.html">summary(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Summarize a fitted model object</dd>
       </dl><dl><dt>
-          
-          <code><a href="tidy.SSN2.html">tidy(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="tidy.SSN2.html">tidy(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="tidy.SSN2.html">tidy(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="tidy.SSN2.html">tidy(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Tidy a fitted model object</dd>
       </dl><dl><dt>
-          
-          <code><a href="Torgegram.html">Torgegram()</a></code> 
+
+          <code><a href="Torgegram.html">Torgegram()</a></code>
+
         </dt>
         <dd>Compute the empirical semivariogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="varcomp.SSN2.html">varcomp(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="varcomp.SSN2.html">varcomp(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="varcomp.SSN2.html">varcomp(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="varcomp.SSN2.html">varcomp(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Variability component comparison</dd>
       </dl><dl><dt>
-          
-          <code><a href="vcov.SSN2.html">vcov(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="vcov.SSN2.html">vcov(<i>&lt;ssn_glm&gt;</i>)</a></code> 
+
+          <code><a href="vcov.SSN2.html">vcov(<i>&lt;ssn_lm&gt;</i>)</a></code> <code><a href="vcov.SSN2.html">vcov(<i>&lt;ssn_glm&gt;</i>)</a></code>
+
         </dt>
         <dd>Calculate variance-covariance matrix for a fitted model object</dd>
       </dl></div>
@@ -330,18 +375,18 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/influence.SSN2.html b/docs/reference/influence.SSN2.html
index 25580d1..b30b17c 100644
--- a/docs/reference/influence.SSN2.html
+++ b/docs/reference/influence.SSN2.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Provides basic quantities which are used in forming
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+a wide variety of diagnostics for checking the quality of fitted model objects."><meta property="og:description" content="Provides basic quantities which are used in forming
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-      <img src="" class="logo" alt=""><h1>Regression diagnostics</h1>
+
+      <h1>Regression diagnostics</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/influence.R" class="external-link"><code>R/influence.R</code></a></small>
       <div class="d-none name"><code>influence.SSN2.Rd</code></div>
     </div>
@@ -60,28 +46,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/lm.influence.html" class="external-link">influence</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/lm.influence.html" class="external-link">influence</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>model</dt>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A tibble with residuals (<code>.resid</code>), leverage values (<code>.hat</code>),
+    <p>A tibble with residuals (<code>.resid</code>), leverage values (<code>.hat</code>),
 cook's distance (<code>.cooksd</code>), and standardized residuals (<code>.std.resid</code>).</p>
     </div>
     <div class="section level2">
@@ -112,38 +98,38 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/lm.influence.html" class="external-link">influence</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 45 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    .resid    .hat    .cooksd .std.resid</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>  -<span style="color: #BB0000;">2.96</span> 0.157   0.135         -<span style="color: #BB0000;">1.31</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>  -<span style="color: #BB0000;">3.42</span> 0.092<span style="text-decoration: underline;">4</span>  0.018<span style="text-decoration: underline;">4</span>        -<span style="color: #BB0000;">0.631</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span>  -<span style="color: #BB0000;">3.45</span> 0.030<span style="text-decoration: underline;">1</span>  0.010<span style="text-decoration: underline;">3</span>        -<span style="color: #BB0000;">0.827</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span>  -<span style="color: #BB0000;">3.35</span> 0.007<span style="text-decoration: underline;">98</span> 0.000<span style="text-decoration: underline;">239</span>      -<span style="color: #BB0000;">0.245</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span>  -<span style="color: #BB0000;">3.30</span> 0.010<span style="text-decoration: underline;">5</span>  0.000<span style="text-decoration: underline;">006</span>74     0.035<span style="text-decoration: underline;">9</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span>  -<span style="color: #BB0000;">3.52</span> 0.003<span style="text-decoration: underline;">50</span> 0.002<span style="text-decoration: underline;">85</span>       -<span style="color: #BB0000;">1.28</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span>  -<span style="color: #BB0000;">3.26</span> 0.017<span style="text-decoration: underline;">9</span>  0.000<span style="text-decoration: underline;">014</span>4      0.040<span style="text-decoration: underline;">1</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span>  -<span style="color: #BB0000;">3.25</span> 0.016<span style="text-decoration: underline;">9</span>  0.000<span style="text-decoration: underline;">074</span>1      0.093<span style="text-decoration: underline;">5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span>  -<span style="color: #BB0000;">3.40</span> 0.008<span style="text-decoration: underline;">31</span> 0.000<span style="text-decoration: underline;">891</span>      -<span style="color: #BB0000;">0.463</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span>  -<span style="color: #BB0000;">1.45</span> 0.056<span style="text-decoration: underline;">1</span>  0.029<span style="text-decoration: underline;">0</span>        -<span style="color: #BB0000;">1.02</span>  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     .resid    .hat   .cooksd .std.resid</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>  0.503  0.111   0.016<span style="text-decoration: underline;">5</span>         0.545</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>  0.473  0.055<span style="text-decoration: underline;">7</span>  0.001<span style="text-decoration: underline;">73</span>        0.249</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span>  0.568  0.033<span style="text-decoration: underline;">7</span>  0.006<span style="text-decoration: underline;">58</span>        0.625</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> -<span style="color: #BB0000;">0.016</span><span style="color: #BB0000; text-decoration: underline;">4</span> 0.074<span style="text-decoration: underline;">4</span>  0.008<span style="text-decoration: underline;">93</span>        0.490</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> -<span style="color: #BB0000;">0.439</span>  0.020<span style="text-decoration: underline;">2</span>  0.015<span style="text-decoration: underline;">8</span>        -<span style="color: #BB0000;">1.25</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span>  0.634  0.005<span style="text-decoration: underline;">69</span> 0.000<span style="text-decoration: underline;">970</span>       0.584</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span>  0.414  0.001<span style="text-decoration: underline;">62</span> 0.000<span style="text-decoration: underline;">050</span>7     -<span style="color: #BB0000;">0.250</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span>  0.454  0.057<span style="text-decoration: underline;">4</span>  0.014<span style="text-decoration: underline;">3</span>        -<span style="color: #BB0000;">0.706</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span>  0.607  0.073<span style="text-decoration: underline;">9</span>  0.006<span style="text-decoration: underline;">66</span>        0.425</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span>  0.705  0.058<span style="text-decoration: underline;">1</span>  0.019<span style="text-decoration: underline;">6</span>         0.821</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 35 more rows</span></span>
 </code></pre></div>
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diff --git a/docs/reference/labels.SSN2.html b/docs/reference/labels.SSN2.html
index fc03ad0..46fcd1d 100644
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     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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+
 
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-      <img src="" class="logo" alt=""><h1>Find labels from object</h1>
+
+      <h1>Find labels from object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/labels.R" class="external-link"><code>R/labels.R</code></a></small>
       <div class="d-none name"><code>labels.SSN2.Rd</code></div>
     </div>
@@ -57,28 +43,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/labels.html" class="external-link">labels</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/labels.html" class="external-link">labels</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A character vector containing the terms used for the fixed effects
+    <p>A character vector containing the terms used for the fixed effects
 from a fitted model object.</p>
     </div>
 
@@ -101,23 +87,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "ELEV_DEM"</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/logLik.SSN2.html b/docs/reference/logLik.SSN2.html
index b8b604c..3636639 100644
--- a/docs/reference/logLik.SSN2.html
+++ b/docs/reference/logLik.SSN2.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Find the log-likelihood of a fitted model."><title>Extract log-likelihood — logLik.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract log-likelihood — logLik.SSN2"><meta property="og:description" content="Find the log-likelihood of a fitted model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Extract log-likelihood — logLik.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract log-likelihood — logLik.SSN2"><meta name="description" content="Find the log-likelihood of a fitted model."><meta property="og:description" content="Find the log-likelihood of a fitted model."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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+
 
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Extract log-likelihood</h1>
+
+      <h1>Extract log-likelihood</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/logLik.R" class="external-link"><code>R/logLik.R</code></a></small>
       <div class="d-none name"><code>logLik.SSN2.Rd</code></div>
     </div>
@@ -57,28 +43,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The log-likelihood.</p>
+    <p>The log-likelihood.</p>
     </div>
 
     <div class="section level2">
@@ -97,26 +83,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -27.09128</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -38.4466</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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index e8f1b26..23dccff 100644
--- a/docs/reference/loocv.SSN2.html
+++ b/docs/reference/loocv.SSN2.html
@@ -1,54 +1,40 @@
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+efficient approximations for big data."><meta property="og:description" content="Perform leave-one-out cross validation with options for computationally
+efficient approximations for big data."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Perform leave-one-out cross validation</h1>
+
+      <h1>Perform leave-one-out cross validation</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/loocv.R" class="external-link"><code>R/loocv.R</code></a>, <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/loocv_glm.R" class="external-link"><code>R/loocv_glm.R</code></a></small>
       <div class="d-none name"><code>loocv.SSN2.Rd</code></div>
     </div>
@@ -60,38 +46,38 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv</a></span><span class="op">(</span><span class="va">object</span>, cv_predict <span class="op">=</span> <span class="cn">FALSE</span>, se.fit <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/loocv.html" class="external-link">loocv</a></span><span class="op">(</span><span class="va">object</span>, cv_predict <span class="op">=</span> <span class="cn">FALSE</span>, se.fit <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>cv_predict</dt>
+<dt id="arg-cv-predict">cv_predict<a class="anchor" aria-label="anchor" href="#arg-cv-predict"></a></dt>
 <dd><p>A logical indicating whether the leave-one-out fitted values
 should be returned. Defaults to <code>FALSE</code>.</p></dd>
 
 
-<dt>se.fit</dt>
+<dt id="arg-se-fit">se.fit<a class="anchor" aria-label="anchor" href="#arg-se-fit"></a></dt>
 <dd><p>A logical indicating whether the leave-one-out
 prediction standard errors should be returned. Defaults to <code>FALSE</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>If <code>cv_predict = FALSE</code> and <code>se.fit = FALSE</code>,
+    <p>If <code>cv_predict = FALSE</code> and <code>se.fit = FALSE</code>,
 a tibble indicating several
 leave-one-out cross validation error metrics. If <code>cv_predict = TRUE</code> or <code>se.fit = TRUE</code>,
 a list with elements: <code>stats</code>, a tibble indicating several
@@ -99,8 +85,6 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 with leave-one-out predictions for each observation (if <code>cv_predict = TRUE</code>);
 and <code>se.fit</code>, a numeric vector with leave-one-out prediction standard
 errors for each observation (if <code>se.fit = TRUE</code>).</p>
-
-
 <p>If an <code>ssn_lm</code> object, the cross validation error metrics are:</p><ul><li><p>bias: The average difference between the predicted value and true value</p></li>
 <li><p>std.bias: The average standardized difference between the predicted value and true value</p></li>
 <li><p>MSPE: The average squared difference between the predicted value and true value</p></li>
@@ -143,26 +127,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 1 × 10</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     bias std.bias  MSPE RMSPE std.MSPE   RAV  cor2 cover.80 cover.90 cover.95</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 0.091<span style="text-decoration: underline;">7</span>   0.084<span style="text-decoration: underline;">6</span> 0.235 0.485     1.14 0.384 0.942    0.733    0.844    0.889</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 0.044<span style="text-decoration: underline;">9</span>   0.045<span style="text-decoration: underline;">6</span> 0.273 0.522     1.23 0.418 0.930      0.8    0.889    0.911</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/mf04p.html b/docs/reference/mf04p.html
index 89203ff..48b2d67 100644
--- a/docs/reference/mf04p.html
+++ b/docs/reference/mf04p.html
@@ -1,58 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The MiddleFork04.ssn data folder contains the spatial, attribute,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Imported SSN object from the MiddleFork04.ssn data folder — mf04p • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Imported SSN object from the MiddleFork04.ssn data folder — mf04p"><meta name="description" content="The MiddleFork04.ssn data folder contains the spatial, attribute,
 and topological information needed to construct a spatial stream
 network object using the SSN2 package. mf04p was created
-using ssn_import()."><title>Imported SSN object from the MiddleFork04.ssn data folder — mf04p • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Imported SSN object from the MiddleFork04.ssn data folder — mf04p"><meta property="og:description" content="The MiddleFork04.ssn data folder contains the spatial, attribute,
+using ssn_import()."><meta property="og:description" content="The MiddleFork04.ssn data folder contains the spatial, attribute,
 and topological information needed to construct a spatial stream
 network object using the SSN2 package. mf04p was created
-using ssn_import()."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Imported SSN object from the MiddleFork04.ssn data folder</h1>
+
+      <h1>Imported SSN object from the MiddleFork04.ssn data folder</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/data.R" class="external-link"><code>R/data.R</code></a></small>
       <div class="d-none name"><code>mf04p.Rd</code></div>
     </div>
@@ -80,23 +66,23 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <a href="ssn_create_distmat.html">ssn_create_distmat</a> for details about the distance matrix file structure.</p></div>
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diff --git a/docs/reference/model.frame.SSN2.html b/docs/reference/model.frame.SSN2.html
index a3f9311..cf1cd63 100644
--- a/docs/reference/model.frame.SSN2.html
+++ b/docs/reference/model.frame.SSN2.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Extract the model frame from a fitted model object — model.frame.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract the model frame from a fitted model object — model.frame.SSN2"><meta name="description" content="Extract the model frame from a fitted model object."><meta property="og:description" content="Extract the model frame from a fitted model object."></head><body>
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-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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       </ul></div>
 
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Extract the model frame from a fitted model object</h1>
+
+      <h1>Extract the model frame from a fitted model object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/model.frame.R" class="external-link"><code>R/model.frame.R</code></a></small>
       <div class="d-none name"><code>model.frame.SSN2.Rd</code></div>
     </div>
@@ -57,28 +43,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/model.frame.html" class="external-link">model.frame</a></span><span class="op">(</span><span class="va">formula</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/model.frame.html" class="external-link">model.frame</a></span><span class="op">(</span><span class="va">formula</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>formula</dt>
+
+
+<dl><dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A model frame that contains the variables used by the formula
+    <p>A model frame that contains the variables used by the formula
 for the fitted model object.</p>
     </div>
     <div class="section level2">
@@ -103,70 +89,70 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/model.frame.html" class="external-link">model.frame</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    Summer_mn ELEV_DEM</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1      11.38     1977</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2      10.72     1984</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3      10.43     1993</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4      10.13     2007</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5      10.12     2009</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6       9.81     2012</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7       9.76     2023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8       9.77     2023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9       9.53     2026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10     12.57     1988</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11     12.42     2013</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12     14.10     2015</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13     13.22     2038</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14     13.08     2052</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15     12.19     2066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16     11.32     2085</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17     12.11     1975</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18     11.82     1982</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19     11.91     1999</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20     11.41     2002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21     11.71     1997</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22     11.54     2006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23     11.14     2026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24     12.06     2036</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25     12.09     2046</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26     11.96     2050</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27     11.02     2071</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28     11.16     2016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29      8.75     2034</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30      9.02     2042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31     14.61     1958</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32     14.92     1947</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33     14.72     1952</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34     15.22     1923</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35     14.49     1932</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36     15.29     1940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37     15.07     1940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38     14.94     1945</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39     14.99     1948</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40     15.02     1950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41     15.11     1949</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42     14.70     1950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43     14.58     1951</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44     13.70     2015</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45     10.21     2077</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1      14.92     1947</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2      14.72     1952</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3      14.61     1958</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4      15.22     1923</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5      14.49     1932</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6      15.29     1940</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7      15.07     1940</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8      14.94     1945</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9      14.99     1948</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10     15.02     1950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11     15.11     1949</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12     14.70     1950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13     14.58     1951</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14     11.38     1977</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15     10.72     1984</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16     10.43     1993</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17     10.13     2007</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18     10.12     2009</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19      9.81     2012</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20      9.76     2023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21      9.77     2023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22      9.53     2026</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23     12.57     1988</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24     12.42     2013</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 25     14.10     2015</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 26     13.70     2015</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 27     13.22     2038</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 28     13.08     2052</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 29     12.19     2066</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 30     11.32     2085</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 31     12.11     1975</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 32     11.82     1982</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 33     11.91     1999</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 34     11.41     2002</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 35     11.71     1997</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 36     11.54     2006</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 37     11.14     2026</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 38     12.06     2036</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 39     12.09     2046</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 40     11.96     2050</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 41     11.02     2071</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 42     10.21     2077</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 43     11.16     2016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 44      8.75     2034</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 45      9.02     2042</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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index 870d1b3..a88dcd1 100644
--- a/docs/reference/model.matrix.SSN2.html
+++ b/docs/reference/model.matrix.SSN2.html
@@ -1,52 +1,38 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Extract the model matrix from a fitted model object</h1>
+
+      <h1>Extract the model matrix from a fitted model object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/model.matrix.R" class="external-link"><code>R/model.matrix.R</code></a></small>
       <div class="d-none name"><code>model.matrix.SSN2.Rd</code></div>
     </div>
@@ -57,28 +43,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The model matrix (of the fixed effects), whose rows represent
+    <p>The model matrix (of the fixed effects), whose rows represent
 observations and whose columns represent explanatory variables corresponding
 to each fixed effect.</p>
     </div>
@@ -104,72 +90,72 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    (Intercept) ELEV_DEM</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1            1     1977</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2            1     1984</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3            1     1993</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4            1     2007</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5            1     2009</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6            1     2012</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7            1     2023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8            1     2023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9            1     2026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10           1     1988</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11           1     2013</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12           1     2015</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13           1     2038</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14           1     2052</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15           1     2066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16           1     2085</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17           1     1975</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18           1     1982</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19           1     1999</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20           1     2002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21           1     1997</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22           1     2006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23           1     2026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24           1     2036</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25           1     2046</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26           1     2050</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27           1     2071</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28           1     2016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29           1     2034</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30           1     2042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31           1     1958</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32           1     1947</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33           1     1952</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34           1     1923</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35           1     1932</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36           1     1940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37           1     1940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38           1     1945</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39           1     1948</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40           1     1950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41           1     1949</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42           1     1950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43           1     1951</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44           1     2015</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45           1     2077</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1            1     1947</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2            1     1952</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3            1     1958</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4            1     1923</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5            1     1932</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6            1     1940</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7            1     1940</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8            1     1945</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9            1     1948</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10           1     1950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11           1     1949</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12           1     1950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13           1     1951</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14           1     1977</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15           1     1984</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16           1     1993</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17           1     2007</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18           1     2009</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19           1     2012</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20           1     2023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21           1     2023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22           1     2026</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23           1     1988</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24           1     2013</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 25           1     2015</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 26           1     2015</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 27           1     2038</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 28           1     2052</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 29           1     2066</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 30           1     2085</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 31           1     1975</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 32           1     1982</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 33           1     1999</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 34           1     2002</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 35           1     1997</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 36           1     2006</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 37           1     2026</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 38           1     2036</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 39           1     2046</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 40           1     2050</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 41           1     2071</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 42           1     2077</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 43           1     2016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 44           1     2034</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 45           1     2042</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> attr(,"assign")</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0 1</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/docs/reference/plot.SSN2-1.png b/docs/reference/plot.SSN2-1.png
index c9b3ace..7d3e9db 100644
Binary files a/docs/reference/plot.SSN2-1.png and b/docs/reference/plot.SSN2-1.png differ
diff --git a/docs/reference/plot.SSN2.html b/docs/reference/plot.SSN2.html
index d64372d..e6730b8 100644
--- a/docs/reference/plot.SSN2.html
+++ b/docs/reference/plot.SSN2.html
@@ -1,56 +1,42 @@
 <!DOCTYPE html>
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+and Cook's distance vs leverage."><meta property="og:description" content="Plot fitted model diagnostics such as residuals vs fitted values,
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Plot fitted model diagnostics</h1>
+
+      <h1>Plot fitted model diagnostics</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/plot.R" class="external-link"><code>R/plot.R</code></a></small>
       <div class="d-none name"><code>plot.SSN2.Rd</code></div>
     </div>
@@ -63,35 +49,35 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">which</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">which</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>which</dt>
+<dt id="arg-which">which<a class="anchor" aria-label="anchor" href="#arg-which"></a></dt>
 <dd><p>An integer vector taking on values between 1 and 6, which indicates
 the plots to return. Available plots are described in Details. If <code>which</code>
 has length greater than one, additional plots are stepped through in order
 using <code>&lt;Return&gt;</code>. The default is <code>which = c(1, 2)</code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments passed to other methods.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>No return value. Function called for plotting side effects.</p>
+    <p>No return value. Function called for plotting side effects.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -127,23 +113,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="plot.SSN2-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/docs/reference/plot.Torgegram-1.png b/docs/reference/plot.Torgegram-1.png
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diff --git a/docs/reference/plot.Torgegram.html b/docs/reference/plot.Torgegram.html
index 9e409dc..5fb227e 100644
--- a/docs/reference/plot.Torgegram.html
+++ b/docs/reference/plot.Torgegram.html
@@ -1,52 +1,38 @@
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-      <img src="" class="logo" alt=""><h1>Plot Torgegram</h1>
+
+      <h1>Plot Torgegram</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/plot.R" class="external-link"><code>R/plot.R</code></a></small>
       <div class="d-none name"><code>plot.Torgegram.Rd</code></div>
     </div>
@@ -57,35 +43,35 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for Torgegram</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'Torgegram'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">type</span>, separate <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A Torgegram object from <code><a href="Torgegram.html">Torgegram()</a></code>.</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>The type of semivariogram. Can take character values that are a subset
 of objects in <code>x</code>. The default is <code>names(x)</code>.</p></dd>
 
 
-<dt>separate</dt>
+<dt id="arg-separate">separate<a class="anchor" aria-label="anchor" href="#arg-separate"></a></dt>
 <dd><p>When <code>type</code> is length greater than one, whether each
 <code>type</code> be placed in a separate plot. The default is <code>FALSE</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments passed to other methods.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>No return value. Function called for plotting side effects.</p>
+    <p>No return value. Function called for plotting side effects.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -106,23 +92,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="plot.Torgegram-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
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 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/predict.SSN2.html b/docs/reference/predict.SSN2.html
index e6db3d9..7d49959 100644
--- a/docs/reference/predict.SSN2.html
+++ b/docs/reference/predict.SSN2.html
@@ -1,52 +1,38 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Model predictions (Kriging)</h1>
+
+      <h1>Model predictions (Kriging)</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a>, <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/predict_glm.R" class="external-link"><code>R/predict_glm.R</code></a></small>
       <div class="d-none name"><code>predict.SSN2.Rd</code></div>
     </div>
@@ -57,7 +43,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  <span class="va">newdata</span>,</span>
@@ -68,7 +54,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  <span class="va">newdata</span>,</span>
@@ -84,34 +70,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>newdata</dt>
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
 <dd><p>A character vector that indicates the name of the prediction data set
-in the SSN object for which predictions are desired. If omitted, predictions
+for which predictions are desired (accessible via <code>object$ssn.object$preds</code>).
+Note that the prediction data must be in the original SSN object used to fit the model.
+If <code>newdata</code> is omitted, predictions
 for all prediction data sets are returned. Note that the name <code>".missing"</code>
 indicates the prediction data set that contains the missing observations in the data used
 to fit the model.</p></dd>
 
 
-<dt>se.fit</dt>
+<dt id="arg-se-fit">se.fit<a class="anchor" aria-label="anchor" href="#arg-se-fit"></a></dt>
 <dd><p>A logical indicating if standard errors are returned.
 The default is <code>FALSE</code>.</p></dd>
 
 
-<dt>interval</dt>
+<dt id="arg-interval">interval<a class="anchor" aria-label="anchor" href="#arg-interval"></a></dt>
 <dd><p>Type of interval calculation. The default is <code>"none"</code>.
 Other options are <code>"confidence"</code> (for confidence intervals) and
 <code>"prediction"</code> (for prediction intervals).</p></dd>
 
 
-<dt>level</dt>
+<dt id="arg-level">level<a class="anchor" aria-label="anchor" href="#arg-level"></a></dt>
 <dd><p>Tolerance/confidence level. The default is <code>0.95</code>.</p></dd>
 
 
-<dt>block</dt>
+<dt id="arg-block">block<a class="anchor" aria-label="anchor" href="#arg-block"></a></dt>
 <dd><p>A logical indicating whether a block prediction over the entire
 region in <code>newdata</code> should be returned. The default is <code>FALSE</code>, which returns point
 predictions for each location in <code>newdata</code>. Currently only available for
@@ -119,21 +109,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>family</code> is <code>"gaussian"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>The scale (<code>response</code> or <code>link</code>) of predictions obtained
 using <code>ssn_glm</code> objects.</p></dd>
 
 
-<dt>newdata_size</dt>
+<dt id="arg-newdata-size">newdata_size<a class="anchor" aria-label="anchor" href="#arg-newdata-size"></a></dt>
 <dd><p>The <code>size</code> value for each observation in <code>newdata</code>
 used when predicting for the binomial family.</p></dd>
 
 
-<dt>var_correct</dt>
+<dt id="arg-var-correct">var_correct<a class="anchor" aria-label="anchor" href="#arg-var-correct"></a></dt>
 <dd><p>A logical indicating whether to return the corrected prediction
 variances when predicting via models fit using <code>ssn_glm</code>. The default is
 <code>TRUE</code>.</p></dd>
@@ -141,14 +131,10 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>If <code>se.fit</code> is <code>FALSE</code>, <code>predict.ssn()</code> returns
+    <p>If <code>se.fit</code> is <code>FALSE</code>, <code>predict.ssn()</code> returns
 a vector of predictions or a matrix of predictions with column names
-<code>fit</code>, <code>lwr</code>, and <code>upr</code> if <code>interval</code> is <code>"confidence"</code></p>
-
-
-<p>or <code>"prediction"</code>. If <code>se.fit</code> is <code>TRUE</code>, a list with the following components is returned:</p><ul><li><p><code>fit</code>: vector or matrix as above</p></li>
+<code>fit</code>, <code>lwr</code>, and <code>upr</code> if <code>interval</code> is <code>"confidence"</code>
+or <code>"prediction"</code>. If <code>se.fit</code> is <code>TRUE</code>, a list with the following components is returned:</p><ul><li><p><code>fit</code>: vector or matrix as above</p></li>
 <li><p><code>se.fit:</code> standard error of each fit</p></li>
 </ul></div>
     <div class="section level2">
@@ -178,74 +164,74 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">ssn_mod</span>, <span class="st">"pred1km"</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          1          2          3          4          5          6          7 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.6443346 15.0150979 14.7513549 14.9717229 15.1513874 15.1159790 15.1069520 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.6563815 14.6963559 14.8045294 14.0808258 14.1858577 14.5667842 15.0145065 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          8          9         10         11         12         13         14 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.9863186 14.9038332 14.8702627 14.7308979 14.6992062 14.7579446 14.8764063 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.6297345 14.9009338 15.2402931 15.1355108 15.1875988 15.1627073 14.9902752 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         15         16         17         18         19         20         21 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.3287719 14.4149823 14.0532325 13.9397314 10.9750633 13.1067606 13.6244111 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.7615680 14.6655120 14.2122319 14.0809766 10.5777507 13.0996798 13.7106117 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         22         23         24         25         26         27         28 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10.0996588  5.6054226  8.1409677 12.9269053 13.5019980 11.3419554 11.9747664 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  9.1798694  3.8625882  6.8631036 13.1910592 13.8704178 11.4421093 12.1914869 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         29         30         31         32         33         34         35 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11.2154309 10.2149138 10.4816809 10.7348106  9.7103402 11.3759625 12.2120558 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11.9059944 10.2075486 10.4876498 10.7563715  9.7136328  9.2602313 10.2477095 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         36         37         38         39         40         41         42 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.9616826 12.5664805 12.3028149 13.8079249 13.5395082 12.9715817 13.2152150 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11.1327979 12.4559124 12.4129502 13.7980889 13.5323743 12.9587195 13.2053219 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         43         44         45         46         47         48         49 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.3273384 12.5850380 10.7406072 10.9713095 11.2308341  8.6629974  9.3260313 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.2647206 12.5451214 10.5334812 10.8185903 11.1379170  7.7356815  8.5300384 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         50         51         52         53         54         55         56 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10.2772878 12.1296929 11.7154947 11.1310275 11.5790357 11.7855844 12.0521877 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  9.6657132 12.1276515 11.7448118 11.0619591 11.5867648 11.8614135 12.1651404 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         57         58         59         60         61         62         63 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11.2655590  9.7898915  8.0483307  8.7966695  9.1037886  8.5118636 11.4956913 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11.1660976  9.7461962  7.6051681  8.4940758  9.5695913  7.4720775 10.3052974 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         64         65         66         67         68         69         70 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11.9862160 11.6161901 12.6246359 12.4406987 10.6278901 11.1747555 11.6063313 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10.8934126 10.2716308 11.4632367 11.6943370  9.5595283 10.2041277 10.7122002 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         71         72         73         74         75         76         77 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13.9269581 14.4813989 14.5959969 10.1604842 12.4655751 13.8195311 10.1977736 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14.4973491 14.0748939 14.2079722  8.9680246 11.6946555 13.2951523  8.5007739 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         78         79         80         81         82         83         84 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.5789082 15.4149064 15.5879931 13.6734603 14.5383442 15.1150059 15.3746230 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13.6894328 14.6807917 14.8873005 12.2498400 13.2731761 13.9552643 14.2619799 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         85         86         87         88         89         90         91 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10.0788306  7.9453413  8.7202052  7.5438136 11.1468478  9.4793850 10.2847461 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8.7029556  7.8742831  8.7757780  8.4003990 11.0899999  9.1269008 10.0760823 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         92         93         94         95         96         97         98 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5531965  5.4715321  7.2281978  8.6101730  5.0354923 11.3558953 11.7586313 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3864082  4.2490631  6.3251551  7.9575862  2.5376779  0.9631321  6.9393960 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         99        100        101        102        103        104        105 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.2766556 12.5929234  9.8923651  9.3683314  6.6259182 12.7363403  4.3346894 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8.5366536  6.4191929 -3.3142339 -0.2880692  2.8745096  5.6957589  7.3225656 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        106        107        108        109        110        111        112 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3.8724173  3.5215224  7.0452774  8.3977344  7.1561650 -1.1426926  1.4202968 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5.7764329  8.5758955  1.5118142  3.5878501  8.6968251 14.4646670 13.3400039 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        113        114        115        116        117        118        119 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  4.0985733  6.4886255  7.8698972  7.5852381  9.9485049  3.7673801  5.5245583 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8.0509565 10.7070108 14.8419069 12.8760912 14.2394533  4.4802227  7.2513351 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        120        121        122        123        124        125        126 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  8.5264446 15.2798374 14.2197123  9.2990140 11.5461684 15.5970950 14.1134907 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  9.5447586  5.8262213  8.9258430 10.8651879 10.5039605 13.2731116 14.9161932 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        127        128        129        130        131        132        133 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15.2664073  6.0647130  8.3993355 10.3304465  8.0105836 10.6329256 12.2753107 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12.8312424  6.3336288  9.5137927  4.3386584  8.0150939 11.1795926  8.1475599 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        134        135        136        137        138        139        140 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.3778096 14.7132922 16.0976413 14.2078071  9.4203169 12.1022897  5.5831669 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10.5660098 14.1260092 14.3295653  8.9033099 10.0699457  8.1529409 -4.3851596 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        141        142        143        144        145        146        147 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  8.6941805 11.3728589  9.3402885 11.3857715 15.1158602 15.2888149 11.5265537 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1195973  4.3848365  7.4560414 10.8684912 13.2572061  5.5645821  9.1107947 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        148        149        150        151        152        153        154 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.5079422 10.3691975 -0.6748125  2.9279685  6.7325051  9.3265074 12.2087322 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11.5308779  3.9613491 10.4769224 -2.9452086 12.1802353  7.5331144 10.8708259 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        155        156        157        158        159        160        161 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14.2262895  7.5221930 10.5192746 12.5652217  6.8460851 12.3513293  1.0470183 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10.4096884  5.1723903  8.8549716 10.0791094 10.6195587 10.8187203 11.0860880 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        162        163        164        165        166        167        168 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12.9732758  8.9524078 11.7760066 11.4752712  7.9069483 11.0199167 11.3624102 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.2295281 10.2763951 10.7490625 11.3581890 11.7284046  7.7797240  7.9294044 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        169        170        171        172        173        174        175 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11.8228944 11.9951556 12.2250606  8.7431663  9.2898993  2.3113801  6.7492847 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.9415182 11.1746028  0.6856936  5.8826486  6.5147241 -0.1986592  5.0538075 </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/docs/reference/print.SSN.html b/docs/reference/print.SSN.html
index 63dd9a5..1fe9d34 100644
--- a/docs/reference/print.SSN.html
+++ b/docs/reference/print.SSN.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Print information about the data found in an SSN object."><title>Print SSN object — print.SSN • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print SSN object — print.SSN"><meta property="og:description" content="Print information about the data found in an SSN object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Print SSN object</h1>
+
+      <h1>Print SSN object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/print.SSN.R" class="external-link"><code>R/print.SSN.R</code></a></small>
       <div class="d-none name"><code>print.SSN.Rd</code></div>
     </div>
@@ -57,44 +43,44 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for SSN</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'SSN'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An SSN object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Print summary to console</p>
+    <p>Print summary to console</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/print.SSN2.html b/docs/reference/print.SSN2.html
index f93729e..9f3af53 100644
--- a/docs/reference/print.SSN2.html
+++ b/docs/reference/print.SSN2.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Print fitted model objects and summaries."><title>Print values — print.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print values — print.SSN2"><meta property="og:description" content="Print fitted model objects and summaries."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Print values — print.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Print values — print.SSN2"><meta name="description" content="Print fitted model objects and summaries."><meta property="og:description" content="Print fitted model objects and summaries."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
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+
 
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Print values</h1>
+
+      <h1>Print values</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/print.R" class="external-link"><code>R/print.R</code></a></small>
       <div class="d-none name"><code>print.SSN2.Rd</code></div>
     </div>
@@ -57,13 +43,13 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3L</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3L</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3L</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3L</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for summary.ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'summary.ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3L</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3L</span><span class="op">)</span>,</span>
@@ -71,7 +57,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for summary.ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'summary.ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3L</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3L</span><span class="op">)</span>,</span>
@@ -79,7 +65,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for anova.ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'anova.ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">2L</span>, <span class="fl">3L</span><span class="op">)</span>,</span>
@@ -87,7 +73,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for anova.ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'anova.ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">2L</span>, <span class="fl">3L</span><span class="op">)</span>,</span>
@@ -98,47 +84,47 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code>, a fitted model object from <code><a href="ssn_glm.html">ssn_glm()</a></code>,
 or output from <code>summary(x)</code> or or <code>anova(x)</code>.</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>The number of significant digits to use when printing.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments passed to or from other methods.</p></dd>
 
 
-<dt>signif.stars</dt>
+<dt id="arg-signif-stars">signif.stars<a class="anchor" aria-label="anchor" href="#arg-signif-stars"></a></dt>
 <dd><p>Logical. If <code>TRUE</code>, significance stars are printed for each coefficient</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Printed fitted model objects and summaries with formatting.</p>
+    <p>Printed fitted model objects and summaries with formatting.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/docs/reference/pseudoR2.SSN2.html b/docs/reference/pseudoR2.SSN2.html
index 033e622..5ec3c13 100644
--- a/docs/reference/pseudoR2.SSN2.html
+++ b/docs/reference/pseudoR2.SSN2.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Compute a pseudo r-squared for a fitted model object."><title>Compute a pseudo r-squared — pseudoR2.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compute a pseudo r-squared — pseudoR2.SSN2"><meta property="og:description" content="Compute a pseudo r-squared for a fitted model object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Compute a pseudo r-squared — pseudoR2.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compute a pseudo r-squared — pseudoR2.SSN2"><meta name="description" content="Compute a pseudo r-squared for a fitted model object."><meta property="og:description" content="Compute a pseudo r-squared for a fitted model object."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
-    
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compute a pseudo r-squared</h1>
+
+      <h1>Compute a pseudo r-squared</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/pseudoR2.R" class="external-link"><code>R/pseudoR2.R</code></a></small>
       <div class="d-none name"><code>pseudoR2.SSN2.Rd</code></div>
     </div>
@@ -57,34 +43,34 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/pseudoR2.html" class="external-link">pseudoR2</a></span><span class="op">(</span><span class="va">object</span>, adjust <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/pseudoR2.html" class="external-link">pseudoR2</a></span><span class="op">(</span><span class="va">object</span>, adjust <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>adjust</dt>
+<dt id="arg-adjust">adjust<a class="anchor" aria-label="anchor" href="#arg-adjust"></a></dt>
 <dd><p>A logical indicating whether the pseudo r-squared
 should be adjusted to account for the number of explanatory variables. The
 default is <code>FALSE</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The pseudo r-squared as a numeric vector.</p>
+    <p>The pseudo r-squared as a numeric vector.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -113,26 +99,26 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/pseudoR2.html" class="external-link">pseudoR2</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.6688124</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.5854895</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html
index ddf8676..1dd3aa6 100644
--- a/docs/reference/reexports.html
+++ b/docs/reference/reexports.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="These objects are imported from other packages. Follow the links
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Objects exported from other packages — reexports • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Objects exported from other packages — reexports"><meta name="description" content="These objects are imported from other packages. Follow the links
 below to see their documentation.
 
   generics
@@ -10,7 +10,7 @@
 AICc, covmatrix, dispersion_initial, dispersion_params, glances, loocv, pseudoR2, randcov_initial, randcov_params, varcomp
 
 
-"><title>Objects exported from other packages — reexports • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Objects exported from other packages — reexports"><meta property="og:description" content="These objects are imported from other packages. Follow the links
+"><meta property="og:description" content="These objects are imported from other packages. Follow the links
 below to see their documentation.
 
   generics
@@ -21,54 +21,40 @@
 AICc, covmatrix, dispersion_initial, dispersion_params, glances, loocv, pseudoR2, randcov_initial, randcov_params, varcomp
 
 
-"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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-      <img src="" class="logo" alt=""><h1>Objects exported from other packages</h1>
+
+      <h1>Objects exported from other packages</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/utils.R" class="external-link"><code>R/utils.R</code></a></small>
       <div class="d-none name"><code>reexports.Rd</code></div>
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diff --git a/docs/reference/residuals.SSN2.html b/docs/reference/residuals.SSN2.html
index d25f38a..6ac8ac4 100644
--- a/docs/reference/residuals.SSN2.html
+++ b/docs/reference/residuals.SSN2.html
@@ -1,54 +1,40 @@
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+resid is an alias."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Extract fitted model residuals</h1>
+
+      <h1>Extract fitted model residuals</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/residuals.R" class="external-link"><code>R/residuals.R</code></a></small>
       <div class="d-none name"><code>residuals.SSN2.Rd</code></div>
     </div>
@@ -60,32 +46,34 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">resid</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"response"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_lm</span></span>
+<span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">rstandard</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"deviance"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">resid</a></span><span class="op">(</span><span class="va">object</span>, type <span class="op">=</span> <span class="st">"deviance"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">rstandard</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p><code>"response"</code> for response residuals, <code>"pearson"</code>
 for Pearson residuals, or <code>"standardized"</code> for standardized residuals.
 For <code><a href="ssn_lm.html">ssn_lm()</a></code> fitted model objects, the default is <code>"response"</code>.
@@ -93,19 +81,17 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 available (<code>"deviance"</code>) and are the default residual type.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 
-<dt>model</dt>
+<dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The residuals as a numeric vector.</p>
+    <p>The residuals as a numeric vector.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -135,74 +121,74 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           1           2           3           4           5           6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.96157851 -3.41982277 -3.45042255 -3.34691108 -3.29926659 -3.52279984 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.50256099  0.47318349  0.56793047 -0.01642697 -0.43930648  0.63368951 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           7           8           9          10          11          12 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.25575512 -3.24575512 -3.39928838 -1.45453378 -0.88397759  0.85366690 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.41368951  0.45431200  0.60668549  0.70493449  0.76080999  0.38493449 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          13          14          15          16          17          18 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.63657859  0.90009006  0.41360153  0.09122423 -2.28922300 -2.37746727 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.29905899 -2.01370406 -2.43483257 -2.41771208 -2.23996911 -2.18172011 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          19          20          21          22          23          24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.79748906 -2.21102232 -2.05513356 -1.96573333 -1.78928838 -0.58106590 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.38934662 -2.06397713 -2.05397713 -2.19160364 -0.44833458  0.25477788 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          25          26          27          28          29          30 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.26284342 -0.27755443 -0.61228724 -2.05751085 -3.94871040 -3.44813242 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.00302688  1.60302688  1.90789034  2.24563332  1.83337629  1.61174176 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          31          32          33          34          35          36 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.27920121 -0.28624593 -0.34213470 -0.67797988 -1.14857965 -0.11800167 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.35195305 -1.40308157 -0.73296509 -1.13059160 -1.00121409 -0.86409361 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          37          38          39          40          41          42 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.33800167 -0.32389043 -0.18742369 -0.09977919 -0.03860144 -0.41977919 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.58160364  0.67964134  1.05088633  1.05738432  0.83399878  0.22874577 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          43          44          45 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.51095694  0.45366690 -1.24935375 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.90284862 -2.69860765 -2.15561167 </span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">resid</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           1           2           3           4           5           6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.96157851 -3.41982277 -3.45042255 -3.34691108 -3.29926659 -3.52279984 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.50256099  0.47318349  0.56793047 -0.01642697 -0.43930648  0.63368951 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           7           8           9          10          11          12 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.25575512 -3.24575512 -3.39928838 -1.45453378 -0.88397759  0.85366690 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.41368951  0.45431200  0.60668549  0.70493449  0.76080999  0.38493449 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          13          14          15          16          17          18 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.63657859  0.90009006  0.41360153  0.09122423 -2.28922300 -2.37746727 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.29905899 -2.01370406 -2.43483257 -2.41771208 -2.23996911 -2.18172011 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          19          20          21          22          23          24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.79748906 -2.21102232 -2.05513356 -1.96573333 -1.78928838 -0.58106590 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.38934662 -2.06397713 -2.05397713 -2.19160364 -0.44833458  0.25477788 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          25          26          27          28          29          30 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.26284342 -0.27755443 -0.61228724 -2.05751085 -3.94871040 -3.44813242 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.00302688  1.60302688  1.90789034  2.24563332  1.83337629  1.61174176 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          31          32          33          34          35          36 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.27920121 -0.28624593 -0.34213470 -0.67797988 -1.14857965 -0.11800167 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.35195305 -1.40308157 -0.73296509 -1.13059160 -1.00121409 -0.86409361 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          37          38          39          40          41          42 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.33800167 -0.32389043 -0.18742369 -0.09977919 -0.03860144 -0.41977919 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.58160364  0.67964134  1.05088633  1.05738432  0.83399878  0.22874577 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          43          44          45 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.51095694  0.45366690 -1.24935375 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.90284862 -2.69860765 -2.15561167 </span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/influence.measures.html" class="external-link">rstandard</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           1           2           3           4           5           6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.31417513 -0.63086161 -0.82725055 -0.24495775  0.03588943 -1.27769827 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.54482527  0.24902815  0.62481728  0.49000705 -1.25153528  0.58378939 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>           7           8           9          10          11          12 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.04013139  0.09354178 -0.46311916 -1.01690582 -1.17757570  2.07589613 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.24979138 -0.70645636  0.42458717  0.82105356  0.66286002  0.15454106 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          13          14          15          16          17          18 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.48880470  2.10969532 -0.63391890 -0.79209177 -1.13939584 -1.70158717 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.16911790 -1.93108065 -0.94482469 -0.92746033 -0.30105744 -0.08748646 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          19          20          21          22          23          24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.19810814 -1.25985662 -0.31945666 -0.45882772 -0.27215334  1.97924217 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.03751283 -0.14027557 -0.10922866 -0.52442537 -0.25655729 -1.50792407 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          25          26          27          28          29          30 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.44190300  0.40506009 -0.76461961  0.17969367 -1.35425063 -1.70206611 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.88733195  0.08022385  0.84150045  2.10218328  0.49121189  0.62967096 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          31          32          33          34          35          36 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.13160477  0.08275306 -0.25793553  0.49251121 -2.33954032  1.22724195 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.94695811 -1.16773136  0.66216711 -0.89530470 -0.36015318 -0.34642484 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          37          38          39          40          41          42 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.06535745  0.03484793 -0.03803679  0.52173449  1.15188186 -0.57040629 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.34733356  2.77614973  0.15975430  0.24945263  0.41507374 -0.37899919 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          43          44          45 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.03295041 -0.49183235 -0.56160115 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.41832097 -1.69498836 -2.69823469 </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
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+++ b/docs/reference/ssn_create_distmat.html
@@ -1,56 +1,42 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Calculate Hydrologic Distances for an <code>SSN</code> object</h1>
+
+      <h1>Calculate Hydrologic Distances for an <code>SSN</code> object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_create_distmat.R" class="external-link"><code>R/ssn_create_distmat.R</code></a></small>
       <div class="d-none name"><code>ssn_create_distmat.Rd</code></div>
     </div>
@@ -74,36 +60,36 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ssn.object</dt>
+
+
+<dl><dt id="arg-ssn-object">ssn.object<a class="anchor" aria-label="anchor" href="#arg-ssn-object"></a></dt>
 <dd><p>An <code>SSN</code> object</p></dd>
 
 
-<dt>predpts</dt>
+<dt id="arg-predpts">predpts<a class="anchor" aria-label="anchor" href="#arg-predpts"></a></dt>
 <dd><p>name of prediction points in an <code>SSN</code> object.
 When a vector with length greater than one, each name is iterated upon.
 Default is NULL.</p></dd>
 
 
-<dt>overwrite</dt>
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
 <dd><p>Logical. If <code>TRUE</code>, overwrite existing distance
 matrices. Defaults to <code>FALSE</code>.</p></dd>
 
 
-<dt>among_predpts</dt>
+<dt id="arg-among-predpts">among_predpts<a class="anchor" aria-label="anchor" href="#arg-among-predpts"></a></dt>
 <dd><p>Logical. If <code>TRUE</code>, compute the pairwise distances
 between the prediction sites. Defaults to <code>FALSE</code>.</p></dd>
 
 
-<dt>only_predpts</dt>
+<dt id="arg-only-predpts">only_predpts<a class="anchor" aria-label="anchor" href="#arg-only-predpts"></a></dt>
 <dd><p>Logical. If <code>TRUE</code>, only compute distances for
 prediction sites. Defaults to <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The <code class="command">ssn_create_distmat</code> function creates a collection
+    <p>The <code class="command">ssn_create_distmat</code> function creates a collection
 of hierarchical directories in the <code>ssn$path</code> directory,
 which store the pairwise distances between sites associated with
 the <code>SSN</code> object. See details section for additional information.</p>
@@ -198,7 +184,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="copy_lsn_to_temp.html">copy_lsn_to_temp</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co">## Import SSN data</span></span></span>
 <span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km.shp"</span>, <span class="st">"Knapp"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km.gpkg"</span>, <span class="st">"CapeHorn"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -213,28 +199,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Distance matrices for observations and pred1km prediction sites are</span></span></span>
 <span class="r-in"><span><span class="co">## not recalculated.</span></span></span>
 <span class="r-in"><span><span class="fu">ssn_create_distmat</span><span class="op">(</span><span class="va">mf04p</span>,</span></span>
-<span class="r-in"><span>  predpts <span class="op">=</span> <span class="st">"Knapp"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
+<span class="r-in"><span>  predpts <span class="op">=</span> <span class="st">"CapeHorn"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
 <span class="r-in"><span>  among_predpts <span class="op">=</span> <span class="cn">TRUE</span>, only_predpts <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/docs/reference/ssn_get_data.html b/docs/reference/ssn_get_data.html
index d8c47b0..4fc7ff3 100644
--- a/docs/reference/ssn_get_data.html
+++ b/docs/reference/ssn_get_data.html
@@ -1,56 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The ssn_get_data function extracts an
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data"><meta name="description" content="The ssn_get_data function extracts an
 sf data.frame for the observation or prediction data from
-an SSN, ssn_lm, or ssn_glm object."><title>Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data"><meta property="og:description" content="The ssn_get_data function extracts an
+an SSN, ssn_lm, or ssn_glm object."><meta property="og:description" content="The ssn_get_data function extracts an
 sf data.frame for the observation or prediction data from
-an SSN, ssn_lm, or ssn_glm object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+an SSN, ssn_lm, or ssn_glm object."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a>
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-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Get a data.frame from an SSN, ssn_lm, or ssn_glm object</h1>
+
+      <h1>Get a data.frame from an SSN, ssn_lm, or ssn_glm object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_get_data.R" class="external-link"><code>R/ssn_get_data.R</code></a></small>
       <div class="d-none name"><code>ssn_get_data.Rd</code></div>
     </div>
@@ -68,11 +54,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of class <code>SSN</code>, <code>ssn_lm</code>, or <code>ssn_glm</code>.</p></dd>
 
 
-<dt>name</dt>
+<dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>the internal name of the dataset in the object
 <code>x</code>. For observed values, this will always be "obs", the
 default.</p></dd>
@@ -80,9 +68,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An sf data.frame</p>
+    <p>An sf data.frame</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -119,10 +105,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"</span>
 <span class="r-in"><span><span class="va">pred1km.df</span> <span class="op">&lt;-</span> <span class="fu">ssn_get_data</span><span class="op">(</span><span class="va">mf04p</span>, name <span class="op">=</span> <span class="st">"pred1km"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">pred1km.df</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] "COMID"     "GNIS_NAME" "CDRAINAG"  "AREAWTMAP" "SLOPE"     "ELEV_DEM" </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] "FlowCMS"   "AirMEANc"  "AirMWMTc"  "NEAR_X"    "NEAR_Y"    "rid"      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [13] "ratio"     "afvArea"   "upDist"    "locID"     "netID"     "pid"      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] "geometry"  "netgeom"  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] "rid"        "pid"        "COMID"      "AREAWTMAP"  "SLOPE"     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6] "ELEV_DEM"   "FlowCMS"    "AirMEANc"   "AirMWMTc"   "rcaAreaKm2"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] "h2oAreaKm2" "ratio"      "snapdist"   "upDist"     "afvArea"   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16] "locID"      "netID"      "netgeom"    "geom"      </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## extract observed data from an ssn_lm object</span></span></span>
 <span class="r-in"><span><span class="va">ssn_mod</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_lm.html">ssn_lm</a></span><span class="op">(</span></span></span>
@@ -133,74 +119,74 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">obs.mod.df</span> <span class="op">&lt;-</span> <span class="fu">ssn_get_data</span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">obs.mod.df</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   STREAMNAME            COMID             CDRAINAG         AREAWTMAP     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Length:45          Min.   :23519297   Min.   :  8.643   Min.   : 786.4  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Class :character   1st Qu.:23519365   1st Qu.: 60.136   1st Qu.: 968.2  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Mode  :character   Median :23519479   Median : 93.295   Median : 995.2  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     Mean   :23519557   Mean   :136.482   Mean   : 998.6  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     3rd Qu.:23519529   3rd Qu.:171.534   3rd Qu.:1032.7  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     Max.   :23522805   Max.   :477.696   Max.   :1130.4  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      SLOPE             ELEV_DEM       Source            Summer_mn    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :0.000000   Min.   :1923   Length:45          Min.   : 8.75  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.:0.002740   1st Qu.:1952   Class :character   1st Qu.:11.02  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Median :0.005680   Median :2006   Mode  :character   Median :12.06  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   :0.006743   Mean   :1999                      Mean   :12.35  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:0.008430   3rd Qu.:2026                      3rd Qu.:14.58  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :0.044260   Max.   :2085                      Max.   :15.29  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    MaxOver20           C16            C20              C24       FlowCMS     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :0.0000   Min.   : 0.0   Min.   : 0.000   Min.   :0   Min.   :28.67  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.:0.0000   1st Qu.:17.0   1st Qu.: 0.000   1st Qu.:0   1st Qu.:28.67  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Median :0.0000   Median :32.0   Median : 0.000   Median :0   Median :28.67  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   :0.3111   Mean   :26.4   Mean   : 2.867   Mean   :0   Mean   :28.67  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:1.0000   3rd Qu.:39.0   3rd Qu.: 3.000   3rd Qu.:0   3rd Qu.:28.67  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :1.0000   Max.   :41.0   Max.   :19.000   Max.   :0   Max.   :28.67  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     AirMEANc        AirMWMTc        NEAR_X             NEAR_Y       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :21.12   Min.   :35.1   Min.   :-1530805   Min.   :2527111  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.:21.12   1st Qu.:35.1   1st Qu.:-1520336   1st Qu.:2529903  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Median :21.12   Median :35.1   Median :-1512691   Median :2533285  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   :21.12   Mean   :35.1   Mean   :-1514996   Mean   :2532817  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:21.12   3rd Qu.:35.1   3rd Qu.:-1508544   3rd Qu.:2535462  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :21.12   Max.   :35.1   Max.   :-1503079   Max.   :2537823  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       rid             ratio          afvArea              upDist       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :  0.00   Min.   :0.0143   Length:45          Min.   :  909.9  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.: 20.00   1st Qu.:0.1967   Class :character   1st Qu.: 6281.2  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Median : 41.00   Median :0.4720   Mode  :character   Median :10020.0  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   : 41.27   Mean   :0.4564                      Mean   :10176.7  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.: 60.00   3rd Qu.:0.6936                      3rd Qu.:14295.2  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :109.00   Max.   :0.9739                      Max.   :19566.2  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      locID        netID            pid              geometry </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   : 1   Min.   :1.000   Min.   : 1   POINT        :45  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.:12   1st Qu.:1.000   1st Qu.:12   epsg:NA      : 0  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Median :23   Median :2.000   Median :23   +proj=aea ...: 0  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   :23   Mean   :1.711   Mean   :23                     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:34   3rd Qu.:2.000   3rd Qu.:34                     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :45   Max.   :2.000   Max.   :45                     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    netgeom         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Length:45         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Class :character  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Mode  :character  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>       rid              pid      STREAMNAME            COMID         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :  1.00   Min.   : 1   Length:45          Min.   :23519297  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.: 21.00   1st Qu.:12   Class :character   1st Qu.:23519365  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Median : 42.00   Median :23   Mode  :character   Median :23519479  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   : 42.27   Mean   :23                      Mean   :23519557  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.: 61.00   3rd Qu.:34                      3rd Qu.:23519529  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :110.00   Max.   :45                      Max.   :23522805  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    AREAWTMAP          SLOPE             ELEV_DEM       Source         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   : 786.4   Min.   :0.000000   Min.   :1923   Length:45         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.: 968.2   1st Qu.:0.002740   1st Qu.:1952   Class :character  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Median : 995.2   Median :0.005680   Median :2006   Mode  :character  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   : 998.6   Mean   :0.006743   Mean   :1999                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:1032.7   3rd Qu.:0.008430   3rd Qu.:2026                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :1130.4   Max.   :0.044260   Max.   :2085                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    Summer_mn       MaxOver20           C16            C20              C24   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   : 8.75   Min.   :0.0000   Min.   : 0.0   Min.   : 0.000   Min.   :0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.:11.02   1st Qu.:0.0000   1st Qu.:17.0   1st Qu.: 0.000   1st Qu.:0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Median :12.06   Median :0.0000   Median :32.0   Median : 0.000   Median :0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   :12.35   Mean   :0.3111   Mean   :26.4   Mean   : 2.867   Mean   :0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:14.58   3rd Qu.:1.0000   3rd Qu.:39.0   3rd Qu.: 3.000   3rd Qu.:0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :15.29   Max.   :1.0000   Max.   :41.0   Max.   :19.000   Max.   :0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     FlowCMS         AirMEANc        AirMWMTc      rcaAreaKm2    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :28.67   Min.   :21.12   Min.   :35.1   Min.   :0.0234  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.:28.67   1st Qu.:21.12   1st Qu.:35.1   1st Qu.:0.8613  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Median :28.67   Median :21.12   Median :35.1   Median :2.1780  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   :28.67   Mean   :21.12   Mean   :35.1   Mean   :2.2958  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:28.67   3rd Qu.:21.12   3rd Qu.:35.1   3rd Qu.:3.3993  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :28.67   Max.   :21.12   Max.   :35.1   Max.   :5.0175  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    h2oAreaKm2          ratio           snapdist             upDist       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :  3.399   Min.   :0.0143   Min.   :0.000e+00   Min.   :  909.9  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.: 16.790   1st Qu.:0.1967   1st Qu.:0.000e+00   1st Qu.: 6281.2  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Median : 29.092   Median :0.4720   Median :0.000e+00   Median :10020.0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   : 36.063   Mean   :0.4565   Mean   :5.174e-12   Mean   :10176.9  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.: 46.260   3rd Qu.:0.6936   3rd Qu.:0.000e+00   3rd Qu.:14295.2  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :124.250   Max.   :0.9739   Max.   :1.164e-10   Max.   :19566.2  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     afvArea            locID        netID         netgeom         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :0.05144   Min.   : 1   Min.   :1.000   Length:45         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1st Qu.:0.15212   1st Qu.:12   1st Qu.:1.000   Class :character  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Median :0.17089   Median :23   Median :2.000   Mode  :character  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Mean   :0.24583   Mean   :23   Mean   :1.711                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3rd Qu.:0.29944   3rd Qu.:34   3rd Qu.:2.000                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Max.   :1.00000   Max.   :45   Max.   :2.000                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           geometry </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  POINT        :45  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  epsg:NA      : 0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  +proj=aea ...: 0  </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                    </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 58cedf4..dbcb5d5 100644
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+++ b/docs/reference/ssn_get_netgeom.html
@@ -1,53 +1,39 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Extract netgeom column</h1>
-      <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_get_netgeometry.R" class="external-link"><code>R/ssn_get_netgeometry.R</code></a></small>
+
+      <h1>Extract netgeom column</h1>
+      <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_get_netgeom.R" class="external-link"><code>R/ssn_get_netgeom.R</code></a></small>
       <div class="d-none name"><code>ssn_get_netgeom.Rd</code></div>
     </div>
 
@@ -62,12 +48,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An sf data.frame found in an <code>SSN</code> object or the
 netgeom column as a vector</p></dd>
 
 
-<dt>netvars</dt>
+<dt id="arg-netvars">netvars<a class="anchor" aria-label="anchor" href="#arg-netvars"></a></dt>
 <dd><p>Network coordinate variables to return. Default is
 "all". For edges, valid column names include: "NetworkID",
 "SegmentID", and "DistanceUpstream". For point datasets, valid column
@@ -75,15 +63,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 and "locID".</p></dd>
 
 
-<dt>reformat</dt>
+<dt id="arg-reformat">reformat<a class="anchor" aria-label="anchor" href="#arg-reformat"></a></dt>
 <dd><p>Convert network coordinate variables from character to numeric.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>If more than one column is requested using netvars, the
+    <p>If more than one column is requested using netvars, the
 function returns a data.frame (default). If only one column is
 requested, the result is a vector.</p>
     </div>
@@ -116,236 +102,236 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span><span class="va">temp_path</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">ssn_get_netgeom</span><span class="op">(</span><span class="va">mf04p</span><span class="op">$</span><span class="va">obs</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    NetworkID SegmentID DistanceUpstream           ratio pid locID</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1          2        32    3194.82758834  0.321185040918   1     1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2          2        33    3896.10674037 0.0690361937938   2     2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3          2        33    4857.50889816  0.370693074276   3     3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4          2        33    5886.68489225  0.693615191033   4     4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5          2        33    6281.22686216   0.81740969149   5     5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6          2        35    7300.18896771  0.472020290969   6     6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7          2        36    8386.87883527  0.467609736278   7     7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8          2        36    8626.98482356  0.595431065021   8     8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9          2        38    10019.9727397  0.481795562545   9     9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10         2        41    4049.94559903  0.241892607278  10    10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11         2        47    8914.29845837  0.356571475119  11    11</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12         2        48     9295.3266467  0.223896901083  12    12</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13         2        51    12659.9947331  0.171145194729  13    13</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14         2        51    14103.2031154  0.799102017173  14    14</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15         2        52    16019.2025746  0.601882501114  15    15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16         2        54    18447.5789988  0.132889204232  16    16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17         2        57    3405.72135314  0.609185232313  17    17</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18         2        57    4046.04417336  0.973908806041  18    18</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19         2        59    5365.42224132  0.845110196768  19    19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20         2        60    6489.59766227  0.885802604708  20    20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21         2        60    5794.51481162  0.258397975972  21    21</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22         2        62    7547.78605505  0.128723349502  22    22</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23         2        63    10022.9334715   0.70355047499  23    23</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24         2        65     10879.708177  0.078434677104  24    24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25         2        66    11985.9719107 0.0893958570497  25    25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26         2        66    12357.0088971  0.242744463686  26    26</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27         2        67    15080.7113189  0.665890884643  27    27</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28         2        70    9921.48731634  0.881800162966  28    28</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29         2        94     10871.508857  0.196749884783  29    29</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30         2       109    11795.0008893 0.0160819902947  30    30</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31         1         0    16786.5620802   0.79067935202  31    31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32         1         0    14295.1963452 0.0143033570212  32    32</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33         1         0    16258.1596546  0.626015066404  33    33</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34         1         4    909.868496941  0.231794110614  34    34</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35         1         4    1692.25764135  0.515501826192  35    35</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36         1         9    8046.69965981  0.280932180239  36    36</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37         1        12    10443.8838685  0.613552417712  37    37</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38         1        14    13865.3815622  0.907439930469  38    38</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39         1        16    14323.7279441  0.182376231574  39    39</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40         1        16     14580.785008  0.812239349337  40    40</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41         1        18    15652.4273972   0.54977020396  41    41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42         1        20    18642.8233007  0.157200906378  42    42</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43         1        20    19566.2045371  0.639335640746  43    43</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44         2        48    9711.54632884  0.751115282016  44    44</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45         2        68    15672.8306471 0.0751878474962  45    45</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    NetworkID SegmentID DistanceUpstream              ratio pid locID</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1          1         1  14295.196345228 0.0143033570193725   1     1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2          1         1 16258.1867133034  0.626023498621516   2     2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3          1         1 16794.9565779269  0.793295301344197   3     3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4          1         5 909.937835040871  0.231819253798763   4     4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5          1         5 1692.25764134473  0.515501826191856   5     5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6          1        10 8046.69959654784   0.28093206947971   6     6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7          1        13 10443.8838684547  0.613552417711556   7     7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8          1        15 13865.3815621499  0.907439930466464   8     8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9          1        17 14323.7279440717  0.182376231507493   9     9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10         1        17 14580.7850080222  0.812239349337638  10    10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11         1        19 15652.4273971773  0.549770203960467  11    11</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12         1        21 18642.8232807059  0.157200895942302  12    12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13         1        21 19566.2045370585  0.639335640749524  13    13</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14         2        33 3194.82758834595  0.321185040919459  14    14</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15         2        34 3896.10674038296 0.0690361937952187  15    15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16         2        34 4857.50889815636  0.370693074274211  16    16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17         2        34 5886.68489225268  0.693615191031243  17    17</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18         2        34 6281.22686215765  0.817409691487961  18    18</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19         2        36 7300.18896771736  0.472020290982274  19    19</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20         2        37 8386.87883528115  0.467609736277533  20    20</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21         2        37 8626.98482357545  0.595431065021748  21    21</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22         2        39 10019.9727397032  0.481795562544374  22    22</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23         2        42 4049.94559903902  0.241892607277062  23    23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24         2        48  8914.2984583901  0.356571475118011  24    24</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 25         2        49 9295.32664671222  0.223896901080731  25    25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 26         2        49 9711.54632884272  0.751115282023418  26    26</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 27         2        52 12658.4900937506  0.170490508605362  27    27</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 28         2        52 14103.2031153509  0.799102017170274  28    28</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 29         2        53 16019.2025746132  0.601882501113124  29    29</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 30         2        55 18447.5789987791  0.132889204229652  30    30</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 31         2        58 3405.72135312965  0.609185232315584  31    31</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 32         2        58 4046.04417334624  0.973908806043524  32    32</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 33         2        60 5365.42224130655  0.845110196771577  33    33</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 34         2        61 6489.59759943874  0.885802548015722  34    34</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 35         2        61 5794.51481160791  0.258397975972908  35    35</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 36         2        63 7547.78605502196  0.128723349502858  36    36</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 37         2        64 10022.9334714274  0.703550475006261  37    37</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 38         2        66 10879.7081769225  0.078434677103365  38    38</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 39         2        67 11985.9719106275 0.0893958570488306  39    39</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 40         2        67 12357.0088970759  0.242744463683594  40    40</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 41         2        68 15080.7113188755  0.665890884639618  41    41</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 42         2        69 15672.8306470736 0.0751878474972271  42    42</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 43         2        71 9921.48731634449  0.881800162965071  43    43</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 44         2        95  10871.508857035  0.196749884784435  44    44</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 45         2       110 11795.0008892232 0.0160819902932108  45    45</span>
 <span class="r-in"><span><span class="fu">ssn_get_netgeom</span><span class="op">(</span><span class="va">mf04p</span><span class="op">$</span><span class="va">edges</span>, <span class="st">"DistanceUpstream"</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     DistanceUpstream</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> 2      18357.2013474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3      20793.1520844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4      270.643033817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5      3028.37256124</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6      4402.46334121</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7       5256.1104882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8      6616.51936215</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9      7886.30445018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10     8457.24370497</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11     9024.57776818</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12     9536.51353251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13      11015.393429</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14      12325.976268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15     14022.4029322</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16     14249.2973301</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17     14657.4130884</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18     14790.6182827</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19     16358.1988978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20     18341.7531704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21      20256.946519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22     8056.91207735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23     9607.96481233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24     13105.9441562</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25     14112.2130889</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26     16613.9926597</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27     5611.09385126</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28     7959.14006425</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29     1556.81337221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30     1920.27673835</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31     2336.21218485</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32     2967.11873356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33     3676.08342921</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34     6863.15529791</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35     7113.95880148</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36     7508.49727746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37     9386.94738161</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38     9805.94712673</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39     10250.1720836</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40     10289.8733396</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41     13963.1651297</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42      5221.6543089</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43     5793.50030353</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44     6280.45578932</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45     7277.66171417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46     7449.52533783</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47     8801.09743647</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48     9118.56820965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49     9908.03173594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50     11074.7483485</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51     12266.6584864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52     14564.9190207</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53     16981.1440334</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54     17918.0916672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55     21902.5186538</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56      25336.584133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57     26164.3010726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58     4091.85088316</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59     4586.16095742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60     5508.24341114</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61     6616.11354599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62     7197.21170564</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63     9920.68301648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64     10066.0179407</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65     10803.9630453</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66     11769.6727859</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67       14189.23821</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68     15528.0057611</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69     17454.1798817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70     20006.2582619</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71     10029.1136375</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72     10892.8779616</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73     11270.8487206</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74     11844.1591731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75     13763.1476372</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76     12715.7735595</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77     14546.8345236</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78     17838.6435898</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79     8887.67517889</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80     8891.97355539</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81     12839.2250328</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82     5433.90934003</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83     6124.91189817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84       6267.876827</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85     6677.35714099</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86     8073.26914058</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87     11585.4944924</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88     12057.2455603</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89      13058.816991</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90      19026.619806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91     18924.7499077</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92      19525.005383</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93     19880.9175089</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94     21486.7688274</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95     13246.0911694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96     14399.5534169</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97     10396.7058431</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98     12938.8411906</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99     17726.9196396</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100    15870.5572204</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101     20989.122938</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102    15125.2684793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103    15389.8725133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104    14793.2194916</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105    14808.0000677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106    18347.8931324</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107    19583.0855759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108    14729.3812838</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109    6772.63827193</span>
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 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
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diff --git a/docs/reference/ssn_get_stream_distmat.html b/docs/reference/ssn_get_stream_distmat.html
index ac77d11..5714fde 100644
--- a/docs/reference/ssn_get_stream_distmat.html
+++ b/docs/reference/ssn_get_stream_distmat.html
@@ -1,54 +1,40 @@
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-observation or prediction data from an SSN object."><title>Get stream distance matrices from an SSN object — ssn_get_stream_distmat • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Get stream distance matrices from an SSN object — ssn_get_stream_distmat"><meta property="og:description" content="Extracts the stream network distance matrices for the
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+observation or prediction data from an SSN object."><meta property="og:description" content="Extracts the stream network distance matrices for the
+observation or prediction data from an SSN object."></head><body>
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-      <img src="" class="logo" alt=""><h1>Get stream distance matrices from an <code>SSN</code> object</h1>
+
+      <h1>Get stream distance matrices from an <code>SSN</code> object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_get_stream_distmat.R" class="external-link"><code>R/ssn_get_stream_distmat.R</code></a></small>
       <div class="d-none name"><code>ssn_get_stream_distmat.Rd</code></div>
     </div>
@@ -65,11 +51,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An <code>SSN</code> object</p></dd>
 
 
-<dt>name</dt>
+<dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>Internal name of the dataset in the object
 <code>x</code>. For observed values, this will always be "obs", the
 default.  To get a stream network distance matrix for a
@@ -79,9 +67,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></code> of asymmetric downstream only
+    <p>A <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></code> of asymmetric downstream only
 stream distance matrices, by network.</p>
     </div>
     <div class="section level2">
@@ -146,7 +132,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
     <p>Ver Hoef, J.M. and Peterson, E.E. (2010) A moving
 average approach to spatial statistical models of stream
 networks. The Journal of the American Statistical Association,
-<b>105(489)</b>, 22--24</p>
+<b>105(489)</b>, 22–24</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -165,12 +151,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Create distance matrices for obs x obs, obs x preds, and preds x</span></span></span>
 <span class="r-in"><span><span class="co">## preds</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="ssn_create_distmat.html">ssn_create_distmat</a></span><span class="op">(</span><span class="va">mf04p</span>,</span></span>
 <span class="r-in"><span>  predpts <span class="op">=</span> <span class="st">"pred1km"</span>, among_predpts <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Check names of prediction datasets</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">mf04p</span><span class="op">$</span><span class="va">preds</span><span class="op">)</span></span></span>
@@ -181,132 +167,132 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Display structure of list and names of the matrices</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">dist_obs</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> List of 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1: num [1:13, 1:13] 0 2491 528 15877 15094 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1: num [1:13, 1:13] 0 0 0 13385 12603 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "31" "32" "33" "34" ...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "31" "32" "33" "34" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "1" "2" "3" "4" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "1" "2" "3" "4" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net2: num [1:32, 1:32] 0 0 0 0 0 0 0 0 0 0 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "1" "2" "3" "4" ...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "1" "2" "3" "4" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "14" "15" "16" "17" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "14" "15" "16" "17" ...</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dist_obs</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "dist.net1" "dist.net2"</span>
 <span class="r-in"><span><span class="co">## Look at first 5 rows and columns in asymmetric</span></span></span>
 <span class="r-in"><span><span class="co">## downstream only distance matrix for netID == 1</span></span></span>
 <span class="r-in"><span><span class="va">dist_obs</span><span class="op">$</span><span class="va">dist.net1</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            31       32        33 34       35</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31     0.0000     0.00     0.000  0   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32  2491.3657     0.00  1962.963  0   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33   528.4024     0.00     0.000  0   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34 15876.6936 13385.33 15348.291  0 782.3891</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35 15094.3044 12602.94 14565.902  0   0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          1        2          3 4        5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0.00  1962.99  2499.7602 0   0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2     0.00     0.00   536.7699 0   0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3     0.00     0.00     0.0000 0   0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 13385.26 15348.25 15885.0187 0 782.3198</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 12602.94 14565.93 15102.6989 0   0.0000</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Create symmetric total stream distance matrix between</span></span></span>
 <span class="r-in"><span><span class="co">## observations</span></span></span>
 <span class="r-in"><span><span class="va">strdist_2</span> <span class="op">&lt;-</span> <span class="va">dist_obs</span><span class="op">$</span><span class="va">dist.net2</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html" class="external-link">t</a></span><span class="op">(</span><span class="va">dist_obs</span><span class="op">$</span><span class="va">dist.net2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">strdist_2</span><span class="op">[</span><span class="fl">5</span><span class="op">:</span><span class="fl">10</span>, <span class="fl">5</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           5        6        7        8        9       10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     0.000 1018.962 2105.652 2345.758 3738.746 2979.006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1018.962    0.000 1086.690 1326.796 2719.784 3997.968</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  2105.652 1086.690    0.000  240.106 1633.094 5084.658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  2345.758 1326.796  240.106    0.000 1392.988 5324.764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  3738.746 2719.784 1633.094 1392.988    0.000 6717.751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 2979.006 3997.968 5084.658 5324.764 6717.751    0.000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          18       19       20       21       22       23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18    0.000 1018.962 2105.652 2345.758 3738.746 2979.006</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19 1018.962    0.000 1086.690 1326.796 2719.784 3997.968</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20 2105.652 1086.690    0.000  240.106 1633.094 5084.658</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21 2345.758 1326.796  240.106    0.000 1392.988 5324.764</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22 3738.746 2719.784 1633.094 1392.988    0.000 6717.751</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23 2979.006 3997.968 5084.658 5324.764 6717.751    0.000</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get maximum downstream only distance between</span></span></span>
 <span class="r-in"><span><span class="co">## observations on netID == 2</span></span></span>
 <span class="r-in"><span><span class="va">a.mat</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">pmax</a></span><span class="op">(</span><span class="va">dist_obs</span><span class="op">$</span><span class="va">dist.net2</span>, <span class="fu"><a href="https://rdrr.io/r/base/t.html" class="external-link">t</a></span><span class="op">(</span><span class="va">dist_obs</span><span class="op">$</span><span class="va">dist.net2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a.mat</span><span class="op">[</span><span class="fl">5</span><span class="op">:</span><span class="fl">10</span>, <span class="fl">5</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           5        6        7        8        9       10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     0.000 1018.962 2105.652 2345.758 3738.746 2605.143</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1018.962    0.000 1086.690 1326.796 2719.784 3624.106</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  2105.652 1086.690    0.000  240.106 1633.094 4710.795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  2345.758 1326.796  240.106    0.000 1392.988 4950.901</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  3738.746 2719.784 1633.094 1392.988    0.000 6343.889</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 2605.143 3624.106 4710.795 4950.901 6343.889    0.000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          18       19       20       21       22       23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18    0.000 1018.962 2105.652 2345.758 3738.746 2605.143</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19 1018.962    0.000 1086.690 1326.796 2719.784 3624.106</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20 2105.652 1086.690    0.000  240.106 1633.094 4710.795</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21 2345.758 1326.796  240.106    0.000 1392.988 4950.901</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22 3738.746 2719.784 1633.094 1392.988    0.000 6343.889</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23 2605.143 3624.106 4710.795 4950.901 6343.889    0.000</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get minimum downstream only distance between observations. If</span></span></span>
 <span class="r-in"><span><span class="co">## minimum distance == 0, sites are flow-connected</span></span></span>
 <span class="r-in"><span><span class="va">b.mat</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">pmin</a></span><span class="op">(</span><span class="va">dist_obs</span><span class="op">$</span><span class="va">dist.net2</span>, <span class="fu"><a href="https://rdrr.io/r/base/t.html" class="external-link">t</a></span><span class="op">(</span><span class="va">dist_obs</span><span class="op">$</span><span class="va">dist.net2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">b.mat</span><span class="op">[</span><span class="fl">5</span><span class="op">:</span><span class="fl">10</span>, <span class="fl">5</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           5        6        7        8        9       10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 373.8622 373.8622 373.8622 373.8622 373.8622   0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          18       19       20       21       22       23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23 373.8622 373.8622 373.8622 373.8622 373.8622   0.0000</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Get distance matrices for pred1km</span></span></span>
 <span class="r-in"><span><span class="va">dist_pred1km</span> <span class="op">&lt;-</span> <span class="fu">ssn_get_stream_distmat</span><span class="op">(</span><span class="va">mf04p</span>, name <span class="op">=</span> <span class="st">"pred1km"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">dist_pred1km</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> List of 6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1  : num [1:57, 1:57] 0 1000 0 0 0 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1  : num [1:57, 1:57] 0 1000 2000 0 0 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:57] "46" "47" "48" "49" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:57] "46" "47" "48" "49" ...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1.a: num [1:13, 1:57] 0 0 0 1119 336 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1.a: num [1:13, 1:57] 2163 200 0 15548 14766 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "31" "32" "33" "34" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "1" "2" "3" "4" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:57] "46" "47" "48" "49" ...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1.b: num [1:57, 1:13] 14758 15758 13384 11170 9900 ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net1.b: num [1:57, 1:13] 0 0 0 0 0 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:57] "46" "47" "48" "49" ...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "31" "32" "33" "34" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:13] "1" "2" "3" "4" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net2  : num [1:118, 1:118] 0 0 0 0 0 0 0 0 0 0 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:118] "74" "75" "76" "77" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:118] "74" "75" "76" "77" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net2.a: num [1:32, 1:118] 0 0 0 0 0 0 0 0 0 0 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "1" "2" "3" "4" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "14" "15" "16" "17" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:118] "74" "75" "76" "77" ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  $ dist.net2.b: num [1:118, 1:32] 2638 0 0 0 0 ...</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ..- attr(*, "dimnames")=List of 2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:118] "74" "75" "76" "77" ...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "1" "2" "3" "4" ...</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   .. ..$ : chr [1:32] "14" "15" "16" "17" ...</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dist_pred1km</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "dist.net1"   "dist.net1.a" "dist.net1.b" "dist.net2"   "dist.net2.a"</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [6] "dist.net2.b"</span>
 <span class="r-in"><span><span class="co">## Look at first 5 rows and columns of downstream only distances</span></span></span>
 <span class="r-in"><span><span class="co">## FROM prediction sites TO observed sites on netID == 1</span></span></span>
 <span class="r-in"><span><span class="va">dist_pred1km</span><span class="op">$</span><span class="va">dist.net1.a</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           46       47       48       49       50</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31    0.0000   0.0000    0.000    0.000    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32    0.0000   0.0000    0.000    0.000    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33    0.0000   0.0000    0.000    0.000    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34 1118.5041 118.5041 2492.595 4706.651 5976.436</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35  336.1149   0.0000 1710.206 3924.262 5194.047</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           46        47        48        49        50</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  2163.0690  1163.069   163.069  5497.956  4497.956</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   200.0786     0.000     0.000  3534.965  2534.965</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3     0.0000     0.000     0.000  2998.196  1998.196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4 15548.3275 14548.328 13548.328 18883.214 17883.214</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5 14766.0077 13766.008 12766.008 18100.894 17100.894</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Look at downstream only stream distances among prediction</span></span></span>
 <span class="r-in"><span><span class="co">## sites in pred1km on netID == 1. This is useful for block</span></span></span>
 <span class="r-in"><span><span class="co">## prediction</span></span></span>
 <span class="r-in"><span><span class="va">dist_pred1km</span><span class="op">$</span><span class="va">dist.net1</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      46 47       48       49       50</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46    0  0 1374.091 3588.147 4857.932</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47 1000  0 2374.091 4588.147 5857.932</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48    0  0    0.000 2214.056 3483.841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49    0  0    0.000    0.000 1269.785</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50    0  0    0.000    0.000    0.000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      46   47 48       49       50</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 46    0    0  0 3334.887 2334.887</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 47 1000    0  0 4334.887 3334.887</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 48 2000 1000  0 5334.887 4334.887</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 49    0    0  0    0.000    0.000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50    0    0  0 1000.000    0.000</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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index 91c584e..e00c9d2 100644
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+++ b/docs/reference/ssn_glm.html
@@ -1,60 +1,46 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function works on spatial stream network objects to fit
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm"><meta name="description" content="This function works on spatial stream network objects to fit
 generalized linear models with spatially autocorrelated errors using likelihood methods, allowing for
 non-spatial random effects, anisotropy, partition factors, big data methods, and more.
 The spatial formulation is described in Ver Hoef and Peterson (2010)
-and Peterson and Ver Hoef (2010)."><title>Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm"><meta property="og:description" content="This function works on spatial stream network objects to fit
+and Peterson and Ver Hoef (2010)."><meta property="og:description" content="This function works on spatial stream network objects to fit
 generalized linear models with spatially autocorrelated errors using likelihood methods, allowing for
 non-spatial random effects, anisotropy, partition factors, big data methods, and more.
 The spatial formulation is described in Ver Hoef and Peterson (2010)
-and Peterson and Ver Hoef (2010)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+and Peterson and Ver Hoef (2010)."></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fitting Generalized Linear Models for Spatial Stream Networks</h1>
+
+      <h1>Fitting Generalized Linear Models for Spatial Stream Networks</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_glm.R" class="external-link"><code>R/ssn_glm.R</code></a></small>
       <div class="d-none name"><code>ssn_glm.Rd</code></div>
     </div>
@@ -94,17 +80,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>formula</dt>
+
+
+<dl><dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>A two-sided linear formula describing the fixed effect structure
 of the model, with the response to the left of the <code>~</code> operator and
 the terms on the right, separated by <code>+</code> operators.</p></dd>
 
 
-<dt>ssn.object</dt>
+<dt id="arg-ssn-object">ssn.object<a class="anchor" aria-label="anchor" href="#arg-ssn-object"></a></dt>
 <dd><p>A spatial stream network object with class <code>SSN</code>.</p></dd>
 
 
-<dt>family</dt>
+<dt id="arg-family">family<a class="anchor" aria-label="anchor" href="#arg-family"></a></dt>
 <dd><p>The generalized linear model family for use with <code>ssn_glm()</code>.
 Available options include <code>"Gaussian"</code>, <code>"poisson"</code>,
 <code>"nbinomial"</code> (negative binomial), <code>"binomial"</code>, <code>"beta"</code>,
@@ -115,21 +103,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 See Details for more.</p></dd>
 
 
-<dt>tailup_type</dt>
+<dt id="arg-tailup-type">tailup_type<a class="anchor" aria-label="anchor" href="#arg-tailup-type"></a></dt>
 <dd><p>The tailup covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>. Parameterizations are
 described in Details.</p></dd>
 
 
-<dt>taildown_type</dt>
+<dt id="arg-taildown-type">taildown_type<a class="anchor" aria-label="anchor" href="#arg-taildown-type"></a></dt>
 <dd><p>The taildown covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>. Parameterizations are
 described in Details.</p></dd>
 
 
-<dt>euclid_type</dt>
+<dt id="arg-euclid-type">euclid_type<a class="anchor" aria-label="anchor" href="#arg-euclid-type"></a></dt>
 <dd><p>The euclidean covariance function type. Available options
 include <code>"spherical"</code>, <code>"exponential"</code>, <code>"gaussian"</code>,
 <code>"cosine"</code>, <code>"cubic"</code>, <code>"pentaspherical"</code>, <code>"wave"</code>,
@@ -138,38 +126,38 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 described in Details.</p></dd>
 
 
-<dt>nugget_type</dt>
+<dt id="arg-nugget-type">nugget_type<a class="anchor" aria-label="anchor" href="#arg-nugget-type"></a></dt>
 <dd><p>The nugget covariance function type. Available options
 include <code>"nugget"</code> or <code>"none"</code>. Parameterizations are
 described in Details.</p></dd>
 
 
-<dt>tailup_initial</dt>
+<dt id="arg-tailup-initial">tailup_initial<a class="anchor" aria-label="anchor" href="#arg-tailup-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">tailup_initial()</a></code> specifying initial and/or
 known values for the tailup covariance parameters.</p></dd>
 
 
-<dt>taildown_initial</dt>
+<dt id="arg-taildown-initial">taildown_initial<a class="anchor" aria-label="anchor" href="#arg-taildown-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">taildown_initial()</a></code> specifying initial and/or
 known values for the taildown covariance parameters.</p></dd>
 
 
-<dt>euclid_initial</dt>
+<dt id="arg-euclid-initial">euclid_initial<a class="anchor" aria-label="anchor" href="#arg-euclid-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">euclid_initial()</a></code> specifying initial and/or
 known values for the euclidean covariance parameters.</p></dd>
 
 
-<dt>nugget_initial</dt>
+<dt id="arg-nugget-initial">nugget_initial<a class="anchor" aria-label="anchor" href="#arg-nugget-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">nugget_initial()</a></code> specifying initial and/or
 known values for the nugget covariance parameters.</p></dd>
 
 
-<dt>dispersion_initial</dt>
+<dt id="arg-dispersion-initial">dispersion_initial<a class="anchor" aria-label="anchor" href="#arg-dispersion-initial"></a></dt>
 <dd><p>An object from <code><a href="https://usepa.github.io/spmodel/reference/dispersion_initial.html" class="external-link">dispersion_initial()</a></code> specifying initial and/or
 known values for the tailup covariance parameters.</p></dd>
 
 
-<dt>additive</dt>
+<dt id="arg-additive">additive<a class="anchor" aria-label="anchor" href="#arg-additive"></a></dt>
 <dd><p>The name of the variable in <code>ssn.object</code> that is used
 to define spatial weights. Can be quoted or unquoted. For the tailup covariance functions, these additive
 weights are used for branching. Technical details that describe the role
@@ -177,13 +165,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 in Ver Hoef and Peterson (2010).</p></dd>
 
 
-<dt>estmethod</dt>
+<dt id="arg-estmethod">estmethod<a class="anchor" aria-label="anchor" href="#arg-estmethod"></a></dt>
 <dd><p>The estimation method. Available options include
 <code>"reml"</code> for restricted maximum likelihood and <code>"ml"</code> for maximum
 likelihood. The default is <code>"reml"</code>.</p></dd>
 
 
-<dt>anisotropy</dt>
+<dt id="arg-anisotropy">anisotropy<a class="anchor" aria-label="anchor" href="#arg-anisotropy"></a></dt>
 <dd><p>A logical indicating whether (geometric) anisotropy should
 be modeled. Not required if <code>spcov_initial</code> is provided with 1) <code>rotate</code>
 assumed unknown or assumed known and non-zero or 2) <code>scale</code> assumed unknown
@@ -191,7 +179,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 computational times can significantly increase. The default is <code>FALSE</code>.</p></dd>
 
 
-<dt>random</dt>
+<dt id="arg-random">random<a class="anchor" aria-label="anchor" href="#arg-random"></a></dt>
 <dd><p>A one-sided linear formula describing the random effect structure
 of the model. Terms are specified to the right of the <code>~ operator</code>.
 Each term has the structure <code>x1 + ... + xn | g1/.../gm</code>, where <code>x1 + ... + xn</code>
@@ -207,26 +195,24 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 parentheses can be omitted.</p></dd>
 
 
-<dt>randcov_initial</dt>
+<dt id="arg-randcov-initial">randcov_initial<a class="anchor" aria-label="anchor" href="#arg-randcov-initial"></a></dt>
 <dd><p>An optional object specifying initial and/or
 known values for the random effect variances. See <code><a href="https://usepa.github.io/spmodel/reference/randcov_initial.html" class="external-link">spmodel::randcov_initial()</a></code>.</p></dd>
 
 
-<dt>partition_factor</dt>
+<dt id="arg-partition-factor">partition_factor<a class="anchor" aria-label="anchor" href="#arg-partition-factor"></a></dt>
 <dd><p>A one-sided linear formula with a single term
 specifying the partition factor.  The partition factor assumes observations
 from different levels of the partition factor are uncorrelated.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments to <code><a href="https://rdrr.io/r/stats/optim.html" class="external-link">stats::optim()</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list with many elements that store information about
+    <p>A list with many elements that store information about
 the fitted model object and has class <code>ssn_glm</code>. Many generic functions that
 summarize model fit are available for <code>ssn_glm</code> objects, including
 <code>AIC</code>, <code>AICc</code>, <code>anova</code>, <code>augment</code>, <code>coef</code>,
@@ -235,8 +221,6 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <code>labels</code>, <code>logLik</code>, <code>loocv</code>, <code>model.frame</code>, <code>model.matrix</code>,
 <code>plot</code>, <code>predict</code>, <code>print</code>, <code>pseudoR2</code>, <code>summary</code>,
 <code>terms</code>, <code>tidy</code>, <code>update</code>, <code>varcomp</code>, and <code>vcov</code>.</p>
-
-
 <p>This fitted model list contains the following elements:</p><ul><li><p><code>additive</code>: The name of the additive function value column.</p></li>
 <li><p><code>anisotropy</code>: Whether euclidean anisotropy was modeled.</p></li>
 <li><p><code>call</code>: The function call.</p></li>
@@ -444,15 +428,15 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Garreta, V., Monestiez, P. and Ver Hoef, J.M. (2010) Spatial modelling and
 prediction on river networks: up model, down model, or hybrid?
-<em>Environmetrics</em> <b>21(5)</b>, 439--456.</p>
+<em>Environmetrics</em> <b>21(5)</b>, 439–456.</p>
 <p>McCullagh P. and Nelder, J. A. (1989) <em>Generalized Linear Models</em>. London: Chapman and Hall.</p>
 <p>Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. <em>Ecology</em> <b>91(3)</b>,
-644--651.</p>
+644–651.</p>
 <p>Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-<em>Journal of the American Statistical Association</em> <b>105</b>, 6--18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.</p>
+<em>Journal of the American Statistical Association</em> <b>105</b>, 6–18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.</p>
     </div>
 
     <div class="section level2">
@@ -479,44 +463,44 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Deviance Residuals:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        Min         1Q     Median         3Q        Max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0125918 -0.0028720 -0.0006556  0.0027528  0.0157742 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.296e-03 -9.345e-04  4.352e-05  4.850e-04  5.457e-03 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients (fixed):</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               Estimate Std. Error z value Pr(&gt;|z|)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)  7.4233120  0.5207267  14.256   &lt;2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.0024085  0.0002589  -9.301   &lt;2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)  8.4238415  0.7860602  10.717  &lt; 2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.0029545  0.0003907  -7.563 3.95e-14 ***</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Pseudo R-squared: 0.6477</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Pseudo R-squared: 0.606</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients (covariance):</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               Effect     Parameter   Estimate</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential  de (parsill)  3.265e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential         range  1.006e+06</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               nugget        nugget  2.110e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           dispersion    dispersion  8.114e+03</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential  de (parsill)  1.058e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential         range  9.184e+04</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               nugget        nugget  2.898e-04</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           dispersion    dispersion  2.328e+04</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/ssn_import.html b/docs/reference/ssn_import.html
index 53d8560..45629e2 100644
--- a/docs/reference/ssn_import.html
+++ b/docs/reference/ssn_import.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function reads spatial data from a .ssn folder
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Import SSN object — ssn_import • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Import SSN object — ssn_import"><meta name="description" content="This function reads spatial data from a .ssn folder
+and creates an SSN object."><meta property="og:description" content="This function reads spatial data from a .ssn folder
+and creates an SSN object."></head><body>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Import <code>SSN</code> object</h1>
+
+      <h1>Import <code>SSN</code> object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_import.R" class="external-link"><code>R/ssn_import.R</code></a></small>
       <div class="d-none name"><code>ssn_import.Rd</code></div>
     </div>
@@ -60,104 +46,69 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ssn_import</span><span class="op">(</span></span>
-<span>  <span class="va">path</span>,</span>
-<span>  include_obs <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">predpts</span>,</span>
-<span>  format_additive <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  names_additive <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  overwrite <span class="op">=</span> <span class="cn">FALSE</span></span>
-<span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ssn_import</span><span class="op">(</span><span class="va">path</span>, include_obs <span class="op">=</span> <span class="cn">TRUE</span>, predpts <span class="op">=</span> <span class="cn">NULL</span>, overwrite <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>path</dt>
+
+
+<dl><dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>Filepath to the .ssn directory. See details.</p></dd>
 
 
-<dt>include_obs</dt>
+<dt id="arg-include-obs">include_obs<a class="anchor" aria-label="anchor" href="#arg-include-obs"></a></dt>
 <dd><p>default = <code>TRUE</code>. Logical indicating
 whether observed sites should be included in the SSN object.</p></dd>
 
 
-<dt>predpts</dt>
-<dd><p>Vector of shapefile basenames for prediction sites
-found within the .ssn folder.</p></dd>
-
+<dt id="arg-predpts">predpts<a class="anchor" aria-label="anchor" href="#arg-predpts"></a></dt>
+<dd><p>Vector of prediction site dataset names found within
+the .ssn folder. See details.</p></dd>
 
-<dt>format_additive</dt>
-<dd><p>Logical indicating whether the columns containing
-the addtive function values should be formated for
-<code>SSN2</code>. Default = <code>FALSE</code>.</p></dd>
 
-
-<dt>names_additive</dt>
-<dd><p>Character vector of column names in observed and
-prediction site datasets containing additive function values.
-Must be defined if <code>format_additive = TRUE</code>. Default =
-<code>NULL</code>.</p></dd>
-
-
-<dt>overwrite</dt>
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
 <dd><p>default = <code>FALSE</code>. If <code>TRUE</code>, overwrite
-existing binaryID.db files.</p></dd>
+existing binaryID.db files and netgeom column(s) if it exists in the
+edges, observed sites (if <code>include_obs = TRUE</code>), and
+prediction site datasets (if they exist).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p><code>ssn_import</code> returns an object of class SSN, which is a list
+    <p><code>ssn_import</code> returns an object of class SSN, which is a list
 with four elements containing:</p><ul><li><p><code>edges</code>: An <code>sf data.frame</code> containing the stream network,
 with an additional 'netgeom' column.</p></li>
 <li><p><code>obs</code>: An sf data.frame containing observed site locations,
 with an additional 'netgeom' column. NA if <code>include_obs =
     FALSE</code>.</p></li>
 <li><p><code>preds</code>: A list of sf data.frames containing prediction
-site locations. The names of the preds list correspond to the
-basenames of the prediction site shapefiles (without the .shp
-extension) specified in <code>predpts</code>. Empty list if <code>predpts</code> is not provided.</p></li>
-<li><p>path: The local file to the .ssn directory associated with the <code>SSN</code>
+site locations. An empty list is returned if <code>predpts</code> is not provided.</p></li>
+<li><p>path: The local filepath for the .ssn directory associated with the <code>SSN</code>
 object.</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>The <code class="command">importSSN</code> function imports spatial data from a .ssn
-folder to create an <code>SSN</code> object. The information contained in the
-.ssn folder can be generated using a number of proprietary and
-open source software tools:</p><ul><li><p>The Spatial Tools for the Analysis of River Systems
-(STARS) tools for ArcGIS Desktop versions 9.3x-10.8x (Peterson
-and Ver Hoef 2014). This custom ArcGIS toolset is designed to
-work with existing streams data in vector format.</p></li>
-<li><p>The openSTARS package (Kattwinkel et al. 2020) extends
-the functionality of the STARS toolset, which makes use of R
-and GRASS GIS. It is open source and designed to derive streams
-in raster format from a digital elevation model (DEM).</p></li>
-<li><p>The SSNbler package (currently in development as of
-September 2023) is an open source version of the STARS toolset,
-which makes use of the functionality found in the sf package to
-process streams data in vector format.</p></li>
-</ul><p>When spatial data are processed using one of these software
-tools, a .ssn directory is output which contains all of the
-spatial, topological and attribute data needed to fit a spatial
-statistical stream network model to streams data.  This includes:</p><ul><li><p>An edges shapefile of lines that represent the stream
-network.</p></li>
-<li><p>A sites shapefile of points where observed data were
-collected on the stream network.</p></li>
-<li><p>Prediction sites shapefile(s) of locations where
-predictions will be made.</p></li>
-<li><p>netID.dat files for each distinct network, which store
-the topological relationships of the line segments in edges.</p></li>
+    <p>The <code class="command">ssn_import</code> function imports spatial data (shapefile or GeoPackage format)
+from a .ssn folder generated using the
+<code>SSNbler</code> package function <code>SSNbler::lsn_to_ssn</code>. The .ssn folder contains all of the spatial, topological and
+attribute data needed to fit a spatial statistical stream network
+model to streams data.  This includes:</p><ul><li><p>An edges dataset with LINESTRING geometry representing the stream network.</p></li>
+<li><p>A sites dataset with POINT geometry where observed data were collected on
+the stream network.</p></li>
+<li><p>Prediction sites dataset(s) representing
+locations where predictions will be made.</p></li>
+<li><p>netID.dat file(s) for each distinct network, which stores the topological
+relationships of the line features in edges.</p></li>
 </ul><p>A more detailed description of the .ssn directory and its
 contents is provided in Peterson and Ver Hoef (2014).</p>
-<p>The <code class="command">ssn_import</code> imports the edges, observed sites, and
-prediction sites as <code>sf data.frame</code> objects. A new column named 'netgeom'
-is created to store important data that represents
+<p>The <code class="command">ssn_import</code> imports the edges, observed sites (optional), and
+prediction sites (optional) as <code>sf data.frame</code> objects. A new column named 'netgeom'
+is created to store important data representing
 topological relationships in a spatial stream network
 model. These data are stored in character format, which is less
 likely to be inadvertantly changed by users. See
-<code><a href="ssn_get_netgeom.html">ssn_get_netgeom</a></code> for a more detailed description of
+<code><a href="create_netgeom.html">create_netgeom</a></code> for a more detailed description of
 the format and contents of 'netgeom'.</p>
 <p>The information contained in the netID text files is imported
 into an SQLite database, binaryID.db, which is stored in the .ssn
@@ -169,36 +120,33 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <code>overwrite = TRUE</code> (<code>overwrite = FALSE</code> is the default) and a binaryID.db
 file already exists within the .ssn directory, it will be
 overwriten when the <code>SSN</code> object is created.</p>
-<p>At a minimum, an <code>SSN</code> object must always contain streams, which
-are referred to as edges. The <code>SSN</code> object would also typically
-contain a set of observed sites, where measurements have been
-collected and only one observed dataset is permitted. When
-<code>include_obs=FALSE</code>, an <code>SSN</code> object is created without
-observations. This option provides flexibility for users who
-would like to simulate data on a set of artifical sites on an
-existing stream network. Note that observation sites must be
-included in the <code>SSN</code> object in order to fit models using
-<code class="command">ssn_lm</code> or <code class="command">ssn_glm</code>. The <code>SSN</code> object may contain
-multiple sets of prediction points (or none), which are stored as
-separate shapefiles in the .ssn directory. The
-<code><a href="ssn_import_predpts.html">ssn_import_predpts</a></code> function allows users to import additional
-sets of prediction sites to a an existing <code>SSN</code> object.</p>
+<p>At a minimum, an <code>SSN</code> object must always contain streams,
+which are referred to as edges. The <code>SSN</code> object would also
+typically contain a set of observed sites, where measurements
+have been collected. Only one observed dataset is permitted in an
+<code>SSN</code> object. When <code>include_obs=FALSE</code>, an <code>SSN</code>
+object is created without observations. This option provides
+flexibility for users who would like to simulate data on a set of
+artifical sites on an existing stream network. Note that
+observation sites must be included in the <code>SSN</code> object in
+order to fit models using <code class="command">ssn_lm</code> or
+<code class="command">ssn_glm</code>. The <code>SSN</code> object may contain multiple
+sets of prediction points (or none), which are stored as separate
+datasets in the .ssn directory. If <code>predpts</code> is a named
+vector, the names of the <code>preds</code> list in the <code>SSN</code>
+object correspond to the vector names. Otherwise, they are set to
+the basename of the prediction site dataset file(s) specified in
+<code>predpts</code>. The <code><a href="ssn_import_predpts.html">ssn_import_predpts</a></code>
+function allows users to import additional sets of prediction
+sites to a an existing <code>SSN</code> object.</p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Kattwinkel, M., Szocs, E., Peterson, E., and Schafer,
-R.B. (2020) Preparing GIS data for analysis of stream monitoring
-data: The R package openSTARS. <em>PLOS One</em> <b>15(9)</b>,
-e0239237.
-Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
+    <p>Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
 ArcGIS toolset used to calculate the spatial information needed
 to fit spatial statistical stream network models to stream
 network data. <em>Journal of Statistical Software</em>
-<b>56(2)</b>, 1--17.</p>
-    </div>
-    <div class="section level2">
-    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p><a href="ssn_get_netgeom.html">ssn_get_netgeom</a></p></div>
+<b>56(2)</b>, 1–17.</p>
     </div>
 
     <div class="section level2">
@@ -215,32 +163,31 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Import SSN object with 3 sets of prediction sites</span></span></span>
 <span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu">ssn_import</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>    <span class="st">"pred1km.shp"</span>,</span></span>
-<span class="r-in"><span>    <span class="st">"CapeHorn.shp"</span>,</span></span>
-<span class="r-in"><span>    <span class="st">"Knapp.shp"</span></span></span>
+<span class="r-in"><span>    <span class="st">"pred1km"</span>,</span></span>
+<span class="r-in"><span>    <span class="st">"CapeHorn"</span></span></span>
 <span class="r-in"><span>  <span class="op">)</span>,</span></span>
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/ssn_import_predpts.html b/docs/reference/ssn_import_predpts.html
index 89f69fd..5c8403a 100644
--- a/docs/reference/ssn_import_predpts.html
+++ b/docs/reference/ssn_import_predpts.html
@@ -1,56 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A shapefile of prediction points found in the .ssn
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts"><meta name="description" content="A shapefile of prediction points found in the .ssn
 directory are imported into an existing object of class
-SSN, ssn_lm, or ssn_glm."><title>Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts"><meta property="og:description" content="A shapefile of prediction points found in the .ssn
+SSN, ssn_lm, or ssn_glm."><meta property="og:description" content="A shapefile of prediction points found in the .ssn
 directory are imported into an existing object of class
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Import prediction points into an SSN, ssn_lm, or ssn_glm object</h1>
+
+      <h1>Import prediction points into an SSN, ssn_lm, or ssn_glm object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_import_predpts.R" class="external-link"><code>R/ssn_import_predpts.R</code></a></small>
       <div class="d-none name"><code>ssn_import_predpts.Rd</code></div>
     </div>
@@ -63,84 +49,57 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ssn_import_predpts</span><span class="op">(</span><span class="va">x</span>, <span class="va">predpts</span>, format_additive <span class="op">=</span> <span class="cn">FALSE</span>, names_additive <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ssn_import_predpts</span><span class="op">(</span><span class="va">x</span>, <span class="va">predpts</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
-<dd><p>An object of class<code>SSN</code>, <code>ssn_lm</code>, or
-<code>ssn_glm</code>.</p></dd>
-
 
-<dt>predpts</dt>
-<dd><p>Name of the prediction point shapefile to import in
-character format, without the .shp extension.</p></dd>
 
-
-<dt>format_additive</dt>
-<dd><p>Logical indicating whether the columns containing
-the addtive function values should be formated for
-<code>SSN2</code>. Default = <code>FALSE</code>.</p></dd>
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>An object of class<code>SSN</code>, <code>ssn_lm</code>, or
+<code>ssn_glm</code>.</p></dd>
 
 
-<dt>names_additive</dt>
-<dd><p>Character vector of column names in observed and
-prediction site datasets containing additive function
-values. Must be defined if <code>format_additive = TRUE</code>. Default =
-<code>NULL</code>.</p></dd>
+<dt id="arg-predpts">predpts<a class="anchor" aria-label="anchor" href="#arg-predpts"></a></dt>
+<dd><p>Name of the prediction point dataset to import in
+character format. See details.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>an object of class <code>SSN</code>, <code>ssn_lm</code>, or
-<code>ssn_glm</code> which contains the new prediction dataset. The
-name of the prediction dataset in the preds list corresponds to
-the basenames of the prediction site shapefiles (without the .shp
-extension) specified in <code>predpts</code>. See
-<code><a href="ssn_import.html">ssn_import</a></code> for a detailed description of
-the prediction dataset format within the <code>SSN</code> class object.</p>
+    <p>an object of class <code>SSN</code>, <code>ssn_lm</code>, or
+<code>ssn_glm</code> which contains the new prediction dataset.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><code class="command">ssn_import_predpts</code> imports a shapefile of
-prediction points residing in the .ssn directory into an existing
+    <p><code class="command">ssn_import_predpts</code> imports one set of prediction
+points residing in the .ssn directory into an existing
 <code>SSN</code>, <code>ssn_lm</code>, or <code>ssn_glm</code> object. The
-prediction dataset must reside in the ssn.object$path
+prediction dataset must be in shapefile or geopackage format
+(.shp or .gpkg, respectively) and reside in the ssn.object$path
 directory. The path for an <code>SSN</code> object can be updated using
 <code class="command">ssn_update_path()</code> prior to importing prediction
-datasets. Note that, the prediction dataset must contain the
+datasets. The argument <code>predpts</code> accepts the name of the
+prediction point dataset, with or without the file extension. If
+it is passed as a named vector (of length 1), then the name
+provided is used as the prediction dataset name in the <code>SSN</code>
+object prediction sites list
+(e.g. <code>names(ssn.obj$preds)</code>). Otherwise, the file basename
+is used in the names attribute. See
+<code><a href="ssn_import.html">ssn_import</a></code> for a detailed description of the
+prediction dataset format within the <code>SSN</code> class object.</p>
+<p>When the prediction dataset is imported, a new column named
+<code>netgeom</code> is created. If this column already exists it is
+overwritten. Please see <code><a href="create_netgeom.html">create_netgeom</a></code> for a
+detailed description of the <code>netgeom</code> column and the
+information it contains.</p>
+<p>The prediction dataset specified in <code>predpts</code> must contain the
 spatial, topological and attribute information needed to make
-predictions using an ssn_lm or ssn_glm object.  This information
-can be generated using a number of proprietary and open source
-software tools:</p><ul><li><p>The Spatial Tools for the
-Analysis of River Systems (STARS) tools for ArcGIS Desktop
-versions 9.3x-10.8x (Peterson and Ver Hoef 2014). This custom
-ArcGIS toolset is designed to work with existing streams data in
-vector format.</p></li>
-<li><p>The openSTARS package (Kattwinkel et
-al. 2020) extends the functionality of the STARS toolset, which
-makes use of R and GRASS GIS. It is open source and designed to
-derive streams in raster format from a digital elevation model
-(DEM).</p></li>
-<li><p>The SSNbler package (currently in development as
-of September 2023) is an open source version of the STARS
-toolset, which makes use of the functionality found in the sf
-package to process streams data in vector format.</p></li>
-</ul></div>
-    <div class="section level2">
-    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Kattwinkel, M., Szocs, E., Peterson, E., and Schafer,
-R.B. (2020) Preparing GIS data for analysis of stream monitoring
-data: The R package openSTARS. <em>PLOS One</em> <b>15(9)</b>,
-e0239237.
-Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
-ArcGIS toolset used to calculate the spatial information needed
-to fit spatial statistical stream network models to stream
-network data. <em>Journal of Statistical Software</em>
-<b>56(2)</b>, 1--17.</p>
+predictions using an ssn_lm or ssn_glm object. This information
+is generated using the <code>SSNbler</code> package, which makes use of
+the functionality found in the <code>sf</code> and <code>igraph</code>
+packages to process streams data in vector format.</p>
     </div>
 
     <div class="section level2">
@@ -154,40 +113,43 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Import pred1km prediction dataset into SSN object</span></span></span>
-<span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> binaryID.db already exists - no changes were made to binaryID.db table</span>
-<span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu">ssn_import_predpts</span><span class="op">(</span><span class="va">mf04p</span>, predpts <span class="op">=</span> <span class="st">"pred1km"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="co">## Import pred1km prediction dataset into SSN object and assign the</span></span></span>
+<span class="r-in"><span><span class="co">## name preds1</span></span></span>
+<span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu">ssn_import_predpts</span><span class="op">(</span><span class="va">mf04p</span>, predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>preds1 <span class="op">=</span> <span class="st">"pred1km"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">mf04p</span><span class="op">$</span><span class="va">preds</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "preds1"</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Import pred1km prediction dataset into a ssn_glm object</span></span></span>
+<span class="r-in"><span><span class="co">## Import CapeHorn prediction dataset into a ssn_glm object, using</span></span></span>
+<span class="r-in"><span><span class="co">## the default file basename as the name</span></span></span>
 <span class="r-in"><span><span class="va">ssn_gmod</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_glm.html">ssn_glm</a></span><span class="op">(</span><span class="va">Summer_mn</span> <span class="op">~</span> <span class="va">netID</span>, <span class="va">mf04p</span>,</span></span>
 <span class="r-in"><span>  family <span class="op">=</span> <span class="st">"Gamma"</span>,</span></span>
 <span class="r-in"><span>  tailup_type <span class="op">=</span> <span class="st">"exponential"</span>, additive <span class="op">=</span> <span class="st">"afvArea"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">ssn_gmod</span> <span class="op">&lt;-</span> <span class="fu">ssn_import_predpts</span><span class="op">(</span><span class="va">ssn_gmod</span>, predpts <span class="op">=</span> <span class="st">"CapeHorn"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ssn_gmod</span><span class="op">$</span><span class="va">ssn.object</span><span class="op">$</span><span class="va">preds</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"  "CapeHorn"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "preds1"   "CapeHorn"</span>
+<span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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+
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diff --git a/docs/reference/ssn_initial.html b/docs/reference/ssn_initial.html
index 83448b9..f772b8e 100644
--- a/docs/reference/ssn_initial.html
+++ b/docs/reference/ssn_initial.html
@@ -1,58 +1,44 @@
 <!DOCTYPE html>
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 initial and/or known values to use while estimating specific covariance parameters
 with ssn_lm() or ssn_glm().  See spmodel::randcov_initial() for documentation regarding
-random effect covariance parameter initial objects."><title>Create a covariance parameter initial object — ssn_initial • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a covariance parameter initial object — ssn_initial"><meta property="og:description" content="Create a covariance parameter initial object that specifies
+random effect covariance parameter initial objects."><meta property="og:description" content="Create a covariance parameter initial object that specifies
 initial and/or known values to use while estimating specific covariance parameters
 with ssn_lm() or ssn_glm().  See spmodel::randcov_initial() for documentation regarding
-random effect covariance parameter initial objects."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Create a covariance parameter initial object</h1>
+
+      <h1>Create a covariance parameter initial object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/initial.R" class="external-link"><code>R/initial.R</code></a></small>
       <div class="d-none name"><code>ssn_initial.Rd</code></div>
     </div>
@@ -77,34 +63,36 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tailup_type</dt>
+
+
+<dl><dt id="arg-tailup-type">tailup_type<a class="anchor" aria-label="anchor" href="#arg-tailup-type"></a></dt>
 <dd><p>The tailup covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>.</p></dd>
 
 
-<dt>de</dt>
+<dt id="arg-de">de<a class="anchor" aria-label="anchor" href="#arg-de"></a></dt>
 <dd><p>The spatially dependent (correlated) random error variance. Commonly referred to as
 a partial sill.</p></dd>
 
 
-<dt>range</dt>
+<dt id="arg-range">range<a class="anchor" aria-label="anchor" href="#arg-range"></a></dt>
 <dd><p>The correlation parameter.</p></dd>
 
 
-<dt>known</dt>
+<dt id="arg-known">known<a class="anchor" aria-label="anchor" href="#arg-known"></a></dt>
 <dd><p>A character vector indicating which covariance parameters are to be
 assumed known. The value <code>"given"</code> is shorthand for assuming all
 covariance parameters given to <code>*_initial()</code> are assumed known.</p></dd>
 
 
-<dt>taildown_type</dt>
+<dt id="arg-taildown-type">taildown_type<a class="anchor" aria-label="anchor" href="#arg-taildown-type"></a></dt>
 <dd><p>The taildown covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>.</p></dd>
 
 
-<dt>euclid_type</dt>
+<dt id="arg-euclid-type">euclid_type<a class="anchor" aria-label="anchor" href="#arg-euclid-type"></a></dt>
 <dd><p>The euclidean covariance function type. Available options
 include <code>"spherical"</code>, <code>"exponential"</code>, <code>"gaussian"</code>,
 <code>"cosine"</code>, <code>"cubic"</code>, <code>"pentaspherical"</code>, <code>"wave"</code>,
@@ -112,31 +100,29 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>"none"</code>.</p></dd>
 
 
-<dt>rotate</dt>
+<dt id="arg-rotate">rotate<a class="anchor" aria-label="anchor" href="#arg-rotate"></a></dt>
 <dd><p>Anisotropy rotation parameter (from 0 to \(\pi\) radians) for
 the euclidean portion of the covariance. A value of 0 (the default) implies no rotation.</p></dd>
 
 
-<dt>scale</dt>
+<dt id="arg-scale">scale<a class="anchor" aria-label="anchor" href="#arg-scale"></a></dt>
 <dd><p>Anisotropy scale parameter (from 0 to 1) for
 the euclidean portion of the covariance. A value of 1 (the default) implies no scaling.</p></dd>
 
 
-<dt>nugget_type</dt>
+<dt id="arg-nugget-type">nugget_type<a class="anchor" aria-label="anchor" href="#arg-nugget-type"></a></dt>
 <dd><p>The nugget covariance function type. Available options
 include <code>"nugget"</code> or <code>"none"</code>.</p></dd>
 
 
-<dt>nugget</dt>
+<dt id="arg-nugget">nugget<a class="anchor" aria-label="anchor" href="#arg-nugget"></a></dt>
 <dd><p>The spatially independent (not correlated) random error variance. Commonly referred to as
 a nugget.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list with two elements: <code>initial</code> and <code>is_known</code>.
+    <p>A list with two elements: <code>initial</code> and <code>is_known</code>.
 <code>initial</code> is a named numeric vector indicating the spatial covariance parameters
 with specified initial and/or known values. <code>is_known</code> is a named
 numeric vector indicating whether the spatial covariance parameters in
@@ -212,11 +198,11 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. <em>Ecology</em> <b>91(3)</b>,
-644--651.</p>
+644–651.</p>
 <p>Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-<em>Journal of the American Statistical Association</em> <b>105</b>, 6--18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.</p>
+<em>Journal of the American Statistical Association</em> <b>105</b>, 6–18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.</p>
     </div>
 
     <div class="section level2">
@@ -268,23 +254,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
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@@ -1,60 +1,46 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fitting Linear Models for Spatial Stream Networks — ssn_lm • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fitting Linear Models for Spatial Stream Networks — ssn_lm"><meta name="description" content="This function works on spatial stream network objects to fit
 linear models with spatially autocorrelated errors using likelihood methods, allowing for
 non-spatial random effects, anisotropy, partition factors, big data methods, and more.
 The spatial formulation is described in Ver Hoef and Peterson (2010)
-and Peterson and Ver Hoef (2010)."><title>Fitting Linear Models for Spatial Stream Networks — ssn_lm • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fitting Linear Models for Spatial Stream Networks — ssn_lm"><meta property="og:description" content="This function works on spatial stream network objects to fit
+and Peterson and Ver Hoef (2010)."><meta property="og:description" content="This function works on spatial stream network objects to fit
 linear models with spatially autocorrelated errors using likelihood methods, allowing for
 non-spatial random effects, anisotropy, partition factors, big data methods, and more.
 The spatial formulation is described in Ver Hoef and Peterson (2010)
-and Peterson and Ver Hoef (2010)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fitting Linear Models for Spatial Stream Networks</h1>
+
+      <h1>Fitting Linear Models for Spatial Stream Networks</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_lm.R" class="external-link"><code>R/ssn_lm.R</code></a></small>
       <div class="d-none name"><code>ssn_lm.Rd</code></div>
     </div>
@@ -92,31 +78,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>formula</dt>
+
+
+<dl><dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>A two-sided linear formula describing the fixed effect structure
 of the model, with the response to the left of the <code>~</code> operator and
 the terms on the right, separated by <code>+</code> operators.</p></dd>
 
 
-<dt>ssn.object</dt>
+<dt id="arg-ssn-object">ssn.object<a class="anchor" aria-label="anchor" href="#arg-ssn-object"></a></dt>
 <dd><p>A spatial stream network object with class <code>SSN</code>.</p></dd>
 
 
-<dt>tailup_type</dt>
+<dt id="arg-tailup-type">tailup_type<a class="anchor" aria-label="anchor" href="#arg-tailup-type"></a></dt>
 <dd><p>The tailup covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>. Parameterizations are
 described in Details.</p></dd>
 
 
-<dt>taildown_type</dt>
+<dt id="arg-taildown-type">taildown_type<a class="anchor" aria-label="anchor" href="#arg-taildown-type"></a></dt>
 <dd><p>The taildown covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>. Parameterizations are
 described in Details.</p></dd>
 
 
-<dt>euclid_type</dt>
+<dt id="arg-euclid-type">euclid_type<a class="anchor" aria-label="anchor" href="#arg-euclid-type"></a></dt>
 <dd><p>The euclidean covariance function type. Available options
 include <code>"spherical"</code>, <code>"exponential"</code>, <code>"gaussian"</code>,
 <code>"cosine"</code>, <code>"cubic"</code>, <code>"pentaspherical"</code>, <code>"wave"</code>,
@@ -125,33 +113,33 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 described in Details.</p></dd>
 
 
-<dt>nugget_type</dt>
+<dt id="arg-nugget-type">nugget_type<a class="anchor" aria-label="anchor" href="#arg-nugget-type"></a></dt>
 <dd><p>The nugget covariance function type. Available options
 include <code>"nugget"</code> or <code>"none"</code>. Parameterizations are
 described in Details.</p></dd>
 
 
-<dt>tailup_initial</dt>
+<dt id="arg-tailup-initial">tailup_initial<a class="anchor" aria-label="anchor" href="#arg-tailup-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">tailup_initial()</a></code> specifying initial and/or
 known values for the tailup covariance parameters.</p></dd>
 
 
-<dt>taildown_initial</dt>
+<dt id="arg-taildown-initial">taildown_initial<a class="anchor" aria-label="anchor" href="#arg-taildown-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">taildown_initial()</a></code> specifying initial and/or
 known values for the taildown covariance parameters.</p></dd>
 
 
-<dt>euclid_initial</dt>
+<dt id="arg-euclid-initial">euclid_initial<a class="anchor" aria-label="anchor" href="#arg-euclid-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">euclid_initial()</a></code> specifying initial and/or
 known values for the euclidean covariance parameters.</p></dd>
 
 
-<dt>nugget_initial</dt>
+<dt id="arg-nugget-initial">nugget_initial<a class="anchor" aria-label="anchor" href="#arg-nugget-initial"></a></dt>
 <dd><p>An object from <code><a href="ssn_initial.html">nugget_initial()</a></code> specifying initial and/or
 known values for the nugget covariance parameters.</p></dd>
 
 
-<dt>additive</dt>
+<dt id="arg-additive">additive<a class="anchor" aria-label="anchor" href="#arg-additive"></a></dt>
 <dd><p>The name of the variable in <code>ssn.object</code> that is used
 to define spatial weights. Can be quoted or unquoted. For the tailup covariance functions, these additive
 weights are used for branching. Technical details that describe the role
@@ -159,13 +147,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 in Ver Hoef and Peterson (2010).</p></dd>
 
 
-<dt>estmethod</dt>
+<dt id="arg-estmethod">estmethod<a class="anchor" aria-label="anchor" href="#arg-estmethod"></a></dt>
 <dd><p>The estimation method. Available options include
 <code>"reml"</code> for restricted maximum likelihood and <code>"ml"</code> for maximum
 likelihood. The default is <code>"reml"</code>.</p></dd>
 
 
-<dt>anisotropy</dt>
+<dt id="arg-anisotropy">anisotropy<a class="anchor" aria-label="anchor" href="#arg-anisotropy"></a></dt>
 <dd><p>A logical indicating whether (geometric) anisotropy should
 be modeled. Not required if <code>spcov_initial</code> is provided with 1) <code>rotate</code>
 assumed unknown or assumed known and non-zero or 2) <code>scale</code> assumed unknown
@@ -173,7 +161,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 computational times can significantly increase. The default is <code>FALSE</code>.</p></dd>
 
 
-<dt>random</dt>
+<dt id="arg-random">random<a class="anchor" aria-label="anchor" href="#arg-random"></a></dt>
 <dd><p>A one-sided linear formula describing the random effect structure
 of the model. Terms are specified to the right of the <code>~ operator</code>.
 Each term has the structure <code>x1 + ... + xn | g1/.../gm</code>, where <code>x1 + ... + xn</code>
@@ -189,26 +177,24 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 parentheses can be omitted.</p></dd>
 
 
-<dt>randcov_initial</dt>
+<dt id="arg-randcov-initial">randcov_initial<a class="anchor" aria-label="anchor" href="#arg-randcov-initial"></a></dt>
 <dd><p>An optional object specifying initial and/or
 known values for the random effect variances. See <code><a href="https://usepa.github.io/spmodel/reference/randcov_initial.html" class="external-link">spmodel::randcov_initial()</a></code>.</p></dd>
 
 
-<dt>partition_factor</dt>
+<dt id="arg-partition-factor">partition_factor<a class="anchor" aria-label="anchor" href="#arg-partition-factor"></a></dt>
 <dd><p>A one-sided linear formula with a single term
 specifying the partition factor.  The partition factor assumes observations
 from different levels of the partition factor are uncorrelated.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments to <code><a href="https://rdrr.io/r/stats/optim.html" class="external-link">stats::optim()</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list with many elements that store information about
+    <p>A list with many elements that store information about
 the fitted model object and has class <code>ssn_lm</code>. Many generic functions that
 summarize model fit are available for <code>ssn_lm</code> objects, including
 <code>AIC</code>, <code>AICc</code>, <code>anova</code>, <code>augment</code>, <code>coef</code>,
@@ -217,8 +203,6 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <code>labels</code>, <code>logLik</code>, <code>loocv</code>, <code>model.frame</code>, <code>model.matrix</code>,
 <code>plot</code>, <code>predict</code>, <code>print</code>, <code>pseudoR2</code>, <code>summary</code>,
 <code>terms</code>, <code>tidy</code>, <code>update</code>, <code>varcomp</code>, and <code>vcov</code>.</p>
-
-
 <p>This fitted model list contains the following elements:</p><ul><li><p><code>additive</code>: The name of the additive function value column.</p></li>
 <li><p><code>anisotropy</code>: Whether euclidean anisotropy was modeled.</p></li>
 <li><p><code>call</code>: The function call.</p></li>
@@ -371,14 +355,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Garreta, V., Monestiez, P. and Ver Hoef, J.M. (2010) Spatial modelling and
 prediction on river networks: up model, down model, or hybrid?
-<em>Environmetrics</em> <b>21(5)</b>, 439--456.</p>
+<em>Environmetrics</em> <b>21(5)</b>, 439–456.</p>
 <p>Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. <em>Ecology</em> <b>91(3)</b>,
-644--651.</p>
+644–651.</p>
 <p>Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-<em>Journal of the American Statistical Association</em> <b>105</b>, 6--18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.</p>
+<em>Journal of the American Statistical Association</em> <b>105</b>, 6–18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.</p>
     </div>
 
     <div class="section level2">
@@ -404,43 +388,43 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Residuals:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     Min      1Q  Median      3Q     Max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.9487 -2.3775 -0.8840 -0.2776  0.9001 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.6986 -1.4031  0.2287  0.6796  2.2456 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients (fixed):</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>              Estimate Std. Error z value Pr(&gt;|z|)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 71.323162   6.193050  11.517   &lt;2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.028822   0.003075  -9.372   &lt;2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 80.857837   8.814114   9.174  &lt; 2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.034124   0.004376  -7.797 6.22e-15 ***</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Pseudo R-squared: 0.6688</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Pseudo R-squared: 0.5855</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients (covariance):</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               Effect     Parameter   Estimate</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential  de (parsill)  4.415e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential         range  1.175e+06</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               nugget        nugget  2.562e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential  de (parsill)  1.390e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential         range  1.306e+05</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               nugget        nugget  5.415e-02</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/ssn_names.html b/docs/reference/ssn_names.html
new file mode 100644
index 0000000..08c33a9
--- /dev/null
+++ b/docs/reference/ssn_names.html
@@ -0,0 +1,84 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Return names of data in an SSN object — ssn_names • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Return names of data in an SSN object — ssn_names"><meta name="description" content="Extract and print names from the edges, sites
+and preds elements of an SSN object."><meta property="og:description" content="Extract and print names from the edges, sites
+and preds elements of an SSN object."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SSN2</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Return names of data in an SSN object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_names.R" class="external-link"><code>R/ssn_names.R</code></a></small>
+      <div class="d-none name"><code>ssn_names.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Extract and print names from the <code>edges</code>, <code>sites</code>
+and <code>preds</code> elements of an SSN object.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ssn_names</span><span class="op">(</span><span class="va">ssn.object</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-ssn-object">ssn.object<a class="anchor" aria-label="anchor" href="#arg-ssn-object"></a></dt>
+<dd><p>An <code>SSN</code> object.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Print variable names to console</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/ssn_params.html b/docs/reference/ssn_params.html
index de7f01a..af40eda 100644
--- a/docs/reference/ssn_params.html
+++ b/docs/reference/ssn_params.html
@@ -1,56 +1,42 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Create a covariance parameter object for us with other functions.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create covariance parameter objects. — ssn_params • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create covariance parameter objects. — ssn_params"><meta name="description" content="Create a covariance parameter object for us with other functions.
 See spmodel::randcov_params() for documentation regarding
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+random effect covariance parameter objects."><meta property="og:description" content="Create a covariance parameter object for us with other functions.
 See spmodel::randcov_params() for documentation regarding
-random effect covariance parameter objects."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Create covariance parameter objects.</h1>
+
+      <h1>Create covariance parameter objects.</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/params.R" class="external-link"><code>R/params.R</code></a></small>
       <div class="d-none name"><code>ssn_params.Rd</code></div>
     </div>
@@ -74,28 +60,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tailup_type</dt>
+
+
+<dl><dt id="arg-tailup-type">tailup_type<a class="anchor" aria-label="anchor" href="#arg-tailup-type"></a></dt>
 <dd><p>The tailup covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>.</p></dd>
 
 
-<dt>de</dt>
+<dt id="arg-de">de<a class="anchor" aria-label="anchor" href="#arg-de"></a></dt>
 <dd><p>The spatially dependent (correlated) random error variance. Commonly referred to as
 a partial sill.</p></dd>
 
 
-<dt>range</dt>
+<dt id="arg-range">range<a class="anchor" aria-label="anchor" href="#arg-range"></a></dt>
 <dd><p>The correlation parameter.</p></dd>
 
 
-<dt>taildown_type</dt>
+<dt id="arg-taildown-type">taildown_type<a class="anchor" aria-label="anchor" href="#arg-taildown-type"></a></dt>
 <dd><p>The taildown covariance function type. Available options
 include <code>"linear"</code>, <code>"spherical"</code>, <code>"exponential"</code>,
 <code>"mariah"</code>, <code>"epa"</code>, and <code>"none"</code>.</p></dd>
 
 
-<dt>euclid_type</dt>
+<dt id="arg-euclid-type">euclid_type<a class="anchor" aria-label="anchor" href="#arg-euclid-type"></a></dt>
 <dd><p>The euclidean covariance function type. Available options
 include <code>"spherical"</code>, <code>"exponential"</code>, <code>"gaussian"</code>,
 <code>"cosine"</code>, <code>"cubic"</code>, <code>"pentaspherical"</code>, <code>"wave"</code>,
@@ -103,41 +91,39 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>"none"</code>.</p></dd>
 
 
-<dt>rotate</dt>
+<dt id="arg-rotate">rotate<a class="anchor" aria-label="anchor" href="#arg-rotate"></a></dt>
 <dd><p>Anisotropy rotation parameter (from 0 to \(\pi\) radians) for
 the euclidean portion of the covariance. A value of 0 (the default) implies no rotation.</p></dd>
 
 
-<dt>scale</dt>
+<dt id="arg-scale">scale<a class="anchor" aria-label="anchor" href="#arg-scale"></a></dt>
 <dd><p>Anisotropy scale parameter (from 0 to 1) for
 the euclidean portion of the covariance. A value of 1 (the default) implies no scaling.</p></dd>
 
 
-<dt>nugget_type</dt>
+<dt id="arg-nugget-type">nugget_type<a class="anchor" aria-label="anchor" href="#arg-nugget-type"></a></dt>
 <dd><p>The nugget covariance function type. Available options
 include <code>"nugget"</code> or <code>"none"</code>.</p></dd>
 
 
-<dt>nugget</dt>
+<dt id="arg-nugget">nugget<a class="anchor" aria-label="anchor" href="#arg-nugget"></a></dt>
 <dd><p>The spatially independent (not correlated) random error variance. Commonly referred to as
 a nugget.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A parameter object with class that matches the relevant <code>type</code> argument.</p>
+    <p>A parameter object with class that matches the relevant <code>type</code> argument.</p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. <em>Ecology</em> <b>91(3)</b>,
-644--651.</p>
+644–651.</p>
 <p>Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-<em>Journal of the American Statistical Association</em> <b>105</b>, 6--18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.</p>
+<em>Journal of the American Statistical Association</em> <b>105</b>, 6–18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.</p>
     </div>
 
     <div class="section level2">
@@ -164,23 +150,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "nugget_nugget"</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Put an sf data.frame in an SSN object</h1>
+
+      <h1>Put an sf data.frame in an SSN object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_put_data.R" class="external-link"><code>R/ssn_put_data.R</code></a></small>
       <div class="d-none name"><code>ssn_put_data.Rd</code></div>
     </div>
@@ -68,21 +54,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>sf data.frame with point geometry.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of class SSN, ssn_lm, or ssn_glm.</p></dd>
 
 
-<dt>name</dt>
+<dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>the internal name of the data set in the object
 <code>x</code>. For observed data, this will always be "obs", the
 default.</p></dd>
 
 
-<dt>resize_data</dt>
+<dt id="arg-resize-data">resize_data<a class="anchor" aria-label="anchor" href="#arg-resize-data"></a></dt>
 <dd><p>Logical. Indicates whether sf_df can have a
 different number of features than the current data.frame in the
 object. Default is FALSE.</p></dd>
@@ -90,9 +78,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns an object of the same class as x, which contains
+    <p>Returns an object of the same class as x, which contains
 the sf data.frame sf_data.</p>
     </div>
     <div class="section level2">
@@ -133,34 +119,34 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Simple feature collection with 6 features and 2 fields</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Geometry type: POINT</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Dimension:     XY</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Bounding box:  xmin: -1515032 ymin: 2529461 xmax: -1512690 ymax: 2531883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Summer_mn Value                 geometry</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     11.38    NA POINT (-1512690 2531883)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     10.72 10.72 POINT (-1512852 2531295)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     10.43 10.43 POINT (-1513400 2530706)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     10.13 10.13 POINT (-1514027 2530147)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     10.12 10.12 POINT (-1514309 2529902)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6      9.81  9.81 POINT (-1515032 2529461)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Bounding box:  xmin: -1528485 ymin: 927846.1 xmax: -1519790 ymax: 930808.7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   Summer_mn Value                  geometry</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1     14.92    NA POINT (-1528194 929550.4)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2     14.72 14.72 POINT (-1528222 928237.7)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3     14.61 14.61 POINT (-1528485 927846.1)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4     15.22 15.22 POINT (-1519790 930112.1)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5     14.49 14.49   POINT (-1520336 929772)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6     15.29 15.29 POINT (-1524599 930808.7)</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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-  
 
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index 7ec28ff..f0b4cc6 100644
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+++ b/docs/reference/ssn_simulate.html
@@ -1,58 +1,44 @@
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+resposne distribution also exist."><meta property="og:description" content="Simulate random variables on a stream
 network with a specific mean and covariance structure. Designed to use
 ssn_simulate(), but individual simulation functions for each
-resposne distribution also exist."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+resposne distribution also exist."></head><body>
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+
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Simulate random variables on a stream network</h1>
+
+      <h1>Simulate random variables on a stream network</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_simulate.R" class="external-link"><code>R/ssn_simulate.R</code></a>, <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_rbeta.R" class="external-link"><code>R/ssn_rbeta.R</code></a>, <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_rbinom.R" class="external-link"><code>R/ssn_rbinom.R</code></a>, and 5 more</small>
       <div class="d-none name"><code>ssn_simulate.Rd</code></div>
     </div>
@@ -197,41 +183,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>family</dt>
+
+
+<dl><dt id="arg-family">family<a class="anchor" aria-label="anchor" href="#arg-family"></a></dt>
 <dd><p>The response distribution family. The default is <code>"Gaussian"</code>.</p></dd>
 
 
-<dt>ssn.object</dt>
+<dt id="arg-ssn-object">ssn.object<a class="anchor" aria-label="anchor" href="#arg-ssn-object"></a></dt>
 <dd><p>A spatial stream network object with class <code>SSN</code>. Random
 variables are simulated for each row of <code>ssn.object$obs</code>.</p></dd>
 
 
-<dt>network</dt>
+<dt id="arg-network">network<a class="anchor" aria-label="anchor" href="#arg-network"></a></dt>
 <dd><p>The spatial stream network to simulate on. Currently only
 allowed to be <code>"obs"</code> for the <code>ssn.object$obs</code> object.</p></dd>
 
 
-<dt>tailup_params</dt>
+<dt id="arg-tailup-params">tailup_params<a class="anchor" aria-label="anchor" href="#arg-tailup-params"></a></dt>
 <dd><p>An object from <code><a href="ssn_params.html">tailup_params()</a></code> specifying
 the tailup covariance parameters.</p></dd>
 
 
-<dt>taildown_params</dt>
+<dt id="arg-taildown-params">taildown_params<a class="anchor" aria-label="anchor" href="#arg-taildown-params"></a></dt>
 <dd><p>An object from <code><a href="ssn_params.html">taildown_params()</a></code> specifying
 the taildown covariance parameters.</p></dd>
 
 
-<dt>euclid_params</dt>
+<dt id="arg-euclid-params">euclid_params<a class="anchor" aria-label="anchor" href="#arg-euclid-params"></a></dt>
 <dd><p>An object from <code><a href="ssn_params.html">euclid_params()</a></code> specifying
 the Euclidean covariance parameters.</p></dd>
 
 
-<dt>nugget_params</dt>
+<dt id="arg-nugget-params">nugget_params<a class="anchor" aria-label="anchor" href="#arg-nugget-params"></a></dt>
 <dd><p>An object from <code><a href="ssn_params.html">nugget_params()</a></code> specifying
 the nugget covariance parameters.</p></dd>
 
 
-<dt>additive</dt>
+<dt id="arg-additive">additive<a class="anchor" aria-label="anchor" href="#arg-additive"></a></dt>
 <dd><p>The name of the variable in <code>ssn.object</code> that is used
 to define spatial weights. Can be quoted or unquoted. For the tailup covariance functions, these additive
 weights are used for branching. Technical details that describe the role
@@ -239,46 +227,42 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 in Ver Hoef and Peterson (2010).</p></dd>
 
 
-<dt>mean</dt>
+<dt id="arg-mean">mean<a class="anchor" aria-label="anchor" href="#arg-mean"></a></dt>
 <dd><p>A numeric vector representing the mean. <code>mean</code> must have length 1
 (in which case it is recycled) or length equal
 to the number of rows in <code>data</code>. The default is <code>0</code>.</p></dd>
 
 
-<dt>samples</dt>
+<dt id="arg-samples">samples<a class="anchor" aria-label="anchor" href="#arg-samples"></a></dt>
 <dd><p>The number of independent samples to generate. The default
 is <code>1</code>.</p></dd>
 
 
-<dt>dispersion</dt>
+<dt id="arg-dispersion">dispersion<a class="anchor" aria-label="anchor" href="#arg-dispersion"></a></dt>
 <dd><p>The dispersion value (if relevant).</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>A numeric vector representing the sample size for each binomial trial.
 The default is <code>1</code>, which corresponds to a Bernoulli trial for each observation.</p></dd>
 
 
-<dt>randcov_params</dt>
+<dt id="arg-randcov-params">randcov_params<a class="anchor" aria-label="anchor" href="#arg-randcov-params"></a></dt>
 <dd><p>A <code><a href="https://usepa.github.io/spmodel/reference/randcov_params.html" class="external-link">spmodel::randcov_params()</a></code> object.</p></dd>
 
 
-<dt>partition_factor</dt>
+<dt id="arg-partition-factor">partition_factor<a class="anchor" aria-label="anchor" href="#arg-partition-factor"></a></dt>
 <dd><p>A formula indicating the partition factor.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>If <code>samples</code> is 1, a vector of random variables for each row of <code>ssn.object$obs</code></p>
-
-
-<p>is returned. If <code>samples</code> is greater than one, a matrix of random variables
+    <p>If <code>samples</code> is 1, a vector of random variables for each row of <code>ssn.object$obs</code>
+is returned. If <code>samples</code> is greater than one, a matrix of random variables
 is returned, where the rows correspond to each row of <code>ssn.object$obs</code> and the columns
 correspond to independent samples.</p>
     </div>
@@ -307,8 +291,8 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-<em>Journal of the American Statistical Association</em> <b>105</b>, 6--18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.</p>
+<em>Journal of the American Statistical Association</em> <b>105</b>, 6–18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.</p>
     </div>
 
     <div class="section level2">
@@ -325,34 +309,34 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">euclid</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_params.html">euclid_params</a></span><span class="op">(</span><span class="st">"spherical"</span>, de <span class="op">=</span> <span class="fl">0.2</span>, range <span class="op">=</span> <span class="fl">1000</span>, rotate <span class="op">=</span> <span class="fl">0</span>, scale <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">nugget</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_params.html">nugget_params</a></span><span class="op">(</span><span class="st">"nugget"</span>, nugget <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">ssn_simulate</span><span class="op">(</span><span class="st">"gaussian"</span>, <span class="va">mf04p</span>, <span class="st">"obs"</span>, <span class="va">tailup</span>, <span class="va">taildown</span>, <span class="va">euclid</span>, <span class="va">nugget</span>, additive <span class="op">=</span> <span class="st">"afvArea"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.23243313  0.45329177  0.18570140 -2.34240654 -1.64613982 -1.87065835</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.29619710  0.64904228 -0.03683179  0.94769933  0.60677198  1.18204714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [13]  0.68230622 -0.19460793 -1.16475226  0.82461078  0.34565867  0.91866220</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] -1.72120539 -0.68537520  0.09534920 -0.84413067  0.94423558  0.01030223</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [25]  0.88562756  1.92679655  0.70020757  0.17573536  0.07202305 -0.17097118</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [31] -0.73808153 -0.89988571  0.78511665 -0.03124410 -0.67371771  0.39741993</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [37]  0.14390780 -0.07988414  0.65623517  0.82860617 -0.13304709  0.01619557</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [43]  1.31099525 -0.31899800  1.08881410</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  1.45785676  0.76604939 -0.12910867 -0.35397583  0.19671514 -0.07588754</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.57765723 -0.81538653  1.75224086  0.72687527 -0.65778712 -1.73063057</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13] -0.11226686  0.46230814  1.30434670  0.39980221  0.34100800  1.58922294</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] -0.56814769 -1.01022346 -0.83452440 -0.41155550 -0.59774668 -0.31112518</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25]  0.09688149 -0.48593585  1.22120747  1.56687390 -0.15460358  0.89744186</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31] -0.43396443 -0.53227176 -0.12537756  0.97001333 -0.24502012  1.62890851</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37] -0.15335130 -0.78883608 -0.80805800 -1.18710291 -0.35885264  0.13201272</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43]  0.56219793  0.73955602 -0.88882700</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/docs/reference/ssn_split_predpts.html b/docs/reference/ssn_split_predpts.html
index 7c24cde..ef006cb 100644
--- a/docs/reference/ssn_split_predpts.html
+++ b/docs/reference/ssn_split_predpts.html
@@ -1,62 +1,48 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The splitPrediction function is used to
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Split a prediction dataset in an SSN object — ssn_split_predpts • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Split a prediction dataset in an SSN object — ssn_split_predpts"><meta name="description" content="The splitPrediction function is used to
 split prediction sets in an SSN object into smaller
 prediction sets. It returns a SSN object with additional
 prediction sets based on equal interval splits, a factor,
 integer, character or logical column stored within the prediction
-set, or a logical expression."><title>Split a prediction dataset in an SSN object — ssn_split_predpts • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Split a prediction dataset in an SSN object — ssn_split_predpts"><meta property="og:description" content="The splitPrediction function is used to
+set, or a logical expression."><meta property="og:description" content="The splitPrediction function is used to
 split prediction sets in an SSN object into smaller
 prediction sets. It returns a SSN object with additional
 prediction sets based on equal interval splits, a factor,
 integer, character or logical column stored within the prediction
-set, or a logical expression."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+set, or a logical expression."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Split a prediction dataset in an <code>SSN</code> object</h1>
+
+      <h1>Split a prediction dataset in an <code>SSN</code> object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_split_predpts.R" class="external-link"><code>R/ssn_split_predpts.R</code></a></small>
       <div class="d-none name"><code>ssn_split_predpts.Rd</code></div>
     </div>
@@ -87,77 +73,77 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ssn</dt>
+
+
+<dl><dt id="arg-ssn">ssn<a class="anchor" aria-label="anchor" href="#arg-ssn"></a></dt>
 <dd><p>An <code>SSN</code> object.</p></dd>
 
 
-<dt>predpts</dt>
+<dt id="arg-predpts">predpts<a class="anchor" aria-label="anchor" href="#arg-predpts"></a></dt>
 <dd><p>A character string representing the name of the
-prediction dataset</p></dd>
+prediction dataset.</p></dd>
 
 
-<dt>size_predpts</dt>
+<dt id="arg-size-predpts">size_predpts<a class="anchor" aria-label="anchor" href="#arg-size-predpts"></a></dt>
 <dd><p>numeric value representing the size of the new
 prediction sets. The existing prediction set is split equally to
 produce multiple prediction sets of this size</p></dd>
 
 
-<dt>by</dt>
+<dt id="arg-by">by<a class="anchor" aria-label="anchor" href="#arg-by"></a></dt>
 <dd><p>character string representing the column name of type
 factor, integer, character or logical that the split will be
 based on</p></dd>
 
 
-<dt>subset</dt>
+<dt id="arg-subset">subset<a class="anchor" aria-label="anchor" href="#arg-subset"></a></dt>
 <dd><p>logical expression indicating which elements or rows
 to keep; missing values are taken as <code>FALSE</code></p></dd>
 
 
-<dt>id_predpts</dt>
+<dt id="arg-id-predpts">id_predpts<a class="anchor" aria-label="anchor" href="#arg-id-predpts"></a></dt>
 <dd><p>character string representing the new prediction
 dataset name. This value is only specified when the subset method is
 used</p></dd>
 
 
-<dt>keep</dt>
+<dt id="arg-keep">keep<a class="anchor" aria-label="anchor" href="#arg-keep"></a></dt>
 <dd><p>logical value indicating whether the original
 prediction dataset should be retained in the <code>SSN</code>
 object. Default is <code>TRUE</code></p></dd>
 
 
-<dt>drop_levels</dt>
+<dt id="arg-drop-levels">drop_levels<a class="anchor" aria-label="anchor" href="#arg-drop-levels"></a></dt>
 <dd><p>logical value indicating whether empty factor
 levels should be dropped in the <code>by</code> column when the new
 prediction dataset(s) are created. Default is <code>FALSE</code></p></dd>
 
 
-<dt>overwrite</dt>
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
 <dd><p>logical indicating whether the new prediction
-dataset shapefile should be deleted in the .ssn directory if it
+dataset geopackage should be deleted in the .ssn directory if it
 already exists. Default = <code>FALSE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>returns the <code>SSN</code> specified in <code>ssn</code>, with one or more new prediction
-sets. Shapefiles of the new prediction sets are written to the
+    <p>returns the <code>SSN</code> specified in <code>ssn</code>, with one or more new prediction
+sets. Geopackages of the new prediction sets are written to the
 .ssn directory designated in ssn$path.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Three methods have been provided to split prediction sets:
-<code>size_predpts</code>, <code>by</code>, and <code>subset</code>. The
-<code>size_predpts</code> method is used to split the existing prediction
-set into multiple equally-sized prediction sets. Note that the
+size, by, and subset. The
+size method is used to split the existing prediction
+set into multiple equally-sized prediction sets using the <code>size_predpts</code> argument. Note that the
 final prediction set may be smaller in size than the others if
 the total number of predictions is not evenly divisible by
-<code>size_predpts</code>. The <code>by</code> method is used if the prediction
+<code>size_predpts</code>. The by method is used if the prediction
 set is to be split into multiple new prediction sets based on an
 existing column of type factor, integer, character, or
-logical. The <code>subset</code> method is used to create one new
-prediction set based on a logical expression.</p>
+logical specified using the argument <code>by</code>. The subset method is used to create one new
+prediction set based on a logical expression defined in <code>subset</code>.</p>
 <p>When more than one prediction dataset is created the prediction
 dataset names will be appended with a hyphen and prediction
 dataset number if more than one prediction dataset is
@@ -179,57 +165,56 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Import SSN object</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="copy_lsn_to_temp.html">copy_lsn_to_temp</a></span><span class="op">(</span><span class="op">)</span> <span class="co">## Only needed for this example</span></span></span>
 <span class="r-in"><span><span class="va">ssn</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km.shp"</span>, <span class="st">"Knapp"</span>, <span class="st">"CapeHorn"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km"</span>, <span class="st">"CapeHorn"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Split predictions into size_predpts 200</span></span></span>
+<span class="r-in"><span><span class="co">## Split predictions based on 'size' method</span></span></span>
 <span class="r-in"><span><span class="va">ssn1</span> <span class="op">&lt;-</span> <span class="fu">ssn_split_predpts</span><span class="op">(</span><span class="va">ssn</span>, <span class="st">"CapeHorn"</span>,</span></span>
 <span class="r-in"><span>  size_predpts <span class="op">=</span> <span class="fl">200</span>,</span></span>
 <span class="r-in"><span>  keep <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ssn1</span><span class="op">$</span><span class="va">preds</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"    "Knapp"      "CapeHorn-1" "CapeHorn-2" "CapeHorn-3"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [6] "CapeHorn-4"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"    "CapeHorn-1" "CapeHorn-2" "CapeHorn-3" "CapeHorn-4"</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">ssn1</span><span class="op">$</span><span class="va">preds</span><span class="op">[[</span><span class="st">"CapeHorn-1"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 200</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Split predictions using by method</span></span></span>
+<span class="r-in"><span><span class="co">## Split predictions using 'by' method</span></span></span>
 <span class="r-in"><span><span class="va">ssn</span><span class="op">$</span><span class="va">preds</span><span class="op">$</span><span class="va">pred1km</span><span class="op">$</span><span class="va">net.fac</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">ssn</span><span class="op">$</span><span class="va">preds</span><span class="op">$</span><span class="va">pred1km</span><span class="op">$</span><span class="va">netID</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">ssn2</span> <span class="op">&lt;-</span> <span class="fu">ssn_split_predpts</span><span class="op">(</span><span class="va">ssn</span>, <span class="st">"pred1km"</span>,</span></span>
 <span class="r-in"><span>  by <span class="op">=</span> <span class="st">"net.fac"</span>,</span></span>
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ssn2</span><span class="op">$</span><span class="va">preds</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"           "Knapp"             "CapeHorn"         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [4] "pred1km-net.fac-1" "pred1km-net.fac-2"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"           "CapeHorn"          "pred1km-net.fac-1"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [4] "pred1km-net.fac-2"</span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Split predictions using subset method</span></span></span>
+<span class="r-in"><span><span class="co">## Split predictions using 'subset' method</span></span></span>
 <span class="r-in"><span><span class="va">ssn3</span> <span class="op">&lt;-</span> <span class="fu">ssn_split_predpts</span><span class="op">(</span><span class="va">ssn</span>, <span class="st">"pred1km"</span>,</span></span>
 <span class="r-in"><span>  subset <span class="op">=</span> <span class="va">ratio</span> <span class="op">&gt;</span> <span class="fl">0.5</span>,</span></span>
 <span class="r-in"><span>  id_predpts <span class="op">=</span> <span class="st">"RATIO_05"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ssn3</span><span class="op">$</span><span class="va">preds</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"  "Knapp"    "CapeHorn" "RATIO_05"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "pred1km"  "CapeHorn" "RATIO_05"</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
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diff --git a/docs/reference/ssn_subset.html b/docs/reference/ssn_subset.html
index 867b600..116c962 100644
--- a/docs/reference/ssn_subset.html
+++ b/docs/reference/ssn_subset.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Returns an SSN object that has been subset based on a
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+logical expression."><meta property="og:description" content="Returns an SSN object that has been subset based on a
+logical expression."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Subset an <code>SSN</code> object</h1>
+
+      <h1>Subset an <code>SSN</code> object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_subset.R" class="external-link"><code>R/ssn_subset.R</code></a></small>
       <div class="d-none name"><code>ssn_subset.Rd</code></div>
     </div>
@@ -65,35 +51,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ssn</dt>
+
+
+<dl><dt id="arg-ssn">ssn<a class="anchor" aria-label="anchor" href="#arg-ssn"></a></dt>
 <dd><p>An <code>SSN</code> object.</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>The filepath to the .ssn folder, in string format,
 where the subset <code>SSN</code> will be saved.</p></dd>
 
 
-<dt>subset</dt>
+<dt id="arg-subset">subset<a class="anchor" aria-label="anchor" href="#arg-subset"></a></dt>
 <dd><p>A logical expression indicating which features to keep.</p></dd>
 
 
-<dt>clip</dt>
+<dt id="arg-clip">clip<a class="anchor" aria-label="anchor" href="#arg-clip"></a></dt>
 <dd><p>If <code>TRUE</code>, create a subset of the edges and
 prediction sites, based on the same logical expression used to
 subset the observed sites.  Default = <code>FALSE</code>.</p></dd>
 
 
-<dt>overwrite</dt>
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
 <dd><p>If <code>TRUE</code>, overwrite the folder specified in
 <code>path</code> if it exists. Default = FALSE.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>an object of class <code>SSN</code>, which is stored locally in the .ssn
+    <p>an object of class <code>SSN</code>, which is stored locally in the .ssn
 directory specified in <code>path</code>. It also creates and
 stores an SQLite database, binaryID.db, within the .ssn
 directory.</p>
@@ -127,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Import SSN object</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="copy_lsn_to_temp.html">copy_lsn_to_temp</a></span><span class="op">(</span><span class="op">)</span> <span class="co">## Only needed for this example</span></span></span>
 <span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km.shp"</span>, <span class="st">"Knapp"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  predpts <span class="op">=</span> <span class="st">"pred1km"</span>,</span></span>
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -146,23 +132,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/ssn_update_path.html b/docs/reference/ssn_update_path.html
index e35c1e9..348fb18 100644
--- a/docs/reference/ssn_update_path.html
+++ b/docs/reference/ssn_update_path.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Update the local path in an existing SSN object based
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-on an user defined file."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+on an user defined file."><meta property="og:description" content="Update the local path in an existing SSN object based
+on an user defined file."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Update path in an SSN object</h1>
+
+      <h1>Update path in an SSN object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_update_path.R" class="external-link"><code>R/ssn_update_path.R</code></a></small>
       <div class="d-none name"><code>ssn_update_path.Rd</code></div>
     </div>
@@ -65,25 +51,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An SSN, ssn_lm or ssn_glm object.</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>Filepath to the .ssn folder associated with the SSN
 object.</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>A logical that indicates if the new path should be printed
 to the console.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An SSN object with a new path list element.</p>
+    <p>An SSN object with a new path list element.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -106,23 +92,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu">ssn_update_path</span><span class="op">(</span><span class="va">mf04p</span>, <span class="va">newpath</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/ssn_write.html b/docs/reference/ssn_write.html
index b8bb1ad..f027a48 100644
--- a/docs/reference/ssn_write.html
+++ b/docs/reference/ssn_write.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function writes an SSN object to a local
-.ssn directory"><title>write an SSN object — ssn_write • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="write an SSN object — ssn_write"><meta property="og:description" content="This function writes an SSN object to a local
-.ssn directory"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>write an SSN object — ssn_write • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="write an SSN object — ssn_write"><meta name="description" content="This function writes an SSN object to a local
+.ssn directory"><meta property="og:description" content="This function writes an SSN object to a local
+.ssn directory"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
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+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">SSN2</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
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-  <a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/introduction.html">An Introduction to Spatial Stream Network Modeling in R Using SSN2</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/USEPA/SSN2/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>write an SSN object</h1>
+
+      <h1>write an SSN object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/ssn_write.R" class="external-link"><code>R/ssn_write.R</code></a></small>
       <div class="d-none name"><code>ssn_write.Rd</code></div>
     </div>
@@ -65,36 +51,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ssn</dt>
+
+
+<dl><dt id="arg-ssn">ssn<a class="anchor" aria-label="anchor" href="#arg-ssn"></a></dt>
 <dd><p>An <code>SSN</code> object.</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>filepath to the local .ssn directory to write to.</p></dd>
 
 
-<dt>overwrite</dt>
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
 <dd><p>If <code>TRUE</code>, overwrite existing files in <code>file</code>
 (if it exists). Defaults to <code>FALSE</code>.</p></dd>
 
 
-<dt>copy_dist</dt>
+<dt id="arg-copy-dist">copy_dist<a class="anchor" aria-label="anchor" href="#arg-copy-dist"></a></dt>
 <dd><p>If <code>TRUE</code>, copy distance matrices to <code>file</code>
 (if they exist). Defaults to <code>FALSE</code>.</p></dd>
 
 
-<dt>import</dt>
+<dt id="arg-import">import<a class="anchor" aria-label="anchor" href="#arg-import"></a></dt>
 <dd><p>If <code>TRUE</code>, import and return the <code>SSN</code> object
 after writing to file. Defaults to <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ssn_write creates an .ssn directory that contains the
+    <p>ssn_write creates an .ssn directory that contains the
 spatial, topological, and attribute information stored in the
-original <code>SSN</code> object. When <code>import = TRUE</code>, the
+original <code>SSN</code> object. Spatial datasets found in the
+<code>SSN</code> object (e.g. edges, obs, and prediction sites) are
+saved in GeoPackage format. When <code>import = TRUE</code>, the
 <code>SSN</code> object is imported and returned.</p>
     </div>
 
@@ -105,38 +93,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="copy_lsn_to_temp.html">copy_lsn_to_temp</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co">## Import SSN object with prediction sites</span></span></span>
 <span class="r-in"><span><span class="va">mf04p</span> <span class="op">&lt;-</span> <span class="fu"><a href="ssn_import.html">ssn_import</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/MiddleFork04.ssn"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  predpts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pred1km.shp"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  predpts <span class="op">=</span> <span class="st">"pred1km"</span>,</span></span>
 <span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Write SSN to new .ssn directory</span></span></span>
-<span class="r-in"><span><span class="fu">ssn_write</span><span class="op">(</span><span class="va">mf04p</span>, path <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/tempSSN.ssn"</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">ssn_write</span><span class="op">(</span><span class="va">mf04p</span>,</span></span>
+<span class="r-in"><span>  path <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"/tempSSN.ssn"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  overwrite <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Write SSN to .ssn directory and return SSN object</span></span></span>
 <span class="r-in"><span><span class="va">tempSSN</span> <span class="op">&lt;-</span> <span class="fu">ssn_write</span><span class="op">(</span><span class="va">mf04p</span>, path <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempdir</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  <span class="st">"/tempSSN.ssn"</span></span></span>
 <span class="r-in"><span><span class="op">)</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>, import <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> binaryID.db already exists - no changes were made to binaryID.db table</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/summary.SSN.html b/docs/reference/summary.SSN.html
index e7dc1f3..ada97e9 100644
--- a/docs/reference/summary.SSN.html
+++ b/docs/reference/summary.SSN.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Summarize an SSN object</h1>
+
+      <h1>Summarize an SSN object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/summary.SSN.R" class="external-link"><code>R/summary.SSN.R</code></a></small>
       <div class="d-none name"><code>summary.SSN.Rd</code></div>
     </div>
@@ -57,25 +43,25 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for SSN</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'SSN'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An SSN object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list with several fitted model quantities used to create
+    <p>A list with several fitted model quantities used to create
 informative summaries when printing.</p>
     </div>
     <div class="section level2">
@@ -87,23 +73,23 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 sf data.frames.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/summary.SSN2.html b/docs/reference/summary.SSN2.html
index 502bc32..5d958e1 100644
--- a/docs/reference/summary.SSN2.html
+++ b/docs/reference/summary.SSN2.html
@@ -1,52 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Summarize a fitted model object."><title>Summarize a fitted model object — summary.SSN2 • SSN2</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summarize a fitted model object — summary.SSN2"><meta property="og:description" content="Summarize a fitted model object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Summarize a fitted model object</h1>
+
+      <h1>Summarize a fitted model object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/summary.R" class="external-link"><code>R/summary.R</code></a></small>
       <div class="d-none name"><code>summary.SSN2.Rd</code></div>
     </div>
@@ -57,28 +43,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list with several fitted model quantities used to create
+    <p>A list with several fitted model quantities used to create
 informative summaries when printing.</p>
     </div>
     <div class="section level2">
@@ -117,42 +103,42 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Residuals:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     Min      1Q  Median      3Q     Max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.9487 -2.3775 -0.8840 -0.2776  0.9001 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.6986 -1.4031  0.2287  0.6796  2.2456 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients (fixed):</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>              Estimate Std. Error z value Pr(&gt;|z|)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 71.323162   6.193050  11.517   &lt;2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.028822   0.003075  -9.372   &lt;2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 80.857837   8.814114   9.174  &lt; 2e-16 ***</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.034124   0.004376  -7.797 6.22e-15 ***</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Pseudo R-squared: 0.6688</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Pseudo R-squared: 0.5855</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients (covariance):</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               Effect     Parameter   Estimate</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential  de (parsill)  4.415e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential         range  1.175e+06</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               nugget        nugget  2.562e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential  de (parsill)  1.390e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   tailup exponential         range  1.306e+05</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               nugget        nugget  5.415e-02</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -1,52 +1,38 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Tidy a fitted model object</h1>
+
+      <h1>Tidy a fitted model object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/tidy.R" class="external-link"><code>R/tidy.R</code></a></small>
       <div class="d-none name"><code>tidy.SSN2.Rd</code></div>
     </div>
@@ -57,31 +43,33 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy</a></span><span class="op">(</span><span class="va">x</span>, conf.int <span class="op">=</span> <span class="cn">FALSE</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, effects <span class="op">=</span> <span class="st">"fixed"</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy</a></span><span class="op">(</span><span class="va">x</span>, conf.int <span class="op">=</span> <span class="cn">FALSE</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, effects <span class="op">=</span> <span class="st">"fixed"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>conf.int</dt>
+<dt id="arg-conf-int">conf.int<a class="anchor" aria-label="anchor" href="#arg-conf-int"></a></dt>
 <dd><p>Logical indicating whether or not to include a confidence interval
 in the tidied output. The default is <code>FALSE</code>.</p></dd>
 
 
-<dt>conf.level</dt>
+<dt id="arg-conf-level">conf.level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>The confidence level to use for the confidence interval if
 <code>conf.int</code> is <code>TRUE</code>. Must be strictly greater than 0 and less than 1.
 The default is 0.95, which corresponds to a 95 percent confidence interval.</p></dd>
 
 
-<dt>effects</dt>
+<dt id="arg-effects">effects<a class="anchor" aria-label="anchor" href="#arg-effects"></a></dt>
 <dd><p>The type of effects to tidy. Available options are <code>"fixed"</code>
 (fixed effects), <code>"tailup"</code> (tailup covariance parameters),
 <code>"taildown"</code> (taildown covariance parameters), <code>"euclid"</code> (Euclidean
@@ -91,15 +79,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>"dispersion"</code>, and <code>"randcov"</code> (random effect variances). The default is <code>"fixed"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A tidy tibble of summary information <code>effects</code>.</p>
+    <p>A tidy tibble of summary information <code>effects</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -123,29 +109,29 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">tidy</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 2 × 5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   term        estimate std.error statistic p.value</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> (Intercept)  71.3      6.19        11.5        0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ELEV_DEM     -<span style="color: #BB0000;">0.028</span><span style="color: #BB0000; text-decoration: underline;">8</span>   0.003<span style="text-decoration: underline;">08</span>     -<span style="color: #BB0000;">9.37</span>       0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   term        estimate std.error statistic  p.value</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> (Intercept)  80.9      8.81         9.17 0   <span style="color: #949494;"> </span>   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ELEV_DEM     -<span style="color: #BB0000;">0.034</span><span style="color: #BB0000; text-decoration: underline;">1</span>   0.004<span style="text-decoration: underline;">38</span>     -<span style="color: #BB0000;">7.80</span> 6.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-15</span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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index e1fb070..826bc13 100644
--- a/docs/reference/varcomp.SSN2.html
+++ b/docs/reference/varcomp.SSN2.html
@@ -1,54 +1,40 @@
 <!DOCTYPE html>
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+and each variance parameter."><meta property="og:description" content="Compare the proportion of total variability explained by the fixed effects
+and each variance parameter."></head><body>
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Variability component comparison</h1>
+
+      <h1>Variability component comparison</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/varcomp.R" class="external-link"><code>R/varcomp.R</code></a></small>
       <div class="d-none name"><code>varcomp.SSN2.Rd</code></div>
     </div>
@@ -60,28 +46,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/varcomp.html" class="external-link">varcomp</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://usepa.github.io/spmodel/reference/varcomp.html" class="external-link">varcomp</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A tibble that partitions the the total variability by the fixed effects
+    <p>A tibble that partitions the the total variability by the fixed effects
 and each variance parameter. The proportion of variability explained by the
 fixed effects is the pseudo R-squared obtained by <code>psuedoR2()</code>. The
 remaining proportion is spread accordingly among each variance parameter:
@@ -110,30 +96,30 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 5 × 2</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   varcomp            proportion</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Covariates (PR-sq)    0.669  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> tailup_de             0.329  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> taildown_de           0      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> euclid_de             0      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> nugget                0.001<span style="text-decoration: underline;">91</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Covariates (PR-sq)     0.585 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> tailup_de              0.399 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> taildown_de            0     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> euclid_de              0     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> nugget                 0.015<span style="text-decoration: underline;">5</span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
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diff --git a/docs/reference/vcov.SSN2.html b/docs/reference/vcov.SSN2.html
index a323f97..663f26e 100644
--- a/docs/reference/vcov.SSN2.html
+++ b/docs/reference/vcov.SSN2.html
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-      <img src="" class="logo" alt=""><h1>Calculate variance-covariance matrix for a fitted model object</h1>
+
+      <h1>Calculate variance-covariance matrix for a fitted model object</h1>
       <small class="dont-index">Source: <a href="https://github.com/USEPA/SSN2/blob/HEAD/R/vcov.R" class="external-link"><code>R/vcov.R</code></a></small>
       <div class="d-none name"><code>vcov.SSN2.Rd</code></div>
     </div>
@@ -57,24 +43,26 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for ssn_lm</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'ssn_lm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/vcov.html" class="external-link">vcov</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for ssn_glm</span></span>
+<span><span class="co"># S3 method for class 'ssn_glm'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/vcov.html" class="external-link">vcov</a></span><span class="op">(</span><span class="va">object</span>, var_correct <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>object</dt>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object from <code><a href="ssn_lm.html">ssn_lm()</a></code> or <code><a href="ssn_glm.html">ssn_glm()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Other arguments. Not used (needed for generic consistency).</p></dd>
 
 
-<dt>var_correct</dt>
+<dt id="arg-var-correct">var_correct<a class="anchor" aria-label="anchor" href="#arg-var-correct"></a></dt>
 <dd><p>A logical indicating whether to return the corrected variance-covariance
 matrix for models fit using <code><a href="ssn_glm.html">ssn_glm()</a></code> (when <code>family</code> is different
 from <code>"Gaussian"</code>). The default is <code>TRUE</code>.</p></dd>
@@ -82,9 +70,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The variance-covariance matrix of coefficients obtained via <code><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coef()</a></code>.
+    <p>The variance-covariance matrix of coefficients obtained via <code><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coef()</a></code>.
 Currently, only the variance-covariance matrix of the fixed effects is supported.</p>
     </div>
 
@@ -105,27 +91,27 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/vcov.html" class="external-link">vcov</a></span><span class="op">(</span><span class="va">ssn_mod</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>             (Intercept)      ELEV_DEM</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 38.35386871 -1.902743e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.01902743  9.458396e-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept) 77.68860270 -3.855169e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ELEV_DEM    -0.03855169  1.915303e-05</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
+  <p>Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.</p>
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 <div class="pkgdown-footer-right">
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diff --git a/docs/search.json b/docs/search.json
index c6299cc..a7bcd7f 100644
--- a/docs/search.json
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@@ -1 +1 @@
-[{"path":"/articles/introduction.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"Data streams frequently exhibit unique patterns spatial autocorrelation resulting branching network structure, longitudinal (.e., upstream/downstream) connectivity, directional water flow, differences flow volume throughout network (Erin E. Peterson et al. 2013). addition, stream networks embedded within spatial environment, can also influence observations stream network. Traditional spatial statistical models, based solely Eucliean distance, often fail adequately describe unique complex spatial dependencies. Spatial stream network models based moving-average construction (J. M. Ver Hoef Peterson 2010) specifically designed describe two unique spatial relationships found streams data. pair sites considered flow-connected water flows upstream site downstream site. Sites flow-unconnected reside stream network (.e., share common junction downstream) share flow. Spatial stream network models typically rely two families covariance functions represent relationships: tail-tail-models. tail-model, moving-average function points upstream direction. Covariance function stream distance weighting structure used proportionally allocate, split, function upstream junctions account differences flow volume influential variables (Erin E. Peterson Ver Hoef 2010). result, non-zero covariances restricted flow-connected sites tail-model. tail-model, moving average function points downstream direction. contrast tail-model, tail-models describe flow-connected flow-unconnected relationships, although covariances always equal stronger flow-unconnected sites flow-connected sites separated equal stream distances (J. M. Ver Hoef Peterson 2010). tail-model, covariance function stream distance weights required. However, also possible often preferable build spatial stream network models based mixture four components: tail-component, tail-component, Euclidean component, nugget component. Euclidean component useful captures covariance influential processes independent stream network intermediate broad scales (e.g., air temperature, soil type, geology). nugget component captures covariance processes highly localized, thus independent across sites. details regarding construction spatial stream network models covariance components, see Cressie et al. (2006), J. M. Ver Hoef, Peterson, Theobald (2006), J. M. Ver Hoef Peterson (2010), Erin E. Peterson Ver Hoef (2010), Isaak et al. (2014). SSN2 R package used fit summarize spatial stream network models make predictions unobserved locations (Kriging). SSN2 updated version SSN R package (J. Ver Hoef et al. 2014). create SSN2 replace SSN? two main reasons: SSN depends rgdal (Bivand, Keitt, Rowlingson 2021), rgeos (Bivand Rundel 2020), maptools (Bivand Lewin-Koh 2021) R packages, retired October, 2023. functionality replaced modernized sf package (Pebesma 2018). SSN2 depends sf instead rgdal, rgeos, maptools, reflecting broader change regarding handling spatial data R. See Nowosad (2023) information regarding retirement rgdal, rgeos, maptools, available https://geocompx.org//post/2023/rgdal-retirement . features added SSN2 difficult implement SSN without massive restructuring SSN’s foundation, created new package. example, SSN objects SSN2 S3 objects SSN objects SSN S4 objects. Additionally, many functions rewritten /repurposed SSN2 use generic functions (e.g., block prediction SSN2 performed using predict() SSN performed using BlockPredict()). vignette provides overview basic features SSN2. load SSN2 running use SSN2 formal publication report, please cite . Citing SSN2 lets us devote resources future. view SSN2 citation running","code":"library(SSN2) citation(package = \"SSN2\") #>  #> To cite SSN2 in publications use: #>  #>   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: #>   Spatial Modeling on Stream Networks in R. R package version 0.1.0 #>  #> A BibTeX entry for LaTeX users is #>  #>   @Manual{, #>     title = {{SSN2}: Spatial Modeling on Stream Networks in {R}}, #>     author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak}, #>     year = {2023}, #>     note = {{R} package version 0.1.0}, #>   }"},{"path":"/articles/introduction.html","id":"input-data","dir":"Articles","previous_headings":"","what":"Input Data","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"Spatial input data must pre-processed can used fit spatial stream network models SSN2 package. information generated using Spatial Tools Analysis River Systems (STARS) toolset ArcGIS Desktop versions 9.3x-10.8x (E. Peterson Ver Hoef 2014). Note STARS designed work existing streams data vector format. openSTARS R package (Kattwinkel Szöcs 2022) provides open source alternative STARS toolset, depends GRASS GIS. contrast STARS, openSTARS designed derive streams raster format digital elevation model (DEM). tools output non-proprietary .ssn folder (.e., directory) contains spatial, topological attribute information needed fit spatial stream network model using SSN2. includes: edges: shapefile lines representing linear geometry stream network(s). sites: shapefile site locations observed data collected stream network. prediction sites: one shapefiles locations predictions made. Optional. netIDx.dat stream network: text file containing topological relationships line segments edges, network.","code":""},{"path":"/articles/introduction.html","id":"ssn-objects-in-ssn2","dir":"Articles","previous_headings":"","what":"SSN Objects in SSN2","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"data contained .ssn object read R stored SSN object, special list structure four elements: edges: sf object contains edges LINESTRING geometry. obs: sf object contains observed data POINT geometry. preds list sf objects POINT geometry, containing set locations predictions made. path: character string represents path relevant .ssn directory stored computer. netgeom (short “network geometry”) column also added sf objects stored within SSN object. netgeom column contains character string describing position line (edges) point (obs preds) feature relation one another. format netgeom column differs depending whether describing feature LINESTRING POINT geometry. edges, format netgeom \"ENETWORK (netID rid upDist)\", sites \"SNETWORK (netID rid upDist ratio pid locID)\", data used define netgeom column found edges, observed sites, prediction sites shapefiles, created using STARS openSTARS software. edges, includes unique network identifier (netID) reach (.e., edge) identifier (rid), well distance downstream location stream network (.e., stream outlet) upstream node edge segment, movement restricted stream network (upDist). netgeom column sites also contains netID rid edge site resides. point identifier (pid) unique measurement, location identifier (locID) unique spatial location. Note locID may multiple pids associated repeated measurements observed data multiple predictions made location. upDist value site represents stream distance stream outlet site location. Finally, ratio used describe relative position site associated edge segment. proportional distance downstream node edge segment site location. example, ratio site close zero site close downstream node edge segment, ratio site close one site far downstream node edge segment. Together key pieces data used describe network edge site resides , well exactly site line segment. may first seem redundant combine store multiple numeric columns text netgeom column. However, data dictate observed prediction sites relate one another topological space, impacts parameter estimates predicted values generated fitted models. Storing data text netgeom column significantly reduces chance values accidentally (unknowingly) altered user.","code":""},{"path":"/articles/introduction.html","id":"the-middle-fork-data","dir":"Articles","previous_headings":"SSN Objects in SSN2","what":"The Middle Fork Data","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"vignette, use Middle Fork 2004 stream temperature data SSN2. raw input data stored lsndata/MiddleFork04.ssn directory installed alongside SSN2. may store file path example data: Several functions SSN2 reading writing data (use shortly) directly manipulate .ssn folder. desirable directly manipulate MiddleFork04.ssn data installed alongside SSN2, MiddleFork04.ssn may copied temporary directory relevant path alternative location can stored: specifying path (using system.file() copy_lsn_to_temp()), import stream reaches, observed sites, prediction sites: summarise mf04p data running see mf04p contains 45 observation sites total 2102 prediction sites stored three different prediction datasets. explore several variables throughout rest vignette: AREAWTMAP: Precipitation (area-weighted mm) ELEV_DEM: Elevation (based 30m DEM) Summer_mn: Summer mean stream temperature (Celsius) C16: Number times daily stream temperature exceeded 16 Celsius (summer) detailed description variables mf04p available documentation can seen running ?MiddleFork04.ssn help(MiddleFork04.ssn, package = \"SSN2\"). SSN2 currently generic plotting function SSN objects. Instead, rely plotting functionality ggplot2 (Wickham 2016) sf (Pebesma 2018). vignette focuses use ggplot2, load running ggplot2 installed alongside SSN2 dependencies = TRUE install.packages(), check installed reproducing visualizations vignette. Prediction sites can easily accessed SSN object using list element number names attribute. example, print names prediction datasets console view Middle Fork stream network, overlay observed sites data collected using brown circles, overlay pred1km prediction locations using smaller, blue triangles running  Later fit models stream network data. , however, supplement .ssn object hydrologic distance matrices preserve directionality, required statistical modeling: Stream distance matrices saved local files .ssn directory associated SSN object, mf04p$path, folder called distance created ssn_create_distmat(). matrices stored .Rdata files separate sub-folders observed sites (obs) set prediction sites. file path .ssn directory incorrect, ssn_update_path() can used update distance matrices generated.","code":"path <- system.file(\"lsndata/MiddleFork04.ssn\", package = \"SSN2\") copy_lsn_to_temp() path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(   path = path,   predpts = c(\"pred1km\", \"CapeHorn\", \"Knapp\"),   overwrite = TRUE ) summary(mf04p) #> Object of class SSN #>  #> Object includes observations on 26 variables across 45 sites within the bounding box #>     xmin     ymin     xmax     ymax  #> -1531385  2521181 -1498448  2540274  #>  #> Object also includes 3 sets of prediction points with a total of 2102 locations #>  #> Variable names are (found using names(object)): #> $obs #>  [1] \"STREAMNAME\" \"COMID\"      \"CDRAINAG\"   \"AREAWTMAP\"  \"SLOPE\"      #>  [6] \"ELEV_DEM\"   \"Source\"     \"Summer_mn\"  \"MaxOver20\"  \"C16\"        #> [11] \"C20\"        \"C24\"        \"FlowCMS\"    \"AirMEANc\"   \"AirMWMTc\"   #> [16] \"NEAR_X\"     \"NEAR_Y\"     \"rid\"        \"ratio\"      \"afvArea\"    #> [21] \"upDist\"     \"locID\"      \"netID\"      \"pid\"        \"geometry\"   #> [26] \"netgeom\"    #>  #> $pred1km #>  [1] \"COMID\"     \"GNIS_NAME\" \"CDRAINAG\"  \"AREAWTMAP\" \"SLOPE\"     \"ELEV_DEM\"  #>  [7] \"FlowCMS\"   \"AirMEANc\"  \"AirMWMTc\"  \"NEAR_X\"    \"NEAR_Y\"    \"rid\"       #> [13] \"ratio\"     \"afvArea\"   \"upDist\"    \"locID\"     \"netID\"     \"pid\"       #> [19] \"geometry\"  \"netgeom\"   #>  #> $CapeHorn #>  [1] \"COMID\"     \"GNIS_NAME\" \"CDRAINAG\"  \"AREAWTMAP\" \"SLOPE\"     \"ELEV_DEM\"  #>  [7] \"FlowCMS\"   \"AirMEANc\"  \"AirMWMTc\"  \"NEAR_X\"    \"NEAR_Y\"    \"rid\"       #> [13] \"ratio\"     \"afvArea\"   \"upDist\"    \"locID\"     \"netID\"     \"pid\"       #> [19] \"geometry\"  \"netgeom\"   #>  #> $Knapp #>  [1] \"COMID\"     \"GNIS_NAME\" \"CDRAINAG\"  \"AREAWTMAP\" \"SLOPE\"     \"ELEV_DEM\"  #>  [7] \"FlowCMS\"   \"AirMEANc\"  \"AirMWMTc\"  \"NEAR_X\"    \"NEAR_Y\"    \"rid\"       #> [13] \"ratio\"     \"afvArea\"   \"upDist\"    \"locID\"     \"netID\"     \"pid\"       #> [19] \"geometry\"  \"netgeom\" library(ggplot2) names(mf04p$preds) #> [1] \"pred1km\"  \"CapeHorn\" \"Knapp\" ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$preds$pred1km, pch = 17, color = \"blue\") +   geom_sf(data = mf04p$obs, color = \"brown\", size = 2) +   theme_bw() ssn_create_distmat(   ssn.object = mf04p,   predpts = c(\"pred1km\", \"CapeHorn\", \"Knapp\"),   among_predpts = TRUE,   overwrite = TRUE )"},{"path":[]},{"path":"/articles/introduction.html","id":"sec:lm","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models","what":"Linear SSN Models","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"begin fitting linear models stream network data using ssn_lm() function. Later fit generalized linear models stream network data using ssn_glm() function. Typically, linear models used response variable (.e., dependent variable) continuous highly skewed, generalized linear models often used response variable binary, count, highly skewed. Linear spatial stream network models quantitative response vector \\(\\mathbf{y}\\) spatially dependent random errors often parameterized \\(\\mathbf{X}\\) matrix explanatory variables (usually including column 1’s intercept), \\(\\boldsymbol{\\beta}\\) vector fixed effects describe average impact \\(\\mathbf{X}\\) \\(\\mathbf{y}\\), \\(\\boldsymbol{\\tau}_{tu}\\) vector spatially dependent (correlated) tail-random errors, \\(\\boldsymbol{\\tau}_{td}\\) vector spatially dependent (correlated) tail-random errors, \\(\\boldsymbol{\\tau}_{eu}\\) vector spatially dependent (correlated) Euclidean random errors, \\(\\boldsymbol{\\epsilon}\\) vector spatially independent (uncorrelated) random errors. spatial dependence \\(\\boldsymbol{\\tau}\\) term explicitly specified using spatial covariance function incorporates variance respective \\(\\boldsymbol{\\tau}\\) term, often called partial sill, range parameter controls behavior respective spatial covariance. variance \\(\\boldsymbol{\\epsilon}\\) often called nugget (nugget effect). Suppose interested studying summer mean temperature (Summer_mn) stream network. can visualize distribution summer mean temperature (overlain onto stream network) running  ssn_lm() function used fit linear spatial stream network models bears many similarities base-R’s lm() function non-spatial linear models. provide commonly used arguments ssn_lm(): formula uses syntax formula argument lm(). ssn.object: .ssn object. tailup_type: tail-covariance, can \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\" (default) taildown_type: tail-covariance, can \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\" (default) euclid_type: Euclidean covariance, can \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\" (default) nugget_type: \"nugget\" (default) \"none\". important note default tailup_type, taildown_type, euclid_type \"none\", means must specified relevant covariances desired. default nugget_type \"nugget\", specifies nugget effect, useful many ecological processes localized variability important capture. Full parameterizations covariance function given ssn_lm()’s documentation, can viewed running help(\"ssn_lm\", \"SSN2\"). different approaches choosing covariance functions. One approach fit several models compare fits using statistics like AIC cross-validation error. Another approach visualize Torgegram() choose functions appropriately. Torgegram() SSN2 essentially semivariogram describes variability streams data based flow-connected, flow-unconnected, Euclidean spatial relationships. Like semivariograms, Torgegram describes semivariance (.e. halved average squared difference) observations changes hydrologic Euclidean distances. strong dependence sites based flow-connected flow-unconnected relationships, semivariance increase respective distance. , however, strong dependence, semivariance relatively flat. Torgegram() output can combined plot() better understand covariance components may suitable model. example, semivariance flow-connected sites increases hydrologic distance semivariance flow-unconnected sites flat, tail-component may sufficient model (.e., tail-component needed). However, model likely benefit tail-component combination tail-tail-models semivariance flow-connected flow-unconnected sites increases distance. Alternatively, semivariance flat, model unlikely benefit tail-tail-components. SSN2 also allows users visualize changes semivariance based Euclidean distance, may provide additional insights whether Euclidean component mixture tail-, tail-/Euclidean models improve model. Please see Zimmerman Ver Hoef (2017) -depth review Toregegrams, along strategies interpreting using inform model fitting. formal comparison models, use statistics like AIC cross-validation error, discuss later. Suppose want model summer mean stream temperature function elevation precipitation. can aid understanding covariance components may informative visualizing Torgegram: first argument Torgegram() formula. Residuals non-spatial linear model specified formula used Toregram visualize remaining spatial dependence. type argument specifies Torgegram types default value c(\"flowcon\", \"flowuncon\") flow-connected flow-unconnected semivariances. also desire visualize Euclidean semivariance. visualize three components running  flow-connected semivariances seem generally increase distance, suggests model benefit least tail-component. takeaway flow-unconnected Euclidean semivariances less clear – seem generally increase distance low distances high semivariances. suggests tail-Euclidean components may impactful model fit. investigate next fit model three components: tail-, tail-, Euclidean. fit spatial stream network model regressing summer mean stream temperature elevation watershed-averaged precipitation using exponential tail-covariance function additive weights created using watershed area (afvArea), spherical tail-covariance function, Gaussian Euclidean covariance function, nugget effect running estimation method specified via estmethod argument, default value \"reml\" restricted maximum likelihood (REML). estimation method \"ml\" maximum likelihood (ML). REML chosen default tends yield accurate covariance parameter estimates ML, especially small sample sizes. One nuance REML, however, comparisons likelihood-based statistics like AIC valid models fixed effects structure (.e., formula). compare fixed effects covariance structures simultaneously, use ML model comparison tool likelihood-based, cross validation via loocv(), discuss later.","code":"ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = Summer_mn), size = 2) +   scale_color_viridis_c(limits = c(-1.5, 17), option = \"H\") +   theme_bw() tg <- Torgegram(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   type = c(\"flowcon\", \"flowuncon\", \"euclid\") ) plot(tg) ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\" )"},{"path":"/articles/introduction.html","id":"model-summaries","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models > Linear SSN Models","what":"Model Summaries","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"summarize fitted model running Similar summaries lm() objects, summaries ssn_lm() objects include original function call, residuals, coefficients table fixed effects. (Intercept) represents average summer mean stream temperature sea level (elevation zero) precipitation, ELEV_DEM represents decrease average summer mean stream temperature one unit (meter) increase elevation, AREAWTMAP represents decrease average summer mean stream temperature one unit (mm) increase precipitation. strong evidence average summer mean stream temperature decreases elevation (\\(p\\)-value \\(< 0.001\\)), moderate evidence average summer mean stream temperature decreases precipitation (\\(p\\)-value \\(\\approx\\) 0.05). pseudo r-squared also returned, quantifies proportion variability explained fixed effects. coefficients table covariance parameters describes model’s dependence. larger de parameter, variability process attributed relevant effect. , model’s random variability comes tail-portion model. larger range parameter, correlated nearby observations respect relevant effect. directly compare sources variability model using varcomp function: variability summer mean stream temperature explained fixed effects elevation precipitation (Covariates (PR-sq)) well tail-component. Note values proportion column sum one. remainder subsection, describe broom (Robinson, Hayes, Couch 2021) functions tidy(), glance() augment(). tidy() tidies coefficient output convenient tibble, glance() glances model-fit statistics, augment() augments data fitted model diagnostics. tidy fixed effects (add confidence intervals) running glance model-fit statistics running columns tibble represent: n: sample size. p: number fixed effects (linearly independent columns \\(\\mathbf{X}\\)). npar: number estimated covariance parameters. value: value minimized objective function used fitting model. AIC: Akaike Information Criterion (AIC). AICc: AIC small sample size correction. logLik: log-likelihood. deviance: deviance. pseudo.r.squared: pseudo r-squared. glances() function can used glance multiple models . Suppose wanted compare current model new model omits tail-Euclidean components. using glances() running Often AIC AICc used model selection, balance model fit model simplicity. lower AIC AICc original model (ssn_mod) indicates better fit data (ssn_mod2). Outside glance() glances(), functions AIC(), AICc(), logLik(), deviance(), pseudoR2() available compute relevant statistics. Note additive required tail-covariance specified. able compare AIC AICc models fit using REML changing covariance structure, fixed effects structure. compare AIC AICc models varying fixed effect covariance structures, use ML. example, compare model without elevation assess importance: Elevation seems important model fit, evidenced lower AIC. Erin E. Peterson Ver Hoef (2010) describe two-step model procedure model selection based AIC comparing models varying covariance fixed structures. First, covariance components included (tail-, tail-, Euclidean, nugget) fixed effects compared using ML. using model lowest AIC, refit using REML compare models varying combinations covariance components. Finally, proceed model lowest AIC. Another approach compare suite models (varying fixed effect covariance components) using ML refit best model using REML. Henceforth, proceed REML models, ssn_mod ssn_mod2. Another way compare model fits leave-one-cross validation available via loocv() function. loocv() returns many model-fit statistics. One root-mean-squared-prediction error, captures typical absolute error associated prediction. can compare mean-squared-prediction error ssn_mod, ssn_mod2: ssn_mod better model respect AIC, AICc, RMSPE shortly use return model diagnostics make predictions. loocv() predictions using ssn_mod typically within 0.437. true summer mean stream temperature. Note model comparison using loocv() depend estimation method (ML vs REML). augment data model diagnostics running columns tibble represent: Summer_mn: Summer mean stream temperature. ELEV_DEM: Elevation. Precipitation: Precipitation. .fitted: fitted values (estimated mean given explanatory variable values). .resid: residuals (response minus fitted values). .hat: leverage (hat) values. .cooksd: Cook’s distance. .std.residuals: Standardized residuals. pid: pid value. geometry: spatial information sf object. default, augment() returns variables data used model. variables original data returned setting drop = FALSE. can write augmented data shapefile loading sf (comes installed alongside SSN2) running Many model diagnostics returned augment() can visualized running using plot(). example, plot fitted values standardized residuals running  6 total diagnostic plots (specified via argument) return information returned running plot() lm() object.","code":"summary(ssn_mod) #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_type = \"gaussian\", additive = \"afvArea\") #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -3.6393 -2.0646 -0.5952  0.2143  0.7497  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 76.195041   7.871574   9.680  < 2e-16 *** #> ELEV_DEM    -0.026905   0.003646  -7.379  1.6e-13 *** #> AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *   #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.6124 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  3.800e+00 #>   tailup exponential         range  4.194e+06 #>   taildown spherical  de (parsill)  4.480e-01 #>   taildown spherical         range  1.647e+05 #>      euclid gaussian  de (parsill)  1.509e-02 #>      euclid gaussian         range  4.496e+03 #>               nugget        nugget  2.087e-02 varcomp(ssn_mod) #> # A tibble: 5 × 2 #>   varcomp            proportion #>   <chr>                   <dbl> #> 1 Covariates (PR-sq)    0.612   #> 2 tailup_de             0.344   #> 3 taildown_de           0.0405  #> 4 euclid_de             0.00137 #> 5 nugget                0.00189 tidy(ssn_mod, conf.int = TRUE) #> # A tibble: 3 × 7 #>   term        estimate std.error statistic  p.value conf.low conf.high #>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl> #> 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6      #> 2 AREAWTMAP   -0.00910   0.00446     -2.04 4.14e- 2  -0.0178 -0.000356 #> 3 ELEV_DEM    -0.0269    0.00365     -7.38 1.60e-13  -0.0341 -0.0198 glance(ssn_mod) #> # A tibble: 1 × 9 #>       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1    45     3     7  59.3  73.3  76.3  -29.6     41.9            0.612 ssn_mod2 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   taildown_type = \"spherical\" ) glances(ssn_mod, ssn_mod2) #> # A tibble: 2 × 10 #>   model        n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <chr>    <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1 ssn_mod     45     3     7  59.3  73.3  76.3  -29.6     41.9            0.612 #> 2 ssn_mod2    45     3     3 130.  136.  137.   -65.1     41.9            0.180 ml_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) ml_mod2 <- ssn_lm(   formula = Summer_mn ~ AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) glances(ml_mod, ml_mod2) #> # A tibble: 2 × 10 #>   model       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <chr>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1 ml_mod     45     3     7  39.3  59.3  65.8  -19.6     44.6           0.609  #> 2 ml_mod2    45     2     7  56.1  74.1  79.3  -28.1     42.0           0.0344 loocv_mod <- loocv(ssn_mod) loocv_mod$RMSPE #> [1] 0.4365597 loocv_mod2 <- loocv(ssn_mod2) loocv_mod2$RMSPE #> [1] 0.8150283 aug_ssn_mod <- augment(ssn_mod) aug_ssn_mod #> Simple feature collection with 45 features and 9 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1530805 ymin: 2527111 xmax: -1503079 ymax: 2537823 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version #> # A tibble: 45 × 10 #>    Summer_mn ELEV_DEM AREAWTMAP .fitted .resid   .hat  .cooksd .std.resid pid   #>  *     <dbl>    <int>     <dbl>   <dbl>  <dbl>  <dbl>    <dbl>      <dbl> <chr> #>  1     11.4      1977      940.    14.4  -3.07 0.0915 0.0962       -1.78  1     #>  2     10.7      1984     1087.    12.9  -2.20 0.114  0.00471      -0.352 2     #>  3     10.4      1993     1087.    12.7  -2.25 0.0372 0.00724      -0.764 3     #>  4     10.1      2007     1087.    12.3  -2.18 0.0251 0.00153      -0.427 4     #>  5     10.1      2009     1087.    12.3  -2.13 0.0374 0.000583     -0.216 5     #>  6      9.81     2012     1109.    12.0  -2.16 0.0602 0.0150       -0.863 6     #>  7      9.76     2023     1116.    11.6  -1.85 0.0736 0.00739       0.549 7     #>  8      9.77     2023     1116.    11.6  -1.84 0.0648 0.00687       0.564 8     #>  9      9.53     2026     1130.    11.4  -1.87 0.112  0.00152       0.202 9     #> 10     12.6      1988      864.    14.9  -2.28 0.0498 0.00964      -0.762 10    #> # ℹ 35 more rows #> # ℹ 1 more variable: geometry <POINT [m]> library(sf) st_write(aug_ssn_mod, paste0(tempdir(), \"/aug_ssn_mod.shp\")) plot(ssn_mod, which = 1)"},{"path":"/articles/introduction.html","id":"prediction-kriging","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models > Linear SSN Models","what":"Prediction (Kriging)","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"Commonly goal data analysis make predictions unobserved locations. spatial contexts, prediction often called Kriging. Next make summer mean stream temperature predictions location pred1km data mf04p running augment() previously used augment original data model diagnostics, can also used augment newdata predictions: .fitted represents predictions. Confidence intervals mean response prediction intervals predicted response can obtained specifying interval argument predict() augment(). default, predict() augment() compute 95% intervals, though can changed using level argument. arguments predict() augment() ssn_lm() objects slightly different arguments lm() object – learn run help(\"predict.SSN2\", \"SSN2\") help(\"augment.SSN2\", \"SSN2\"). visualize predictions (overlain onto stream network) running  Previously wrote model diagnostics shapefile. Now write predictions geopackage (recall sf must loaded) running performing prediction SSN2, name newdata must name prediction data set contained ssn.object$preds. newdata omitted value \"\", prediction performed prediction data sets ssn.object. example, makes predictions pred1km, Knapp, CapeHorn (names mf04p$preds). Lastly, observations (obs object) whose response missing (NA), observations removed model fitting moved prediction data set named .missing. predictions can obtained locations. can also predict average value region using block Prediction (instead making point predictions). predict average summer mean temperature throughout Middle Fork stream network running","code":"predict(ssn_mod, newdata = \"pred1km\") aug_preds <- augment(ssn_mod, newdata = \"pred1km\") aug_preds[, \".fitted\"] #> Simple feature collection with 175 features and 1 field #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1530631 ymin: 2521707 xmax: -1500020 ymax: 2540253 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version #> # A tibble: 175 × 2 #>    .fitted           geometry #>      <dbl>        <POINT [m]> #>  1    14.6 (-1520657 2536657) #>  2    15.0 (-1519866 2536812) #>  3    14.8 (-1521823 2536911) #>  4    15.0 (-1523183 2537256) #>  5    15.2 (-1523860 2537452) #>  6    15.1 (-1525443 2537698) #>  7    15.1 (-1526397 2537254) #>  8    15.0 (-1527436 2536803) #>  9    14.9 (-1529043 2536449) #> 10    14.9 (-1529689 2537313) #> # ℹ 165 more rows ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = aug_preds, aes(color = .fitted), size = 2) +   scale_color_viridis_c(limits = c(-1.5, 17), option = \"H\") +   theme_bw() st_write(aug_preds, paste0(tempdir(), \"/aug_preds.gpkg\")) predict(ssn_mod) predict(ssn_mod, newdata = \"all\") predict(ssn_mod, newdata = \"pred1km\", block = TRUE, interval = \"prediction\") #>        fit      lwr      upr #> 1 10.73479 9.987173 11.48241"},{"path":"/articles/introduction.html","id":"sec:advanced","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models > Linear SSN Models","what":"Advanced Features","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"several additional modeling tools available SSN2 discuss next: Fixing parameter values; non-spatial random effects; partition factors. Perhaps want assume particular covariance parameter known. may reasonable information known process desire perform model selection nested models create profile likelihood confidence intervals. Fixing covariance parameters SSN2 accomplished via tailup_initial, taildown_initial, euclid_initial, nugget_initial arguments ssn_lm(). arguments passed appropriate initial value object created using tailup_initial(), taildown_initial(), euclid_initial(), nugget_initial() function, respectively. example, suppose want fix Euclidean dependent error variance parameter 1, forcing component moderate effect covariance. First, specify appropriate object running euclid_init output shows range parameter initial value 1 assumed known. range parameter still estimated. Next model fit: Notice Euclidean variance 1. Random effects can added SSN model incorporate additional sources variability separate stream network. Common additional sources variability modeled include repeated observations sites network-specific effects. random effects modeled using similar syntax random effects nlme (Pinheiro Bates 2006) lme4 (Bates et al. 2015) R packages, specified via formula passed random argument. model random intercepts two networks data running random = ~ .factor(netID) short-hand random = ~ (1 | .factor(netID)), familiar lme4 nlme syntax. partition factor variable allows observations uncorrelated different levels partition factors. example, one may want partition model two networks despite adjacency significant land mass similar obstruction. Partition factors modeled using formula contains single variable passed partition_factor argument. model two networks uncorrelated running short, partition factor enables model fitting builds independence places typical stream network deemed relevant researcher.","code":"euclid_init <- euclid_initial(\"gaussian\", de = 1, known = \"de\") euclid_init #> $initial #> de  #>  1  #>  #> $is_known #>   de  #> TRUE  #>  #> attr(,\"class\") #> [1] \"euclid_gaussian\" ssn_init <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_initial = euclid_init,   additive = \"afvArea\" ) ssn_init #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_initial = euclid_init, additive = \"afvArea\") #>  #>  #> Coefficients (fixed): #> (Intercept)     ELEV_DEM    AREAWTMAP   #>   77.181436    -0.027921    -0.008011   #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  3.882e+00 #>   tailup exponential         range  1.668e+06 #>   taildown spherical  de (parsill)  3.233e-02 #>   taildown spherical         range  7.041e+03 #>      euclid gaussian  de (parsill)  1.000e+00 #>      euclid gaussian         range  8.231e+04 #>               nugget        nugget  1.948e-02 ssn_rand <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   random = ~ as.factor(netID) ) ssn_rand #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_type = \"gaussian\", additive = \"afvArea\", random = ~as.factor(netID)) #>  #>  #> Coefficients (fixed): #> (Intercept)     ELEV_DEM    AREAWTMAP   #>   76.219407    -0.026705    -0.009449   #>  #> Coefficients (covariance): #>               Effect             Parameter   Estimate #>   tailup exponential          de (parsill)  3.688e+00 #>   tailup exponential                 range  6.971e+05 #>   taildown spherical          de (parsill)  1.191e-02 #>   taildown spherical                 range  1.089e+04 #>      euclid gaussian          de (parsill)  3.377e-02 #>      euclid gaussian                 range  1.368e+04 #>               nugget                nugget  2.046e-02 #>               random  1 | as.factor(netID)  3.809e-01 ssn_part <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   partition_factor = ~ as.factor(netID) ) ssn_part #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_type = \"gaussian\", additive = \"afvArea\", partition_factor = ~as.factor(netID)) #>  #>  #> Coefficients (fixed): #> (Intercept)     ELEV_DEM    AREAWTMAP   #>    76.32118     -0.02805     -0.00697   #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  3.300e+00 #>   tailup exponential         range  6.695e+07 #>   taildown spherical  de (parsill)  3.618e-07 #>   taildown spherical         range  2.233e+06 #>      euclid gaussian  de (parsill)  1.002e-01 #>      euclid gaussian         range  5.036e+03 #>               nugget        nugget  2.525e-02"},{"path":"/articles/introduction.html","id":"sec:glm","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models","what":"Generalized Linear SSN Models","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"Generalized linear spatial stream network models response vector \\(\\mathbf{y}\\) spatially dependent random errors often parameterized \\(\\boldsymbol{\\mu}\\) mean \\(\\mathbf{y}\\), \\(g(\\cdot)\\) link function “links” \\(\\mathbf{\\mu}\\) linear function predictor variables random errors, terms defined linear spatial stream network models. Rather assuming \\(y\\) normally (Gaussian) distributed often case linear spatial stream network models, generalized linear spatial stream network models assume \\(\\mathbf{y}\\) follows one many distributions corresponding link function. summarize families generalized linear spatial stream network models supported SSN2 link functions, type data typically associated families. generalized linear models generally, see McCullagh Nelder (1989), Myers et al. (2012), Faraway (2016). ssn_glm() function used fit generalized linear spatial stream network models bears many similarities base-R’s glm() function non-spatial generalized linear models. family (.e., resposne distribution) controlled family argument. family Gaussian(), model fit equivalent one fit using ssn_lm(). Note parameters estimated relevant link scale interpreted accordingly. C16 variable mf04p represents number times daily summer stream temperature exceeded 16 Celsius:  Suppose want model C16 function elevation precipitation. Often count data modeled using Poisson regression. Using tail-, tail-, nugget components, fit Poisson model running previous SSN2 functions used explore linear spatial stream network models also available generalized linear spatial stream network models. example, can summarize model using summary(): Similar summaries glm() objects, summaries ssn_glm() objects include original function call, deviance residuals, coefficients table fixed effects. (Intercept) represents log average C16 sea level (elevation zero) zero precipitation, ELEV_DEM represents decrease log average summer mean temperature one unit (meter) increase elevation, AREAWTMAP represents decrease log average summer mean temperature one unit (mm) increase precipitation. strong evidence log average summer mean temperature decreases elevation (\\(p\\)-value \\(< 0.001\\)), moderate evidence log average summer mean temperature decreases precipitation (\\(p\\)-value \\(\\approx\\) 0.03). Recall covariance parameter estimates link (, log) scale. Poisson model assumes observations mean variance equal. Often ecological environmental data, variance larger mean – called overdispersion. negative binomial model accommodates overdispersion count data. fit negative binomial model running can compare fit models using leave-one-cross validation running Poisson lower RMSPE, suggests evidence overdispersion. advanced modeling features discussed linear spatial stream network models (e.g., fixing covariance parameter values, random effects, partition factors) also available generalized linear spatial stream network models.","code":"ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = C16), size = 2) +   scale_color_viridis_c(option = \"H\") +   theme_bw() ssn_pois <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"poisson\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) summary(ssn_pois) #>  #> Call: #> ssn_glm(formula = C16 ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     family = \"poisson\", tailup_type = \"epa\", taildown_type = \"mariah\",  #>     additive = \"afvArea\") #>  #> Deviance Residuals: #>      Min       1Q   Median       3Q      Max  #> -2.12277 -0.43689 -0.07905  0.26269  1.28473  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 45.549245  10.025635   4.543 5.54e-06 *** #> ELEV_DEM    -0.017567   0.004544  -3.866 0.000111 *** #> AREAWTMAP   -0.007586   0.003413  -2.222 0.026253 *   #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.2443 #>  #> Coefficients (covariance): #>            Effect     Parameter   Estimate #>        tailup epa  de (parsill)  9.484e-01 #>        tailup epa         range  4.290e+04 #>   taildown mariah  de (parsill)  1.774e-02 #>   taildown mariah         range  9.782e+06 #>            nugget        nugget  1.008e-04 #>        dispersion    dispersion  1.000e+00 ssn_nb <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"nbinomial\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) loocv_pois <- loocv(ssn_pois) loocv_pois$RMSPE #> [1] 5.249275 loocv_nb <- loocv(ssn_nb) loocv_nb$RMSPE #> [1] 5.255264"},{"path":"/articles/introduction.html","id":"simulating-spatial-stream-network-data","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models","what":"Simulating Spatial Stream Network Data","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"ssn_simulate() function used simulate data stream network. First, covariance parameter values specified seed set: call ssn_simulate(), specifying family argument depending type simulated data desired (, Gaussian), ssn.object network (, observed network): simulate binomial (presence/absence) data running Currently, ssn_simulate() works observed network (network = \"obs\"). However, simulation SSN2 focus future updates, plan add support simulating prediction networks well observed prediction networks simultaneously.","code":"tu_params <- tailup_params(\"exponential\", de = 0.4, range = 1e5) td_params <- taildown_params(\"spherical\", de = 0.1, range = 1e6) euc_params <- euclid_params(\"gaussian\", de = 0.2, range = 1e3) nug_params <- nugget_params(\"nugget\", nugget = 0.1) set.seed(2) sims <- ssn_simulate(   family = \"gaussian\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 1 ) head(sims) #> [1] -0.8022248 -0.2953168  0.7179851 -0.2965733 -0.2811469 -0.1010336 sims <- ssn_simulate(   family = \"binomial\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 2 ) head(sims) #>      1 2 #> [1,] 0 1 #> [2,] 0 1 #> [3,] 0 1 #> [4,] 1 0 #> [5,] 1 0 #> [6,] 0 1"},{"path":"/articles/introduction.html","id":"function-glossary","dir":"Articles","previous_headings":"","what":"Function Glossary","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"list two SSN2 functions used fit models: ssn_glm(): Fit spatial stream network generalized linear model. ssn_lm(): Fit spatial stream network linear model. list commonly used SSN2 functions operate model fits: AIC(): Compute AIC. AICc(): Compute AICc. anova(): Perform analysis variance. augment(): Augment data diagnostics new data predictions. coef(): Return coefficients. confint(): Compute confidence intervals. cooks.distance(): Compute Cook’s distance. covmatrix(): Return covariance matrices. deviance(): Compute deviance. fitted(): Compute fitted values. glance(): Glance fitted model. glances(): Glance multiple fitted models. hatvalues(): Compute leverage (hat) values. logLik(): Compute log-likelihood. loocv(): Perform leave-one-cross validation compute relevant statistics. model.matrix(): Return model matrix (\\(\\mathbf{X}\\)). plot(): Create fitted model plots. predict(): Compute predictions prediction intervals. pseudoR2(): Compute pseudo r-squared. residuals(): Compute residuals. summary(): Summarize fitted models. tidy(): Tidy fitted models. varcomp(): Compare variance components. vcov(): Compute variance-covariance matrices estimated parameters. Documentation functions can found running ?function_name.SSN2 help(\"function_name.SSN2\", \"SSN2\"). example, ?predict.SSN2 help(\"predict.SSN2\", \"SSN2\"). list commonly used SSN2 functions manipulating SSN objects: ssn_create_distmat(): Create distance matrices .ssn directory use modeling functions. ssn_get_data(): Extract sf data.frame observed prediction locations SSN object. ssn_get_netgeom(): Extract topological information netgeom column. ssn_get_stream_distmat(): Extract stream distance matrices observed prediction locations SSN object. ssn_import(): Import SSN object .ssn directory. ssn_import_predpts(): Import prediction data store within existing SSN object. ssn_put_data(): Replace sf data.frame observed prediction locations SSN object. ssn_split_predpts(): Split prediction data stored within SSN object multiple prediction data sets. ssn_subset(): Subset existing SSN object based logical expression. SSN_to_SSN2(): Convert S4 SpatialStreamNetwork object created SSN S3 SSN object used SSN2. ssn_update_path(): Update path element SSN object. ssn_write(): Write SSN project new local .ssn directory. functions manipulate SSN objects ssn_ prefix, makes easily accessible via tab completion RStudio. list commonly used miscellaneous SSN2 functions: ssn_simulate(): Simulate spatially correlated random variables stream network. full list SSN2 functions alongside documentation, see documentation manual.","code":""},{"path":"/articles/introduction.html","id":"from-ssn-to-ssn2","dir":"Articles","previous_headings":"","what":"From SSN to SSN2","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"present table SSN functions provide relevant successors SSN2: addition function name changes , function argument names also changed. Please read documentation function interest see relevant argument name changes.","code":""},{"path":"/articles/introduction.html","id":"the-future-of-ssn2","dir":"Articles","previous_headings":"","what":"The Future of SSN2","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"several features planned future versions SSN2 make initial release due October timeline regarding rgdal, rgeos, maptools retirements. , plan regularly update add features SSN2 coming years – check back often! features include additional tools large data sets (model fitting prediction), manipulating .ssn object, simulating data, . best make future versions SSN2 backward compatible version, minor changes may occur ready release version 1.0.0.","code":""},{"path":"/articles/introduction.html","id":"r-code-appendix","dir":"Articles","previous_headings":"","what":"R Code Appendix","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"","code":"library(SSN2) citation(package = \"SSN2\") path <- system.file(\"lsndata/MiddleFork04.ssn\", package = \"SSN2\") copy_lsn_to_temp() path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(   path = path,   predpts = c(\"pred1km\", \"CapeHorn\", \"Knapp\"),   overwrite = TRUE ) summary(mf04p) library(ggplot2) names(mf04p$preds) ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$preds$pred1km, pch = 17, color = \"blue\") +   geom_sf(data = mf04p$obs, color = \"brown\", size = 2) +   theme_bw() ssn_create_distmat(   ssn.object = mf04p,   predpts = c(\"pred1km\", \"CapeHorn\", \"Knapp\"),   among_predpts = TRUE,   overwrite = TRUE ) ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = Summer_mn), size = 2) +   scale_color_viridis_c(limits = c(-1.5, 17), option = \"H\") +   theme_bw() tg <- Torgegram(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   type = c(\"flowcon\", \"flowuncon\", \"euclid\") ) plot(tg) ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\" ) summary(ssn_mod) varcomp(ssn_mod) tidy(ssn_mod, conf.int = TRUE) glance(ssn_mod) ssn_mod2 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   taildown_type = \"spherical\" ) glances(ssn_mod, ssn_mod2) ml_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) ml_mod2 <- ssn_lm(   formula = Summer_mn ~ AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) glances(ml_mod, ml_mod2) loocv_mod <- loocv(ssn_mod) loocv_mod$RMSPE loocv_mod2 <- loocv(ssn_mod2) loocv_mod2$RMSPE aug_ssn_mod <- augment(ssn_mod) aug_ssn_mod library(sf) st_write(aug_ssn_mod, paste0(tempdir(), \"/aug_ssn_mod.shp\")) plot(ssn_mod, which = 1) predict(ssn_mod, newdata = \"pred1km\") aug_preds <- augment(ssn_mod, newdata = \"pred1km\") aug_preds[, \".fitted\"] ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = aug_preds, aes(color = .fitted), size = 2) +   scale_color_viridis_c(limits = c(-1.5, 17), option = \"H\") +   theme_bw() st_write(aug_preds, paste0(tempdir(), \"/aug_preds.gpkg\")) predict(ssn_mod) predict(ssn_mod, newdata = \"all\") predict(ssn_mod, newdata = \"pred1km\", block = TRUE, interval = \"prediction\") euclid_init <- euclid_initial(\"gaussian\", de = 1, known = \"de\") euclid_init ssn_init <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_initial = euclid_init,   additive = \"afvArea\" ) ssn_init ssn_rand <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   random = ~ as.factor(netID) ) ssn_rand ssn_part <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   partition_factor = ~ as.factor(netID) ) ssn_part ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = C16), size = 2) +   scale_color_viridis_c(option = \"H\") +   theme_bw() ssn_pois <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"poisson\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) summary(ssn_pois) ssn_nb <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"nbinomial\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) loocv_pois <- loocv(ssn_pois) loocv_pois$RMSPE loocv_nb <- loocv(ssn_nb) loocv_nb$RMSPE tu_params <- tailup_params(\"exponential\", de = 0.4, range = 1e5) td_params <- taildown_params(\"spherical\", de = 0.1, range = 1e6) euc_params <- euclid_params(\"gaussian\", de = 0.2, range = 1e3) nug_params <- nugget_params(\"nugget\", nugget = 0.1) set.seed(2) sims <- ssn_simulate(   family = \"gaussian\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 1 ) head(sims) sims <- ssn_simulate(   family = \"binomial\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 2 ) head(sims)"},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michael Dumelle. Author, maintainer. Jay M. Ver Hoef. Author. Erin Peterson. Author. Alan Pearse. Contributor. Dan Isaak. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Dumelle M, Peterson, E, Ver Hoef JM, Pearse , Isaak D (2023). SSN2: Spatial Modeling Stream Networks R. R package version 0.1.0","code":"@Manual{,   title = {{SSN2}: Spatial Modeling on Stream Networks in {R}},   author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak},   year = {2023},   note = {{R} package version 0.1.0}, }"},{"path":"/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to SSN2 development","title":"Contributing to SSN2 development","text":"outlines propose change SSN2. two main approaches: Create GitHub issue identifying bug suggesting feature. Contribute code change via GitHub pull request. prefer use GitHub, please send email Dumelle.Michael@epa.gov.","code":""},{"path":"/CONTRIBUTING.html","id":"github-issue","dir":"","previous_headings":"","what":"GitHub Issue","title":"Contributing to SSN2 development","text":"may create GitHub issue identifying bug suggesting feature. may access SSN2’s GitHub issues webpage visiting https://github.com/USEPA/SSN2/issues. Please illustrate bug feature using minimal reproducible example, reprex, linked . See tidyverse guide create great issue linked advice.","code":""},{"path":"/CONTRIBUTING.html","id":"github-pull-request","dir":"","previous_headings":"","what":"GitHub Pull Request","title":"Contributing to SSN2 development","text":"contribute change SSN2, please use pull request. learn creating GitHub pull request, please see GitHub guide linked .","code":""},{"path":"/index.html","id":"ssn2-spatial-modeling-on-stream-networks","dir":"","previous_headings":"","what":"Spatial Modeling on Stream Networks","title":"Spatial Modeling on Stream Networks","text":"SSN2 R package spatial statistical modeling prediction stream networks, including models based -stream distance. Models created using moving average constructions. Spatial linear models, including explanatory variables, can fit (restricted) maximum likelihood. Mapping graphical functions included. successor SSN R package. See SSN2 website : https://usepa.github.io/SSN2/.","code":""},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Spatial Modeling on Stream Networks","text":"use SSN2 formal publication report, please cite . Citing SSN2 lets us devote resources future. View SSN2 citation running","code":"citation(package = \"SSN2\") #>  #> To cite SSN2 in publications use: #>  #>   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.1.0 #>  #> A BibTeX entry for LaTeX users is #>  #>   @Manual{, #>     title = {{SSN2}: Spatial Modeling on Stream Networks in {R}}, #>     author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak}, #>     year = {2023}, #>     note = {{R} package version 0.1.0}, #>   }"},{"path":"/index.html","id":"statement-of-need","dir":"","previous_headings":"","what":"Statement of Need","title":"Spatial Modeling on Stream Networks","text":"Streams provide vital aquatic services sustain wildlife, provide drinking irrigation water, support recreational cultural activities. Data often collected various locations stream network used characterize scientific phenomenon stream. Spatial stream network (SSN) models use spatial statistical modeling framework describe unique complex dependencies stream network resulting branching network structure, directional water flow, differences flow volume. SSN models relate response variable one explanatory variables, spatially independent error term (.e., nugget), three spatially dependent error terms: tail-errors, tail-errors, Euclidean errors. Tail-errors restrict spatial dependence flow-connected sites (.e., water flows upstream downstream site) incorporate spatial weights (.e., additive function) describe branching network . Tail-errors describe spatial dependence flow-connected flow-unconnected (.e., sites share common downstream junction flow) sites, spatial weights required. Euclidean errors describe spatial dependence sites based Euclidean distance governed factors confined stream network like regional geology.","code":""},{"path":"/index.html","id":"installation-instructions","dir":"","previous_headings":"","what":"Installation Instructions","title":"Spatial Modeling on Stream Networks","text":"Install load recent approved version CRAN running Install load recent version ofSSN2 GitHub running Install load recent development version ofSSN2 GitHub running","code":"# install the most recent approved version from CRAN install.packages(\"SSN2\") # load the most recent approved version from CRAN library(SSN2) # Installing from GitHub requires you first install the remotes package install.packages(\"remotes\")  # install the most recent version from GitHub remotes::install_github(\"USEPA/SSN2\", ref = \"main\") # load the most recent development version from GitHub library(SSN2) # Installing from GitHub requires you first install the remotes package install.packages(\"remotes\")  # install the most recent version from GitHub remotes::install_github(\"USEPA/SSN2\", ref = \"develop\") # load the most recent development version from GitHub library(SSN2)"},{"path":"/index.html","id":"example-usage","dir":"","previous_headings":"","what":"Example Usage","title":"Spatial Modeling on Stream Networks","text":"provide brief example showing use SSN2. thorough introduction software, see introductory vignette linked . list functions available SSN2, see function reference linked . load SSN2, copy .ssn object comes SSN2 temporary directory, create stream distance matrices used modeling running fit summarize SSN model explaining summer water temperatue (Summer_mn) function elevation (ELEV_DEM) precipitation (AREAWTMAP) exponential, spherical, Gaussian structures tail-, tail-, Euclidean errors, respectively, running tidy, glance , augment (diagnostics) fitted model running make predictions prediction sites (pred1km) running predict() (augment():","code":"library(SSN2) copy_lsn_to_temp() path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(path, predpts = \"pred1km\") ssn_create_distmat(mf04p, predpts = \"pred1km\", overwrite = TRUE) ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\" ) summary(ssn_mod) #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_type = \"gaussian\", additive = \"afvArea\") #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -3.6393 -2.0646 -0.5952  0.2143  0.7497  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 76.195041   7.871574   9.680  < 2e-16 *** #> ELEV_DEM    -0.026905   0.003646  -7.379  1.6e-13 *** #> AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *   #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.6124 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  3.800e+00 #>   tailup exponential         range  4.194e+06 #>   taildown spherical  de (parsill)  4.480e-01 #>   taildown spherical         range  1.647e+05 #>      euclid gaussian  de (parsill)  1.509e-02 #>      euclid gaussian         range  4.496e+03 #> tidy(ssn_mod, conf.int = TRUE) #> # A tibble: 3 × 7 #>   term        estimate std.error statistic  p.value conf.low conf.high #>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl> #> 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6      #> 2 AREAWTMAP   -0.00910   0.00446     -2.04 4.14e- 2  -0.0178 -0.000356 #> 3 ELEV_DEM    -0.0269    0.00365     -7.38 1.60e-13  -0.0341 -0.0198 glance(ssn_mod) #> # A tibble: 1 × 9 #>       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1    45     3     7  59.3  73.3  76.3  -29.6     41.9            0.612 head(augment(ssn_mod)) #> Simple feature collection with 6 features and 9 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1515032 ymin: 2529461 xmax: -1512690 ymax: 2531883 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version #> # A tibble: 6 × 10 #>   Summer_mn ELEV_DEM AREAWTMAP .fitted .resid   .hat  .cooksd .std.resid pid   #>       <dbl>    <int>     <dbl>   <dbl>  <dbl>  <dbl>    <dbl>      <dbl> <chr> #> 1     11.4      1977      940.    14.4  -3.07 0.0915 0.0962       -1.78  1     #> 2     10.7      1984     1087.    12.9  -2.20 0.114  0.00471      -0.352 2     #> 3     10.4      1993     1087.    12.7  -2.25 0.0372 0.00724      -0.764 3     #> 4     10.1      2007     1087.    12.3  -2.18 0.0251 0.00153      -0.427 4     #> 5     10.1      2009     1087.    12.3  -2.13 0.0374 0.000583     -0.216 5     #> 6      9.81     2012     1109.    12.0  -2.16 0.0602 0.0150       -0.863 6     #> # ℹ 1 more variable: geometry <POINT [m]> preds <- predict(ssn_mod, newdata = \"pred1km\", interval = \"prediction\") head(preds) #>        fit      lwr      upr #> 1 14.64383 14.27138 15.01627 #> 2 15.00608 14.65017 15.36198 #> 3 14.79235 14.27414 15.31057 #> 4 14.96884 14.45492 15.48276 #> 5 15.15182 14.73770 15.56595 #> 6 15.12783 14.76358 15.49208"},{"path":"/index.html","id":"contributing-to-ssn2","dir":"","previous_headings":"","what":"Contributing to SSN2","title":"Spatial Modeling on Stream Networks","text":"encourage users propose changes SSN2. 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GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.>"},{"path":"/reference/AIC.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute AIC and AICc of fitted model objects — AIC.SSN2","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"Compute AIC AICc one several fitted model objects log-likelihood value can obtained.","code":""},{"path":"/reference/AIC.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"","code":"# S3 method for ssn_lm AIC(object, ..., k = 2)  # S3 method for ssn_glm AIC(object, ..., k = 2)  # S3 method for ssn_lm AICc(object, ..., k = 2)  # S3 method for ssn_glm AICc(object, ..., k = 2)"},{"path":"/reference/AIC.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... Optionally fitted model objects. k penalty parameter, taken 2. Currently allowed differ 2 (needed generic consistency).","code":""},{"path":"/reference/AIC.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"just one object provided, numeric value corresponding AIC AICc. multiple objects provided, data.frame rows corresponding objects columns representing number parameters estimated (df) AIC AICc.","code":""},{"path":"/reference/AIC.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"comparing models fit maximum restricted maximum likelihood, smaller AIC AICc, better fit. AICc contains correction AIC small sample sizes. AIC AICc comparisons \"ml\" \"reml\" models meaningless -- comparisons made within set models estimated using \"ml\" set models estimated using \"reml\". AIC AICc comparisons \"reml\" must use fixed effects. vary covariance parameters fixed effects simultaneously, use \"ml\". AIC defined \\(-2loglik + 2(npar)\\) AICc defined \\(-2loglik + 2n(npar) / (n - npar - 1)\\), \\(n\\) sample size \\(npar\\) number estimated parameters. \"ml\", \\(npar\\) number estimated covariance parameters plus number estimated fixed effects. \"reml\", \\(npar\\) number estimated covariance parameters.","code":""},{"path":"/reference/AIC.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) AIC(ssn_mod) #> [1] 60.18256 AICc(ssn_mod) #> [1] 60.76793"},{"path":"/reference/anova.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"Compute analysis variance tables fitted model object likelihood ratio test two fitted model objects.","code":""},{"path":"/reference/anova.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"","code":"# S3 method for ssn_lm anova(object, ..., test = TRUE, Terms, L)  # S3 method for ssn_glm anova(object, ..., test = TRUE, Terms, L)  # S3 method for anova.ssn_lm tidy(x, ...)  # S3 method for anova.ssn_glm tidy(x, ...)"},{"path":"/reference/anova.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... additional fitted model object ssn_lm() ssn_glm() (anova()). test logical value indicating whether p-values asymptotic Chi-squared hypothesis tests returned. Defaults TRUE. Terms optional character integer vector specifies terms model used jointly compute test statistics p-values (test = TRUE) null hypothesis zero. Terms used single fitted model object passed function. Terms character vector, contain names fixed effect terms. Terms integer vector, correspond order (starting one) names fixed effect terms. easiest way obtain names possible terms run tidy(anova(object))$effects (integer representation matches positions vector). L optional numeric matrix list specifying linear combinations coefficients model used compute test statistics p-values (test = TRUE) coefficient constraints corresponding null hypothesis zero. L used single fitted model object passed function. L numeric matrix, rows indicate coefficient constraints columns represent coefficients. single hypothesis test conducted null hypothesis zero. L list, list element numeric matrix specified . separate hypothesis tests conducted. easiest way obtain possible coefficients run tidy(object)$term. x object anova(object).","code":""},{"path":"/reference/anova.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"one fitted model object present, anova() returns data frame degrees freedom (Df), test statistics (Chi2), p-values (Pr(>Chi2) test = TRUE) corresponding asymptotic Chi-squared hypothesis tests model term. two fitted model objects present, anova() returns data frame difference degrees freedom full reduced model (Df), test statistic (Chi2), p-value corresponding likelihood ratio test (Pr(>Chi2) test = TRUE). Whether one two fitted model objects provided, tidy() can used obtain tidy tibbles anova(object) output.","code":""},{"path":"/reference/anova.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"one fitted model object present, anova() performs general linear hypothesis test corresponding hypothesis specified matrix constraints. Terms L specified, model term tested zero (correspond type III marginal hypothesis tests classical ANOVA). Terms specified L specified, terms tested jointly zero. L specified, linear combinations terms specified L jointly tested zero. two fitted model objects present, one must \"reduced\" model nested \"full\" model. anova() performs likelihood ratio test.","code":""},{"path":"/reference/anova.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) anova(ssn_mod) #> Analysis of Variance Table #>  #> Response: Summer_mn #>             Df    Chi2 Pr(>Chi2)     #> (Intercept)  1 132.633 < 2.2e-16 *** #> ELEV_DEM     1  87.829 < 2.2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 tidy(anova(ssn_mod)) #> # A tibble: 2 × 4 #>   effects        df statistic  p.value #>   <chr>       <int>     <dbl>    <dbl> #> 1 (Intercept)     1     133.  1.09e-30 #> 2 ELEV_DEM        1      87.8 7.14e-21"},{"path":"/reference/augment.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment data with information from fitted model objects — augment.SSN2","title":"Augment data with information from fitted model objects — augment.SSN2","text":"Augment accepts fitted model object data set adds information observation data set. New columns always begin . prefix avoid overwriting columns original data set. Augment behaves differently depending whether original data new data requires augmenting. Typically, augmenting original data, fitted model object specified, augmenting new data, fitted model object newdata specified. augmenting original data, diagnostic statistics augmented row data set. augmenting new data, predictions optional intervals (confidence prediction) standard errors augmented row new data set.","code":""},{"path":"/reference/augment.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment data with information from fitted model objects — augment.SSN2","text":"","code":"# S3 method for ssn_lm augment(   x,   drop = TRUE,   newdata = NULL,   se_fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   level = 0.95,   ... )  # S3 method for ssn_glm augment(   x,   drop = TRUE,   newdata = NULL,   type = c(\"link\", \"response\"),   se_fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   newdata_size,   level = 0.95,   var_correct = TRUE,   ... )"},{"path":"/reference/augment.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment data with information from fitted model objects — augment.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). drop logical indicating whether drop extra variables fitted model object x augmenting. default drop TRUE. drop ignored augmenting newdata. newdata vector contains names prediction sf objects original ssn.object requiring prediction. original explanatory variables used create fitted model object x must present prediction sf object represented newdata. Defaults NULL, indicates nothing passed newdata augmenting occurs original data. value \"ssn\" shorthand specifying prediction sf objects. se_fit Logical indicating whether .se.fit column added augmented output. Passed predict() defaults FALSE. interval Character indicating type confidence interval columns add augmented newdata output. Passed predict() defaults \"none\". level Tolerance/confidence level. default 0.95. ... Additional arguments predict() augmenting newdata. type scale (response link) predictions obtained using ssn_glm objects. newdata_size size value observation newdata used predicting binomial family. var_correct logical indicating whether return corrected prediction variances predicting via models fit using ssn_glm. default TRUE.","code":""},{"path":"/reference/augment.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Augment data with information from fitted model objects — augment.SSN2","text":"augmenting original data set, tibble additional columns .fitted: Fitted value .resid: Response residual (difference observed fitted values) .hat: Leverage (diagonal hat matrix) .cooksd: Cook's distance .std.resid: Standardized residuals .se.fit: Standard error fitted value. augmenting new data set, tibble additional columns .fitted: Predicted (fitted) value .lower: Lower bound interval .upper: Upper bound interval .se.fit: Standard error predicted (fitted) value predictions prediction objects desired, output list element name matches prediction objects values predictions.","code":""},{"path":"/reference/augment.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augment data with information from fitted model objects — augment.SSN2","text":"augment() returns tibble sf object. Missing response values original data can augmented newdata object providing \".missing\" newdata argument.","code":""},{"path":[]},{"path":"/reference/augment.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augment data with information from fitted model objects — augment.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"CapeHorn\", overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) augment(ssn_mod) #> Simple feature collection with 45 features and 8 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1530805 ymin: 2527111 xmax: -1503079 ymax: 2537823 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version #> # A tibble: 45 × 9 #>    Summer_mn ELEV_DEM .fitted .resid    .hat    .cooksd .std.resid pid   #>  *     <dbl>    <int>   <dbl>  <dbl>   <dbl>      <dbl>      <dbl> <chr> #>  1     11.4      1977    14.3  -2.96 0.157   0.135         -1.31   1     #>  2     10.7      1984    14.1  -3.42 0.0924  0.0184        -0.631  2     #>  3     10.4      1993    13.9  -3.45 0.0301  0.0103        -0.827  3     #>  4     10.1      2007    13.5  -3.35 0.00798 0.000239      -0.245  4     #>  5     10.1      2009    13.4  -3.30 0.0105  0.00000674     0.0359 5     #>  6      9.81     2012    13.3  -3.52 0.00350 0.00285       -1.28   6     #>  7      9.76     2023    13.0  -3.26 0.0179  0.0000144      0.0401 7     #>  8      9.77     2023    13.0  -3.25 0.0169  0.0000741      0.0935 8     #>  9      9.53     2026    12.9  -3.40 0.00831 0.000891      -0.463  9     #> 10     12.6      1988    14.0  -1.45 0.0561  0.0290        -1.02   10    #> # ℹ 35 more rows #> # ℹ 1 more variable: geometry <POINT [m]> augment(ssn_mod, newdata = \"CapeHorn\") #> Simple feature collection with 654 features and 20 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1516634 ymin: 2527816 xmax: -1512722 ymax: 2531418 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version #> # A tibble: 654 × 21 #>       COMID GNIS_NAME       CDRAINAG AREAWTMAP   SLOPE ELEV_DEM FlowCMS AirMEANc #>  *    <int> <chr>              <dbl>     <dbl>   <dbl>    <int>   <dbl>    <dbl> #>  1 23519461 Cape Horn Creek     69.9     1087. 0.00843     2011    34.8     21.5 #>  2 23519461 Cape Horn Creek     69.9     1087. 0.00843     2011    34.8     21.5 #>  3 23519461 Cape Horn Creek     69.9     1087. 0.00843     2011    34.8     21.5 #>  4 23519461 Cape Horn Creek     69.9     1087. 0.00843     2011    34.8     21.5 #>  5 23519461 Cape Horn Creek     69.9     1087. 0.00843     2011    34.8     21.5 #>  6 23519461 Cape Horn Creek     69.9     1087. 0.00843     2011    34.8     21.5 #>  7 23519461 Cape Horn Creek     69.9     1087. 0.00843     2013    34.8     21.5 #>  8 23519461 Cape Horn Creek     69.9     1087. 0.00843     2013    34.8     21.5 #>  9 23519461 Cape Horn Creek     69.9     1087. 0.00843     2013    34.8     21.5 #> 10 23519461 Cape Horn Creek     69.9     1087. 0.00843     2011    34.8     21.5 #> # ℹ 644 more rows #> # ℹ 13 more variables: AirMWMTc <dbl>, NEAR_X <dbl>, NEAR_Y <dbl>, rid <int>, #> #   ratio <dbl>, afvArea <chr>, upDist <dbl>, locID <int>, netID <int>, #> #   pid <int>, netgeom <chr>, .fitted <dbl>, geometry <POINT [m]>"},{"path":"/reference/coef.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted model coefficients — coef.SSN2","title":"Extract fitted model coefficients — coef.SSN2","text":"coef extracts fitted model coefficients fitted model objects. coefficients alias .","code":""},{"path":"/reference/coef.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted model coefficients — coef.SSN2","text":"","code":"# S3 method for ssn_lm coef(object, type = \"fixed\", ...)  # S3 method for ssn_lm coefficients(object, type = \"fixed\", ...)  # S3 method for ssn_glm coef(object, type = \"fixed\", ...)  # S3 method for ssn_glm coefficients(object, type = \"fixed\", ...)"},{"path":"/reference/coef.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted model coefficients — coef.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). type \"fixed\" fixed effect coefficients, \"tailup\" tailup covariance parameter coefficients, \"taildown\" taildown covariance parameter coefficients, \"euclid\" Euclidean covariance parameter coefficients, \"nugget\" nugget covariance parameter coefficients, \"dispersion\" dispersion parameter coefficient (ssn_glm() objects), \"randcov\" random effect variance coefficients, \"ssn\" tailup, taildown, Euclidean, nugget, dispersion (ssn_glm() objects) parameter coefficients. Defaults \"fixed\". ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/coef.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract fitted model coefficients — coef.SSN2","text":"named vector coefficients.","code":""},{"path":"/reference/coef.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted model coefficients — coef.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) coef(ssn_mod) #> (Intercept)    ELEV_DEM  #> 71.32316197 -0.02882225  coef(ssn_mod, type = \"tailup\") #>           de        range  #> 4.415469e+00 1.174539e+06  #> attr(,\"class\") #> [1] \"tailup_exponential\" coefficients(ssn_mod) #> (Intercept)    ELEV_DEM  #> 71.32316197 -0.02882225"},{"path":"/reference/confint.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for fitted model parameters — confint.SSN2","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"Computes confidence intervals one parameters fitted model object.","code":""},{"path":"/reference/confint.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"","code":"# S3 method for ssn_lm confint(object, parm, level = 0.95, ...)  # S3 method for ssn_glm confint(object, parm, level = 0.95, ...)"},{"path":"/reference/confint.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). parm specification parameters given confidence intervals (character vector names). missing, parameters considered. level confidence level required. default 0.95. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/confint.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"Gaussian-based confidence intervals (two-sided equal-tailed) fixed effect coefficients based confidence level specified level. ssn_glm() objects, confidence intervals link scale.","code":""},{"path":"/reference/confint.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) confint(ssn_mod) #>                   2.5 %      97.5 % #> (Intercept) 59.18500695 83.46131699 #> ELEV_DEM    -0.03485002 -0.02279448 confint(ssn_mod, level = 0.9) #>                     5 %        95 % #> (Intercept) 61.13650116 81.50982278 #> ELEV_DEM    -0.03388091 -0.02376358"},{"path":"/reference/cooks.distance.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cook's distance — cooks.distance.SSN2","title":"Compute Cook's distance — cooks.distance.SSN2","text":"Compute Cook's distance observation fitted model object.","code":""},{"path":"/reference/cooks.distance.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cook's distance — cooks.distance.SSN2","text":"","code":"# S3 method for ssn_lm cooks.distance(model, ...)  # S3 method for ssn_glm cooks.distance(model, ...)"},{"path":"/reference/cooks.distance.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cook's distance — cooks.distance.SSN2","text":"model fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/cooks.distance.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Cook's distance — cooks.distance.SSN2","text":"vector Cook's distance values observation fitted model object.","code":""},{"path":"/reference/cooks.distance.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Cook's distance — cooks.distance.SSN2","text":"Cook's distance measures influence observation fitted model object. observation influential, omission data noticeably impacts parameter estimates. larger Cook's distance, larger influence.","code":""},{"path":[]},{"path":"/reference/cooks.distance.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Cook's distance — cooks.distance.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) cooks.distance(ssn_mod) #>            1            2            3            4            5            6  #> 1.352055e-01 1.838183e-02 1.031477e-02 2.394619e-04 6.738905e-06 2.853609e-03  #>            7            8            9           10           11           12  #> 1.444345e-05 7.407903e-05 8.913727e-04 2.902180e-02 7.151814e-02 1.478130e-01  #>           13           14           15           16           17           18  #> 1.325513e-03 8.944452e-02 2.518628e-02 2.855456e-02 4.559303e-02 3.189087e-02  #>           19           20           21           22           23           24  #> 3.236705e-03 1.820146e-03 1.613470e-04 2.607051e-04 5.023190e-04 9.990331e-02  #>           25           26           27           28           29           30  #> 1.362150e-03 1.769408e-03 2.791224e-02 2.507513e-03 7.423961e-03 5.168470e-02  #>           31           32           33           34           35           36  #> 3.062191e-04 3.654006e-04 1.628686e-03 2.898565e-03 1.940961e-04 8.450436e-03  #>           37           38           39           40           41           42  #> 5.261079e-05 7.816457e-05 6.103392e-05 9.366436e-03 1.201142e-02 1.870026e-03  #>           43           44           45  #> 5.228889e-03 8.723253e-03 2.611896e-03"},{"path":"/reference/copy_lsn_to_temp.html","id":null,"dir":"Reference","previous_headings":"","what":"Copy LSN to temporary directory — copy_lsn_to_temp","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"Copies LSN directory MiddleFork04.ssn R's temporary directory examples SSN2 write local library places.","code":""},{"path":"/reference/copy_lsn_to_temp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"","code":"copy_lsn_to_temp()"},{"path":"/reference/copy_lsn_to_temp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"copy MiddleFork04.ssn residing R's temporary directory","code":""},{"path":"/reference/copy_lsn_to_temp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"Copies LSN directory MiddleFork04.ssn R's temporary directory","code":""},{"path":"/reference/copy_lsn_to_temp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"","code":"copy_lsn_to_temp() # getwd() # setwd(tempdir()) # getwd() # if unix-alike, list temporary directory contents using: system('ls') # if windows, list temporary directory contents using: shell('dir')"},{"path":"/reference/covmatrix.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a covariance matrix — covmatrix.SSN2","title":"Create a covariance matrix — covmatrix.SSN2","text":"Create covariance matrix fitted model object.","code":""},{"path":"/reference/covmatrix.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a covariance matrix — covmatrix.SSN2","text":"","code":"# S3 method for ssn_lm covmatrix(object, newdata, cov_type, ...)  # S3 method for ssn_glm covmatrix(object, newdata, cov_type, ...)"},{"path":"/reference/covmatrix.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a covariance matrix — covmatrix.SSN2","text":"object fitted model object (e.g., ssn_lm() ssn_glm()). newdata omitted, covariance matrix observed data returned. provided, newdata data frame sf object contains coordinate information required construct covariance newdata observed data. data frame, newdata must contain variables represent coordinates name coordinates observed data used fit object. sf object, coordinates obtained geometry newdata. cov_type type covariance matrix returned. newdata omitted, \\(n \\times n\\) covariance matrix observed data returned, \\(n\\) sample size used fit object. newdata provided cov_type \"pred.obs\" (default), \\(m \\times n\\) covariance matrix predicted observed data returned, \\(m\\) number observations prediction data. newdata provided cov_type \"obs.pred\", \\(n \\times m\\) covariance matrix observed prediction data returned. newdata provided cov_type \"pred.pred\", \\(m \\times m\\) covariance matrix prediction data returned. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/covmatrix.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a covariance matrix — covmatrix.SSN2","text":"covariance matrix (see cov_type).","code":""},{"path":"/reference/covmatrix.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a covariance matrix — covmatrix.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"CapeHorn\", overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) covmatrix(ssn_mod) #>           [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8] #>  [1,] 4.441093 2.171225 2.169448 2.167548 2.166820 2.159888 2.152978 2.152538 #>  [2,] 2.171225 4.441093 4.411857 4.407992 4.406512 4.392414 4.378361 4.377466 #>  [3,] 2.169448 4.411857 4.441093 4.411602 4.410120 4.396010 4.381946 4.381051 #>  [4,] 2.167548 4.407992 4.411602 4.441093 4.413986 4.399864 4.385788 4.384891 #>  [5,] 2.166820 4.406512 4.410120 4.413986 4.441093 4.401342 4.387261 4.386364 #>  [6,] 2.159888 4.392414 4.396010 4.399864 4.401342 4.441093 4.401343 4.400443 #>  [7,] 2.152978 4.378361 4.381946 4.385788 4.387261 4.401343 4.441093 4.414567 #>  [8,] 2.152538 4.377466 4.381051 4.384891 4.386364 4.400443 4.414567 4.441093 #>  [9,] 2.118957 4.309175 4.312703 4.316484 4.317934 4.331794 4.345697 4.346585 #> [10,] 3.841226 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [11,] 3.678119 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [12,] 3.086145 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [13,] 3.077317 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [14,] 3.073538 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [15,] 3.068528 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [16,] 3.004544 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [17,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [18,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [19,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [20,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [21,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [22,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [23,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [24,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [25,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [26,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [27,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [28,] 1.997808 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [29,] 1.909344 3.882899 3.886079 3.889486 3.890792 3.903281 3.915809 3.916609 #> [30,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [31,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [32,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [33,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [34,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [35,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [36,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [37,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [38,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [39,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [40,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [41,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [42,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [43,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [44,] 3.085052 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [45,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>           [,9]    [,10]    [,11]    [,12]    [,13]    [,14]    [,15]    [,16] #>  [1,] 2.118957 3.841226 3.678119 3.086145 3.077317 3.073538 3.068528 3.004544 #>  [2,] 4.309175 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [3,] 4.312703 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [4,] 4.316484 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [5,] 4.317934 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [6,] 4.331794 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [7,] 4.345697 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [8,] 4.346585 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [9,] 4.441093 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [10,] 0.000000 4.441093 4.227979 3.547507 3.537360 3.533016 3.527257 3.453708 #> [11,] 0.000000 4.227979 4.441093 3.704823 3.694225 3.689688 3.683674 3.606863 #> [12,] 0.000000 3.547507 3.704823 4.441093 4.402839 4.397432 4.390264 4.298720 #> [13,] 0.000000 3.537360 3.694225 4.402839 4.441093 4.410047 4.402859 4.311052 #> [14,] 0.000000 3.533016 3.689688 4.397432 4.410047 4.441093 4.408272 4.316353 #> [15,] 0.000000 3.527257 3.683674 4.390264 4.402859 4.408272 4.441093 4.323400 #> [16,] 0.000000 3.453708 3.606863 4.298720 4.311052 4.316353 4.323400 4.441093 #> [17,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [18,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [19,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [20,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [21,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [22,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [23,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [24,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [25,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [26,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [27,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [28,] 0.000000 2.296470 2.398307 0.000000 0.000000 0.000000 0.000000 0.000000 #> [29,] 3.978679 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [30,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [31,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [32,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [33,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [34,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [35,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [36,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [37,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [38,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [39,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [40,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [41,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [42,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [43,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [44,] 0.000000 3.546251 3.703510 4.413905 4.404399 4.398991 4.391820 4.300244 #> [45,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>           [,17]     [,18]    [,19]     [,20]     [,21]     [,22]     [,23] #>  [1,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [2,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [3,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [4,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [5,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [6,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [7,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [8,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [9,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [10,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [11,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [12,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [13,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [14,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [15,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [16,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [17,] 4.4410930 4.4130628 4.399162 4.3838619 4.3864570 4.3403396 4.2928585 #> [18,] 4.4130628 4.4410930 4.401561 4.3862525 4.3888490 4.3427065 4.2951995 #> [19,] 4.3991620 4.4015609 4.441093 4.4001126 4.4027173 4.3564290 4.3087719 #> [20,] 4.3838619 4.3862525 4.400113 4.4410930 4.4128571 4.3716333 4.3238099 #> [21,] 4.3864570 4.3888490 4.402717 4.4128571 4.4410930 4.3690470 4.3212518 #> [22,] 4.3403396 4.3427065 4.356429 4.3716333 4.3690470 4.4410930 4.3671664 #> [23,] 4.2928585 4.2951995 4.308772 4.3238099 4.3212518 4.3671664 4.4410930 #> [24,] 4.0176297 4.0198206 4.032523 4.0465967 4.0442026 4.0871734 4.1323796 #> [25,] 3.9440371 3.9461878 3.958657 3.9724734 3.9701232 4.0123069 4.0566850 #> [26,] 3.9427913 3.9449414 3.957407 3.9712187 3.9688693 4.0110396 4.0554037 #> [27,] 3.9185684 3.9207053 3.933094 3.9468211 3.9444861 3.9863974 4.0304889 #> [28,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [29,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [30,] 0.7454686 0.7458751 0.748232 0.7508434 0.7503992 0.7583724 0.7667604 #> [31,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [32,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [33,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [34,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [35,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [36,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [37,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [38,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [39,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [40,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [41,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [42,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [43,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [44,] 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [45,] 2.9349643 2.9365648 2.945844 2.9561253 2.9543764 2.9857675 3.0187915 #>           [,24]    [,25]    [,26]    [,27]    [,28]    [,29]     [,30]    [,31] #>  [1,] 0.0000000 0.000000 0.000000 0.000000 1.997808 1.909344 0.0000000 0.000000 #>  [2,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.882899 0.0000000 0.000000 #>  [3,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.886079 0.0000000 0.000000 #>  [4,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.889486 0.0000000 0.000000 #>  [5,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.890792 0.0000000 0.000000 #>  [6,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.903281 0.0000000 0.000000 #>  [7,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.915809 0.0000000 0.000000 #>  [8,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.916609 0.0000000 0.000000 #>  [9,] 0.0000000 0.000000 0.000000 0.000000 0.000000 3.978679 0.0000000 0.000000 #> [10,] 0.0000000 0.000000 0.000000 0.000000 2.296470 0.000000 0.0000000 0.000000 #> [11,] 0.0000000 0.000000 0.000000 0.000000 2.398307 0.000000 0.0000000 0.000000 #> [12,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [13,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [14,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [15,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [16,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [17,] 4.0176297 3.944037 3.942791 3.918568 0.000000 0.000000 0.7454686 0.000000 #> [18,] 4.0198206 3.946188 3.944941 3.920705 0.000000 0.000000 0.7458751 0.000000 #> [19,] 4.0325228 3.958657 3.957407 3.933094 0.000000 0.000000 0.7482320 0.000000 #> [20,] 4.0465967 3.972473 3.971219 3.946821 0.000000 0.000000 0.7508434 0.000000 #> [21,] 4.0442026 3.970123 3.968869 3.944486 0.000000 0.000000 0.7503992 0.000000 #> [22,] 4.0871734 4.012307 4.011040 3.986397 0.000000 0.000000 0.7583724 0.000000 #> [23,] 4.1323796 4.056685 4.055404 4.030489 0.000000 0.000000 0.7667604 0.000000 #> [24,] 4.4410930 4.334589 4.333220 4.306599 0.000000 0.000000 0.8192875 0.000000 #> [25,] 4.3345893 4.441093 4.414075 4.386956 0.000000 0.000000 0.0000000 0.000000 #> [26,] 4.3332202 4.414075 4.441093 4.388342 0.000000 0.000000 0.0000000 0.000000 #> [27,] 4.3065986 4.386956 4.388342 4.441093 0.000000 0.000000 0.0000000 0.000000 #> [28,] 0.0000000 0.000000 0.000000 0.000000 4.441093 0.000000 0.0000000 0.000000 #> [29,] 0.0000000 0.000000 0.000000 0.000000 0.000000 4.441093 0.0000000 0.000000 #> [30,] 0.8192875 0.000000 0.000000 0.000000 0.000000 0.000000 4.4410930 0.000000 #> [31,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 4.441093 #> [32,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 4.406113 #> [33,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 4.413483 #> [34,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 2.682767 #> [35,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 2.684554 #> [36,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 2.740888 #> [37,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 2.747844 #> [38,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 2.756931 #> [39,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [40,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [41,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [42,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [43,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [44,] 0.0000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 #> [45,] 3.2255947 3.285782 3.286820 3.307138 0.000000 0.000000 0.0000000 0.000000 #>          [,32]    [,33]    [,34]    [,35]    [,36]    [,37]    [,38]    [,39] #>  [1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [2,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [3,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [4,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [5,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [6,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [7,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [8,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [9,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [10,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [11,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [12,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [13,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [14,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [15,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [16,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [17,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [18,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [19,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [20,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [21,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [22,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [23,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [24,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [25,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [26,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [27,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [28,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [29,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [30,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [31,] 4.406113 4.413483 2.682767 2.684554 2.740888 2.747844 2.756931 0.000000 #> [32,] 4.441093 4.408096 2.688463 2.690255 2.746708 2.753679 2.762786 0.000000 #> [33,] 4.408096 4.441093 2.683974 2.685762 2.742122 2.749080 2.758172 0.000000 #> [34,] 2.688463 2.683974 4.441093 4.412529 4.321838 4.310898 4.296688 3.349573 #> [35,] 2.690255 2.685762 4.412529 4.441093 4.324718 4.313770 4.299551 3.351805 #> [36,] 2.746708 2.742122 4.321838 4.324718 4.441093 4.404292 4.389775 3.422141 #> [37,] 2.753679 2.749080 4.310898 4.313770 4.404292 4.441093 4.400915 3.430825 #> [38,] 2.762786 2.758172 4.296688 4.299551 4.389775 4.400915 4.441093 3.442172 #> [39,] 0.000000 0.000000 3.349573 3.351805 3.422141 3.430825 3.442172 4.441093 #> [40,] 0.000000 0.000000 3.348840 3.351072 3.421392 3.430075 3.441418 4.414503 #> [41,] 0.000000 0.000000 3.319002 3.321214 3.390908 3.399513 3.410756 4.375170 #> [42,] 0.000000 0.000000 3.308258 3.310462 3.379930 3.388508 3.399714 4.361007 #> [43,] 0.000000 0.000000 3.305658 3.307861 3.377274 3.385845 3.397043 4.357580 #> [44,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [45,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>          [,40]    [,41]    [,42]    [,43]    [,44]    [,45] #>  [1,] 0.000000 0.000000 0.000000 0.000000 3.085052 0.000000 #>  [2,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [3,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [4,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [5,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [6,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [7,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [8,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #>  [9,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [10,] 0.000000 0.000000 0.000000 0.000000 3.546251 0.000000 #> [11,] 0.000000 0.000000 0.000000 0.000000 3.703510 0.000000 #> [12,] 0.000000 0.000000 0.000000 0.000000 4.413905 0.000000 #> [13,] 0.000000 0.000000 0.000000 0.000000 4.404399 0.000000 #> [14,] 0.000000 0.000000 0.000000 0.000000 4.398991 0.000000 #> [15,] 0.000000 0.000000 0.000000 0.000000 4.391820 0.000000 #> [16,] 0.000000 0.000000 0.000000 0.000000 4.300244 0.000000 #> [17,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.934964 #> [18,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.936565 #> [19,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.945844 #> [20,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.956125 #> [21,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.954376 #> [22,] 0.000000 0.000000 0.000000 0.000000 0.000000 2.985768 #> [23,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.018792 #> [24,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.225595 #> [25,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.285782 #> [26,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.286820 #> [27,] 0.000000 0.000000 0.000000 0.000000 0.000000 3.307138 #> [28,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [29,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [30,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [31,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [32,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [33,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 #> [34,] 3.348840 3.319002 3.308258 3.305658 0.000000 0.000000 #> [35,] 3.351072 3.321214 3.310462 3.307861 0.000000 0.000000 #> [36,] 3.421392 3.390908 3.379930 3.377274 0.000000 0.000000 #> [37,] 3.430075 3.399513 3.388508 3.385845 0.000000 0.000000 #> [38,] 3.441418 3.410756 3.399714 3.397043 0.000000 0.000000 #> [39,] 4.414503 4.375170 4.361007 4.357580 0.000000 0.000000 #> [40,] 4.441093 4.376128 4.361961 4.358533 0.000000 0.000000 #> [41,] 4.376128 4.441093 4.401176 4.397717 0.000000 0.000000 #> [42,] 4.361961 4.401176 4.441093 4.411999 0.000000 0.000000 #> [43,] 4.358533 4.397717 4.411999 4.441093 0.000000 0.000000 #> [44,] 0.000000 0.000000 0.000000 0.000000 4.441093 0.000000 #> [45,] 0.000000 0.000000 0.000000 0.000000 0.000000 4.441093 covmatrix(ssn_mod, \"CapeHorn\") #>            [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8] #>   [1,] 2.165766 4.404367 4.407973 4.411838 4.413320 4.403486 4.389398 4.388501 #>   [2,] 2.165784 4.404404 4.408011 4.411875 4.413357 4.403448 4.389361 4.388463 #>   [3,] 2.165802 4.404442 4.408049 4.411913 4.413395 4.403411 4.389323 4.388426 #>   [4,] 2.165821 4.404479 4.408086 4.411950 4.413433 4.403373 4.389286 4.388389 #>   [5,] 2.165839 4.404517 4.408124 4.411988 4.413470 4.403336 4.389248 4.388351 #>   [6,] 2.165858 4.404554 4.408161 4.412025 4.413508 4.403299 4.389211 4.388314 #>   [7,] 2.165876 4.404592 4.408199 4.412063 4.413545 4.403261 4.389174 4.388277 #>   [8,] 2.165895 4.404629 4.408236 4.412101 4.413583 4.403224 4.389136 4.388239 #>   [9,] 2.165913 4.404667 4.408274 4.412138 4.413620 4.403186 4.389099 4.388202 #>  [10,] 2.165931 4.404704 4.408311 4.412176 4.413658 4.403149 4.389062 4.388164 #>  [11,] 2.165950 4.404742 4.408349 4.412213 4.413696 4.403111 4.389024 4.388127 #>  [12,] 2.165968 4.404779 4.408386 4.412251 4.413733 4.403074 4.388987 4.388090 #>  [13,] 2.165987 4.404817 4.408424 4.412288 4.413771 4.403036 4.388949 4.388052 #>  [14,] 2.166005 4.404854 4.408461 4.412326 4.413808 4.402999 4.388912 4.388015 #>  [15,] 2.166024 4.404892 4.408499 4.412363 4.413846 4.402961 4.388875 4.387978 #>  [16,] 2.166042 4.404929 4.408536 4.412401 4.413883 4.402924 4.388837 4.387940 #>  [17,] 2.166061 4.404967 4.408574 4.412439 4.413921 4.402886 4.388800 4.387903 #>  [18,] 2.166079 4.405004 4.408611 4.412476 4.413959 4.402849 4.388763 4.387866 #>  [19,] 2.166097 4.405042 4.408649 4.412514 4.413996 4.402811 4.388725 4.387828 #>  [20,] 2.166116 4.405079 4.408687 4.412551 4.414034 4.402774 4.388688 4.387791 #>  [21,] 2.166134 4.405117 4.408724 4.412589 4.414071 4.402736 4.388651 4.387753 #>  [22,] 2.166153 4.405154 4.408762 4.412626 4.414109 4.402699 4.388613 4.387716 #>  [23,] 2.166171 4.405192 4.408799 4.412664 4.414147 4.402661 4.388576 4.387679 #>  [24,] 2.166190 4.405229 4.408837 4.412702 4.414184 4.402624 4.388538 4.387641 #>  [25,] 2.166208 4.405267 4.408874 4.412739 4.414222 4.402586 4.388501 4.387604 #>  [26,] 2.166227 4.405304 4.408912 4.412777 4.414259 4.402549 4.388464 4.387567 #>  [27,] 2.166245 4.405342 4.408949 4.412814 4.414297 4.402511 4.388426 4.387529 #>  [28,] 2.166263 4.405379 4.408987 4.412852 4.414334 4.402474 4.388389 4.387492 #>  [29,] 2.166282 4.405417 4.409024 4.412889 4.414372 4.402436 4.388352 4.387455 #>  [30,] 2.166300 4.405454 4.409062 4.412927 4.414410 4.402399 4.388314 4.387417 #>  [31,] 2.166319 4.405492 4.409099 4.412965 4.414447 4.402361 4.388277 4.387380 #>  [32,] 2.166337 4.405529 4.409137 4.413002 4.414485 4.402324 4.388240 4.387343 #>  [33,] 2.166356 4.405567 4.409175 4.413040 4.414522 4.402286 4.388202 4.387305 #>  [34,] 2.166374 4.405604 4.409212 4.413077 4.414560 4.402249 4.388165 4.387268 #>  [35,] 2.166393 4.405642 4.409250 4.413115 4.414598 4.402211 4.388127 4.387231 #>  [36,] 2.166411 4.405679 4.409287 4.413152 4.414635 4.402174 4.388090 4.387193 #>  [37,] 2.166429 4.405717 4.409325 4.413190 4.414673 4.402136 4.388053 4.387156 #>  [38,] 2.166448 4.405755 4.409362 4.413228 4.414710 4.402099 4.388015 4.387118 #>  [39,] 2.166466 4.405792 4.409400 4.413265 4.414748 4.402062 4.387978 4.387081 #>  [40,] 2.166485 4.405830 4.409437 4.413303 4.414785 4.402024 4.387941 4.387044 #>  [41,] 2.166503 4.405867 4.409475 4.413340 4.414823 4.401987 4.387903 4.387006 #>  [42,] 2.166522 4.405905 4.409512 4.413378 4.414861 4.401949 4.387866 4.386969 #>  [43,] 2.166540 4.405942 4.409550 4.413415 4.414898 4.401912 4.387829 4.386932 #>  [44,] 2.166559 4.405980 4.409588 4.413453 4.414936 4.401874 4.387791 4.386894 #>  [45,] 2.166577 4.406017 4.409625 4.413491 4.414973 4.401837 4.387754 4.386857 #>  [46,] 2.166595 4.406055 4.409663 4.413528 4.415011 4.401799 4.387717 4.386820 #>  [47,] 2.166614 4.406092 4.409700 4.413566 4.415049 4.401762 4.387679 4.386782 #>  [48,] 2.166632 4.406130 4.409738 4.413603 4.415086 4.401724 4.387642 4.386745 #>  [49,] 2.166651 4.406167 4.409775 4.413641 4.415124 4.401687 4.387604 4.386708 #>  [50,] 2.166669 4.406205 4.409813 4.413679 4.415161 4.401649 4.387567 4.386670 #>  [51,] 2.166688 4.406242 4.409850 4.413716 4.415199 4.401612 4.387530 4.386633 #>  [52,] 2.166706 4.406280 4.409888 4.413754 4.415237 4.401574 4.387492 4.386596 #>  [53,] 2.166725 4.406317 4.409925 4.413791 4.415274 4.401537 4.387455 4.386558 #>  [54,] 2.166743 4.406355 4.409963 4.413829 4.415312 4.401499 4.387418 4.386521 #>  [55,] 2.166761 4.406392 4.410001 4.413866 4.415349 4.401462 4.387380 4.386484 #>  [56,] 2.166780 4.406430 4.410038 4.413904 4.415387 4.401424 4.387343 4.386446 #>  [57,] 2.166798 4.406467 4.410076 4.413942 4.415425 4.401387 4.387306 4.386409 #>  [58,] 2.166817 4.406505 4.410113 4.413979 4.415462 4.401349 4.387268 4.386371 #>  [59,] 2.166835 4.406542 4.410151 4.414017 4.415439 4.401312 4.387231 4.386334 #>  [60,] 2.166854 4.406580 4.410188 4.414054 4.415401 4.401275 4.387194 4.386297 #>  [61,] 2.166872 4.406617 4.410226 4.414092 4.415364 4.401237 4.387156 4.386259 #>  [62,] 2.166891 4.406655 4.410263 4.414129 4.415326 4.401200 4.387119 4.386222 #>  [63,] 2.166909 4.406692 4.410301 4.414167 4.415289 4.401162 4.387082 4.386185 #>  [64,] 2.166927 4.406730 4.410338 4.414205 4.415251 4.401125 4.387044 4.386147 #>  [65,] 2.166946 4.406767 4.410376 4.414242 4.415213 4.401087 4.387007 4.386110 #>  [66,] 2.166964 4.406805 4.410414 4.414280 4.415176 4.401050 4.386969 4.386073 #>  [67,] 2.166983 4.406842 4.410451 4.414317 4.415138 4.401012 4.386932 4.386035 #>  [68,] 2.167001 4.406880 4.410489 4.414355 4.415101 4.400975 4.386895 4.385998 #>  [69,] 2.167020 4.406917 4.410526 4.414393 4.415063 4.400937 4.386857 4.385961 #>  [70,] 2.167038 4.406955 4.410564 4.414430 4.415026 4.400900 4.386820 4.385923 #>  [71,] 2.167057 4.406993 4.410601 4.414468 4.414988 4.400862 4.386783 4.385886 #>  [72,] 2.167075 4.407030 4.410639 4.414505 4.414950 4.400825 4.386745 4.385849 #>  [73,] 2.167094 4.407068 4.410676 4.414543 4.414913 4.400787 4.386708 4.385811 #>  [74,] 2.167112 4.407105 4.410714 4.414580 4.414875 4.400750 4.386671 4.385774 #>  [75,] 2.167130 4.407143 4.410752 4.414618 4.414838 4.400712 4.386633 4.385737 #>  [76,] 2.167149 4.407180 4.410789 4.414656 4.414800 4.400675 4.386596 4.385699 #>  [77,] 2.167167 4.407218 4.410827 4.414693 4.414762 4.400638 4.386559 4.385662 #>  [78,] 2.167186 4.407255 4.410864 4.414731 4.414725 4.400600 4.386521 4.385625 #>  [79,] 2.167204 4.407293 4.410902 4.414768 4.414687 4.400563 4.386484 4.385587 #>  [80,] 2.167223 4.407330 4.410939 4.414806 4.414650 4.400525 4.386447 4.385550 #>  [81,] 2.167241 4.407368 4.410977 4.414844 4.414612 4.400488 4.386409 4.385513 #>  [82,] 2.167260 4.407405 4.411014 4.414881 4.414574 4.400450 4.386372 4.385475 #>  [83,] 2.167278 4.407443 4.411052 4.414919 4.414537 4.400413 4.386335 4.385438 #>  [84,] 2.167297 4.407480 4.411089 4.414956 4.414499 4.400375 4.386297 4.385401 #>  [85,] 2.167315 4.407518 4.411127 4.414994 4.414462 4.400338 4.386260 4.385363 #>  [86,] 2.167333 4.407555 4.411165 4.415032 4.414424 4.400300 4.386223 4.385326 #>  [87,] 2.167352 4.407593 4.411202 4.415069 4.414387 4.400263 4.386185 4.385289 #>  [88,] 2.167370 4.407630 4.411240 4.415107 4.414349 4.400225 4.386148 4.385251 #>  [89,] 2.167389 4.407668 4.411277 4.415144 4.414311 4.400188 4.386110 4.385214 #>  [90,] 2.167407 4.407705 4.411315 4.415182 4.414274 4.400151 4.386073 4.385177 #>  [91,] 2.167426 4.407743 4.411352 4.415219 4.414236 4.400113 4.386036 4.385139 #>  [92,] 2.167444 4.407781 4.411390 4.415257 4.414199 4.400076 4.385998 4.385102 #>  [93,] 2.167463 4.407818 4.411428 4.415295 4.414161 4.400038 4.385961 4.385065 #>  [94,] 2.167481 4.407856 4.411465 4.415332 4.414123 4.400001 4.385924 4.385027 #>  [95,] 2.167499 4.407893 4.411503 4.415370 4.414086 4.399963 4.385886 4.384990 #>  [96,] 2.167518 4.407931 4.411540 4.415407 4.414048 4.399926 4.385849 4.384953 #>  [97,] 2.167536 4.407968 4.411578 4.415445 4.414011 4.399888 4.385812 4.384915 #>  [98,] 2.167555 4.408006 4.411615 4.415456 4.413973 4.399851 4.385774 4.384878 #>  [99,] 2.167573 4.408043 4.411653 4.415418 4.413936 4.399813 4.385737 4.384841 #> [100,] 2.167592 4.408081 4.411690 4.415381 4.413898 4.399776 4.385700 4.384803 #> [101,] 2.167610 4.408118 4.411728 4.415343 4.413860 4.399738 4.385662 4.384766 #> [102,] 2.167629 4.408156 4.411766 4.415306 4.413823 4.399701 4.385625 4.384729 #> [103,] 2.167647 4.408193 4.411803 4.415268 4.413785 4.399664 4.385588 4.384691 #> [104,] 2.167666 4.408231 4.411841 4.415231 4.413748 4.399626 4.385550 4.384654 #> [105,] 2.167684 4.408268 4.411878 4.415193 4.413710 4.399589 4.385513 4.384617 #> [106,] 2.167702 4.408306 4.411916 4.415155 4.413672 4.399551 4.385476 4.384579 #> [107,] 2.167721 4.408343 4.411953 4.415118 4.413635 4.399514 4.385438 4.384542 #> [108,] 2.167739 4.408381 4.411991 4.415080 4.413597 4.399476 4.385401 4.384505 #> [109,] 2.167758 4.408419 4.412028 4.415043 4.413560 4.399439 4.385364 4.384467 #> [110,] 2.167776 4.408456 4.412066 4.415005 4.413522 4.399401 4.385326 4.384430 #> [111,] 2.167795 4.408494 4.412104 4.414967 4.413485 4.399364 4.385289 4.384393 #> [112,] 2.167813 4.408531 4.412141 4.414930 4.413447 4.399326 4.385252 4.384355 #> [113,] 2.167832 4.408569 4.412179 4.414892 4.413409 4.399289 4.385214 4.384318 #> [114,] 2.167850 4.408606 4.412216 4.414855 4.413372 4.399251 4.385177 4.384281 #> [115,] 2.167869 4.408644 4.412254 4.414817 4.413334 4.399214 4.385140 4.384243 #> [116,] 2.167887 4.408681 4.412291 4.414779 4.413297 4.399177 4.385102 4.384206 #> [117,] 2.167906 4.408719 4.412329 4.414742 4.413259 4.399139 4.385065 4.384169 #> [118,] 2.167924 4.408756 4.412367 4.414704 4.413222 4.399102 4.385028 4.384131 #> [119,] 2.167942 4.408794 4.412404 4.414667 4.413184 4.399064 4.384990 4.384094 #> [120,] 2.167961 4.408831 4.412442 4.414629 4.413146 4.399027 4.384953 4.384057 #> [121,] 2.167979 4.408869 4.412479 4.414592 4.413109 4.398989 4.384916 4.384019 #> [122,] 2.167998 4.408907 4.412517 4.414554 4.413071 4.398952 4.384878 4.383982 #> [123,] 2.168016 4.408944 4.412554 4.414516 4.413034 4.398914 4.384841 4.383945 #> [124,] 2.168035 4.408982 4.412592 4.414479 4.412996 4.398877 4.384804 4.383907 #> [125,] 2.168053 4.409019 4.412630 4.414441 4.412959 4.398840 4.384766 4.383870 #> [126,] 2.168072 4.409057 4.412667 4.414404 4.412921 4.398802 4.384729 4.383833 #> [127,] 2.168090 4.409094 4.412705 4.414366 4.412883 4.398765 4.384692 4.383795 #> [128,] 2.168109 4.409132 4.412742 4.414328 4.412846 4.398727 4.384654 4.383758 #> [129,] 2.168127 4.409169 4.412780 4.414291 4.412808 4.398690 4.384617 4.383721 #> [130,] 2.168145 4.409207 4.412817 4.414253 4.412771 4.398652 4.384580 4.383683 #> [131,] 2.168164 4.409244 4.412855 4.414216 4.412733 4.398615 4.384542 4.383646 #> [132,] 2.168182 4.409282 4.412893 4.414178 4.412696 4.398577 4.384505 4.383609 #> [133,] 2.168201 4.409319 4.412930 4.414141 4.412658 4.398540 4.384468 4.383571 #> [134,] 2.168219 4.409357 4.412968 4.414103 4.412620 4.398502 4.384430 4.383534 #> [135,] 2.168238 4.409395 4.413005 4.414065 4.412583 4.398465 4.384393 4.383497 #> [136,] 2.168256 4.409432 4.413043 4.414028 4.412545 4.398428 4.384356 4.383460 #> [137,] 2.168275 4.409470 4.413080 4.413990 4.412508 4.398390 4.384318 4.383422 #> [138,] 2.168293 4.409507 4.413118 4.413953 4.412470 4.398353 4.384281 4.383385 #> [139,] 2.168312 4.409545 4.413156 4.413915 4.412433 4.398315 4.384244 4.383348 #> [140,] 2.168330 4.409582 4.413193 4.413877 4.412395 4.398278 4.384206 4.383310 #> [141,] 2.168349 4.409620 4.413231 4.413840 4.412357 4.398240 4.384169 4.383273 #> [142,] 2.168367 4.409657 4.413268 4.413802 4.412320 4.398203 4.384132 4.383236 #> [143,] 2.168385 4.409695 4.413306 4.413765 4.412282 4.398165 4.384094 4.383198 #> [144,] 2.168404 4.409732 4.413343 4.413727 4.412245 4.398128 4.384057 4.383161 #> [145,] 2.168422 4.409770 4.413381 4.413690 4.412207 4.398091 4.384020 4.383124 #> [146,] 2.168441 4.409808 4.413419 4.413652 4.412170 4.398053 4.383982 4.383086 #> [147,] 2.168459 4.409845 4.413456 4.413614 4.412132 4.398016 4.383945 4.383049 #> [148,] 2.168478 4.409883 4.413494 4.413577 4.412095 4.397978 4.383908 4.383012 #> [149,] 2.168496 4.409920 4.413531 4.413539 4.412057 4.397941 4.383870 4.382974 #> [150,] 2.168515 4.409958 4.413569 4.413502 4.412019 4.397903 4.383833 4.382937 #> [151,] 2.168533 4.409995 4.413606 4.413464 4.411982 4.397866 4.383796 4.382900 #> [152,] 2.168552 4.410033 4.413644 4.413427 4.411944 4.397828 4.383758 4.382862 #> [153,] 2.168570 4.410070 4.413682 4.413389 4.411907 4.397791 4.383721 4.382825 #> [154,] 2.168589 4.410108 4.413719 4.413351 4.411869 4.397754 4.383684 4.382788 #> [155,] 2.168607 4.410145 4.413757 4.413314 4.411832 4.397716 4.383647 4.382750 #> [156,] 2.168625 4.410183 4.413794 4.413276 4.411794 4.397679 4.383609 4.382713 #> [157,] 2.168644 4.410221 4.413832 4.413239 4.411756 4.397641 4.383572 4.382676 #> [158,] 2.168662 4.410258 4.413870 4.413201 4.411719 4.397604 4.383535 4.382639 #> [159,] 2.168681 4.410296 4.413907 4.413164 4.411681 4.397566 4.383497 4.382601 #> [160,] 2.168699 4.410333 4.413945 4.413126 4.411644 4.397529 4.383460 4.382564 #> [161,] 2.168718 4.410371 4.413982 4.413088 4.411606 4.397491 4.383423 4.382527 #> [162,] 2.168736 4.410408 4.414020 4.413051 4.411569 4.397454 4.383385 4.382489 #> [163,] 2.168755 4.410446 4.414057 4.413013 4.411531 4.397417 4.383348 4.382452 #> [164,] 2.168773 4.410483 4.414095 4.412976 4.411494 4.397379 4.383311 4.382415 #> [165,] 2.168792 4.410521 4.414133 4.412938 4.411456 4.397342 4.383273 4.382377 #> [166,] 2.168810 4.410558 4.414170 4.412900 4.411418 4.397304 4.383236 4.382340 #> [167,] 2.168829 4.410596 4.414208 4.412863 4.411381 4.397267 4.383199 4.382303 #> [168,] 2.168847 4.410634 4.414245 4.412825 4.411343 4.397229 4.383161 4.382265 #> [169,] 2.168866 4.410671 4.414283 4.412788 4.411306 4.397192 4.383124 4.382228 #> [170,] 2.168884 4.410709 4.414320 4.412750 4.411268 4.397155 4.383087 4.382191 #> [171,] 2.168902 4.410746 4.414358 4.412713 4.411231 4.397117 4.383049 4.382153 #> [172,] 2.168921 4.410784 4.414396 4.412675 4.411193 4.397080 4.383012 4.382116 #> [173,] 2.168939 4.410821 4.414433 4.412638 4.411155 4.397042 4.382975 4.382079 #> [174,] 2.168958 4.410859 4.414471 4.412600 4.411118 4.397005 4.382937 4.382042 #> [175,] 2.168976 4.410896 4.414508 4.412562 4.411080 4.396967 4.382900 4.382004 #> [176,] 2.168995 4.410934 4.414546 4.412525 4.411043 4.396930 4.382863 4.381967 #> [177,] 2.169013 4.410972 4.414584 4.412487 4.411005 4.396892 4.382825 4.381930 #> [178,] 2.169032 4.411009 4.414621 4.412450 4.410968 4.396855 4.382788 4.381892 #> [179,] 2.169050 4.411047 4.414659 4.412412 4.410930 4.396818 4.382751 4.381855 #> [180,] 2.169069 4.411084 4.414696 4.412375 4.410893 4.396780 4.382714 4.381818 #> [181,] 2.169087 4.411122 4.414734 4.412337 4.410855 4.396743 4.382676 4.381780 #> [182,] 2.169106 4.411159 4.414772 4.412299 4.410818 4.396705 4.382639 4.381743 #> [183,] 2.169124 4.411197 4.414809 4.412262 4.410780 4.396668 4.382602 4.381706 #> [184,] 2.169143 4.411234 4.414847 4.412224 4.410742 4.396630 4.382564 4.381668 #> [185,] 2.169161 4.411272 4.414884 4.412187 4.410705 4.396593 4.382527 4.381631 #> [186,] 2.169179 4.411310 4.414922 4.412149 4.410667 4.396556 4.382490 4.381594 #> [187,] 2.169198 4.411347 4.414959 4.412112 4.410630 4.396518 4.382452 4.381557 #> [188,] 2.169216 4.411385 4.414997 4.412074 4.410592 4.396481 4.382415 4.381519 #> [189,] 2.169235 4.411422 4.415035 4.412036 4.410555 4.396443 4.382378 4.381482 #> [190,] 2.169253 4.411460 4.415072 4.411999 4.410517 4.396406 4.382340 4.381445 #> [191,] 2.169272 4.411497 4.415110 4.411961 4.410480 4.396368 4.382303 4.381407 #> [192,] 2.169290 4.411535 4.415147 4.411924 4.410442 4.396331 4.382266 4.381370 #> [193,] 2.169309 4.411572 4.415185 4.411886 4.410404 4.396294 4.382228 4.381333 #> [194,] 2.169327 4.411610 4.415223 4.411849 4.410367 4.396256 4.382191 4.381295 #> [195,] 2.169346 4.411648 4.415260 4.411811 4.410329 4.396219 4.382154 4.381258 #> [196,] 2.169364 4.411685 4.415298 4.411774 4.410292 4.396181 4.382117 4.381221 #> [197,] 2.169383 4.411723 4.415335 4.411736 4.410254 4.396144 4.382079 4.381184 #> [198,] 2.169401 4.411760 4.415373 4.411698 4.410217 4.396106 4.382042 4.381146 #> [199,] 2.169420 4.411798 4.415411 4.411661 4.410179 4.396069 4.382005 4.381109 #> [200,] 2.169438 4.411835 4.415448 4.411623 4.410142 4.396032 4.381967 4.381072 #> [201,] 2.169456 4.411873 4.415453 4.411586 4.410104 4.395994 4.381930 4.381034 #> [202,] 2.169475 4.411911 4.415415 4.411548 4.410066 4.395957 4.381893 4.380997 #> [203,] 2.169493 4.411948 4.415378 4.411511 4.410029 4.395919 4.381855 4.380960 #> [204,] 2.169512 4.411986 4.415340 4.411473 4.409991 4.395882 4.381818 4.380922 #> [205,] 2.169530 4.412023 4.415303 4.411435 4.409954 4.395844 4.381781 4.380885 #> [206,] 2.169549 4.412061 4.415265 4.411398 4.409916 4.395807 4.381743 4.380848 #> [207,] 2.169567 4.412098 4.415227 4.411360 4.409879 4.395770 4.381706 4.380811 #> [208,] 2.169586 4.412136 4.415190 4.411323 4.409841 4.395732 4.381669 4.380773 #> [209,] 2.169604 4.412173 4.415152 4.411285 4.409804 4.395695 4.381632 4.380736 #> [210,] 2.169623 4.412211 4.415115 4.411248 4.409766 4.395657 4.381594 4.380699 #> [211,] 2.169641 4.412249 4.415077 4.411210 4.409729 4.395620 4.381557 4.380661 #> [212,] 2.169660 4.412286 4.415039 4.411173 4.409691 4.395582 4.381520 4.380624 #> [213,] 2.169678 4.412324 4.415002 4.411135 4.409653 4.395545 4.381482 4.380587 #> [214,] 2.169697 4.412361 4.414964 4.411097 4.409616 4.395508 4.381445 4.380549 #> [215,] 2.169715 4.412399 4.414927 4.411060 4.409578 4.395470 4.381408 4.380512 #> [216,] 2.169734 4.412436 4.414889 4.411022 4.409541 4.395433 4.381370 4.380475 #> [217,] 2.169752 4.412474 4.414852 4.410985 4.409503 4.395395 4.381333 4.380438 #> [218,] 2.169771 4.412512 4.414814 4.410947 4.409466 4.395358 4.381296 4.380400 #> [219,] 2.169789 4.412549 4.414776 4.410910 4.409428 4.395320 4.381259 4.380363 #> [220,] 2.169807 4.412587 4.414739 4.410872 4.409391 4.395283 4.381221 4.380326 #> [221,] 2.169826 4.412624 4.414701 4.410835 4.409353 4.395246 4.381184 4.380288 #> [222,] 2.169844 4.412662 4.414664 4.410797 4.409316 4.395208 4.381147 4.380251 #> [223,] 2.169863 4.412699 4.414626 4.410759 4.409278 4.395171 4.381109 4.380214 #> [224,] 2.169881 4.412737 4.414588 4.410722 4.409241 4.395133 4.381072 4.380177 #> [225,] 2.169900 4.412775 4.414551 4.410684 4.409203 4.395096 4.381035 4.380139 #> [226,] 2.169918 4.412812 4.414513 4.410647 4.409165 4.395059 4.380997 4.380102 #> [227,] 2.169937 4.412850 4.414476 4.410609 4.409128 4.395021 4.380960 4.380065 #> [228,] 2.169955 4.412887 4.414438 4.410572 4.409090 4.394984 4.380923 4.380027 #> [229,] 2.169974 4.412925 4.414401 4.410534 4.409053 4.394946 4.380886 4.379990 #> [230,] 2.169992 4.412962 4.414363 4.410497 4.409015 4.394909 4.380848 4.379953 #> [231,] 2.170011 4.413000 4.414325 4.410459 4.408978 4.394871 4.380811 4.379915 #> [232,] 2.170029 4.413038 4.414288 4.410421 4.408940 4.394834 4.380774 4.379878 #> [233,] 2.170048 4.413075 4.414250 4.410384 4.408903 4.394797 4.380736 4.379841 #> [234,] 2.170066 4.413113 4.414213 4.410346 4.408865 4.394759 4.380699 4.379804 #> [235,] 2.170085 4.413150 4.414175 4.410309 4.408828 4.394722 4.380662 4.379766 #> [236,] 2.170103 4.413188 4.414137 4.410271 4.408790 4.394684 4.380624 4.379729 #> [237,] 2.170122 4.413225 4.414100 4.410234 4.408753 4.394647 4.380587 4.379692 #> [238,] 2.170140 4.413263 4.414062 4.410196 4.408715 4.394610 4.380550 4.379654 #> [239,] 2.170158 4.413301 4.414025 4.410159 4.408677 4.394572 4.380513 4.379617 #> [240,] 2.170177 4.413338 4.413987 4.410121 4.408640 4.394535 4.380475 4.379580 #> [241,] 2.170195 4.413376 4.413950 4.410084 4.408602 4.394497 4.380438 4.379543 #> [242,] 2.170214 4.413413 4.413912 4.410046 4.408565 4.394460 4.380401 4.379505 #> [243,] 2.170232 4.413451 4.413874 4.410008 4.408527 4.394422 4.380363 4.379468 #> [244,] 2.170251 4.413488 4.413837 4.409971 4.408490 4.394385 4.380326 4.379431 #> [245,] 2.170269 4.413526 4.413799 4.409933 4.408452 4.394348 4.380289 4.379393 #> [246,] 2.170288 4.413564 4.413762 4.409896 4.408415 4.394310 4.380252 4.379356 #> [247,] 2.170306 4.413601 4.413724 4.409858 4.408377 4.394273 4.380214 4.379319 #> [248,] 2.170325 4.413639 4.413686 4.409821 4.408340 4.394235 4.380177 4.379282 #> [249,] 2.170343 4.413676 4.413649 4.409783 4.408302 4.394198 4.380140 4.379244 #> [250,] 2.170362 4.413714 4.413611 4.409746 4.408265 4.394161 4.380102 4.379207 #> [251,] 2.170380 4.413751 4.413574 4.409708 4.408227 4.394123 4.380065 4.379170 #> [252,] 2.170399 4.413789 4.413536 4.409671 4.408190 4.394086 4.380028 4.379132 #> [253,] 2.170417 4.413827 4.413499 4.409633 4.408152 4.394048 4.379990 4.379095 #> [254,] 2.170436 4.413864 4.413461 4.409595 4.408114 4.394011 4.379953 4.379058 #> [255,] 2.170454 4.413902 4.413423 4.409558 4.408077 4.393973 4.379916 4.379021 #> [256,] 2.170473 4.413939 4.413386 4.409520 4.408039 4.393936 4.379879 4.378983 #> [257,] 2.170491 4.413977 4.413348 4.409483 4.408002 4.393899 4.379841 4.378946 #> [258,] 2.170510 4.414015 4.413311 4.409445 4.407964 4.393861 4.379804 4.378909 #> [259,] 2.170528 4.414052 4.413273 4.409408 4.407927 4.393824 4.379767 4.378871 #> [260,] 2.170547 4.414090 4.413236 4.409370 4.407889 4.393786 4.379729 4.378834 #> [261,] 2.170565 4.414127 4.413198 4.409333 4.407852 4.393749 4.379692 4.378797 #> [262,] 2.170584 4.414165 4.413160 4.409295 4.407814 4.393712 4.379655 4.378760 #> [263,] 2.170602 4.414202 4.413123 4.409258 4.407777 4.393674 4.379618 4.378722 #> [264,] 2.170620 4.414240 4.413085 4.409220 4.407739 4.393637 4.379580 4.378685 #> [265,] 2.170639 4.414278 4.413048 4.409183 4.407702 4.393599 4.379543 4.378648 #> [266,] 2.170657 4.414315 4.413010 4.409145 4.407664 4.393562 4.379506 4.378611 #> [267,] 2.170676 4.414353 4.412973 4.409107 4.407627 4.393525 4.379468 4.378573 #> [268,] 2.170694 4.414390 4.412935 4.409070 4.407589 4.393487 4.379431 4.378536 #> [269,] 2.170713 4.414428 4.412897 4.409032 4.407552 4.393450 4.379394 4.378499 #> [270,] 2.170731 4.414466 4.412860 4.408995 4.407514 4.393412 4.379357 4.378461 #> [271,] 2.170750 4.414503 4.412822 4.408957 4.407476 4.393375 4.379319 4.378424 #> [272,] 2.170768 4.414541 4.412785 4.408920 4.407439 4.393338 4.379282 4.378387 #> [273,] 2.170787 4.414578 4.412747 4.408882 4.407401 4.393300 4.379245 4.378350 #> [274,] 2.170805 4.414616 4.412710 4.408845 4.407364 4.393263 4.379207 4.378312 #> [275,] 2.170824 4.414653 4.412672 4.408807 4.407326 4.393225 4.379170 4.378275 #> [276,] 2.170842 4.414691 4.412634 4.408770 4.407289 4.393188 4.379133 4.378238 #> [277,] 2.170861 4.414729 4.412597 4.408732 4.407251 4.393151 4.379096 4.378200 #> [278,] 2.170879 4.414766 4.412559 4.408695 4.407214 4.393113 4.379058 4.378163 #> [279,] 2.170898 4.414804 4.412522 4.408657 4.407176 4.393076 4.379021 4.378126 #> [280,] 2.170916 4.414841 4.412484 4.408619 4.407139 4.393038 4.378984 4.378089 #> [281,] 2.170935 4.414879 4.412447 4.408582 4.407101 4.393001 4.378946 4.378051 #> [282,] 2.170953 4.414917 4.412409 4.408544 4.407064 4.392964 4.378909 4.378014 #> [283,] 2.170972 4.414954 4.412371 4.408507 4.407026 4.392926 4.378872 4.377977 #> [284,] 2.170990 4.414992 4.412334 4.408469 4.406989 4.392889 4.378835 4.377940 #> [285,] 2.171009 4.415029 4.412296 4.408432 4.406951 4.392851 4.378797 4.377902 #> [286,] 2.171027 4.415067 4.412259 4.408394 4.406914 4.392814 4.378760 4.377865 #> [287,] 2.171046 4.415105 4.412221 4.408357 4.406876 4.392777 4.378723 4.377828 #> [288,] 2.171064 4.415142 4.412184 4.408319 4.406839 4.392739 4.378685 4.377790 #> [289,] 2.171083 4.415180 4.412146 4.408282 4.406801 4.392702 4.378648 4.377753 #> [290,] 2.171101 4.415217 4.412108 4.408244 4.406764 4.392664 4.378611 4.377716 #> [291,] 2.171119 4.415255 4.412071 4.408207 4.406726 4.392627 4.378574 4.377679 #> [292,] 2.171138 4.415292 4.412033 4.408169 4.406689 4.392590 4.378536 4.377641 #> [293,] 2.171156 4.415330 4.411996 4.408132 4.406651 4.392552 4.378499 4.377604 #> [294,] 2.171175 4.415368 4.411958 4.408094 4.406614 4.392515 4.378462 4.377567 #> [295,] 2.171193 4.415405 4.411921 4.408056 4.406576 4.392477 4.378425 4.377530 #> [296,] 2.171212 4.415443 4.411883 4.408019 4.406538 4.392440 4.378387 4.377492 #> [297,] 2.171230 4.415458 4.411846 4.407981 4.406501 4.392403 4.378350 4.377455 #> [298,] 2.171249 4.415421 4.411808 4.407944 4.406463 4.392365 4.378313 4.377418 #> [299,] 2.171267 4.415383 4.411770 4.407906 4.406426 4.392328 4.378275 4.377380 #> [300,] 2.171286 4.415345 4.411733 4.407869 4.406388 4.392290 4.378238 4.377343 #> [301,] 2.171304 4.415308 4.411695 4.407831 4.406351 4.392253 4.378201 4.377306 #> [302,] 2.171323 4.415270 4.411658 4.407794 4.406313 4.392216 4.378164 4.377269 #> [303,] 2.171341 4.415233 4.411620 4.407756 4.406276 4.392178 4.378126 4.377231 #> [304,] 2.171360 4.415195 4.411583 4.407719 4.406238 4.392141 4.378089 4.377194 #> [305,] 2.171378 4.415158 4.411545 4.407681 4.406201 4.392103 4.378052 4.377157 #> [306,] 2.171397 4.415120 4.411507 4.407644 4.406163 4.392066 4.378014 4.377120 #> [307,] 2.171415 4.415082 4.411470 4.407606 4.406126 4.392029 4.377977 4.377082 #> [308,] 2.171434 4.415045 4.411432 4.407569 4.406088 4.391991 4.377940 4.377045 #> [309,] 2.160469 4.393596 4.397194 4.401049 4.402527 4.414281 4.400158 4.399259 #> [310,] 2.160488 4.393634 4.397232 4.401086 4.402565 4.414243 4.400121 4.399221 #> [311,] 2.160506 4.393671 4.397269 4.401124 4.402602 4.414206 4.400083 4.399184 #> [312,] 2.160524 4.393709 4.397306 4.401161 4.402640 4.414168 4.400046 4.399146 #> [313,] 2.160543 4.393746 4.397344 4.401199 4.402677 4.414130 4.400008 4.399109 #> [314,] 2.160561 4.393783 4.397381 4.401236 4.402715 4.414093 4.399971 4.399071 #> [315,] 2.160580 4.393821 4.397419 4.401274 4.402752 4.414055 4.399933 4.399034 #> [316,] 2.160598 4.393858 4.397456 4.401311 4.402790 4.414018 4.399896 4.398997 #> [317,] 2.160616 4.393896 4.397494 4.401349 4.402827 4.413980 4.399858 4.398959 #> [318,] 2.160635 4.393933 4.397531 4.401386 4.402865 4.413942 4.399821 4.398922 #> [319,] 2.160653 4.393970 4.397569 4.401424 4.402902 4.413905 4.399784 4.398884 #> [320,] 2.160672 4.394008 4.397606 4.401461 4.402940 4.413867 4.399746 4.398847 #> [321,] 2.160690 4.394045 4.397643 4.401498 4.402977 4.413830 4.399709 4.398809 #> [322,] 2.159523 4.391672 4.395268 4.399121 4.400599 4.414724 4.402086 4.401186 #> [323,] 2.159541 4.391709 4.395306 4.399159 4.400637 4.414762 4.402049 4.401149 #> [324,] 2.159560 4.391747 4.395343 4.399196 4.400674 4.414799 4.402011 4.401111 #> [325,] 2.159578 4.391784 4.395381 4.399234 4.400712 4.414837 4.401974 4.401074 #> [326,] 2.159597 4.391822 4.395418 4.399271 4.400749 4.414874 4.401936 4.401036 #> [327,] 2.159615 4.391859 4.395455 4.399309 4.400787 4.414912 4.401899 4.400999 #> [328,] 2.159633 4.391896 4.395493 4.399346 4.400824 4.414949 4.401861 4.400962 #> [329,] 2.159652 4.391934 4.395530 4.399383 4.400861 4.414987 4.401824 4.400924 #> [330,] 2.159670 4.391971 4.395568 4.399421 4.400899 4.415025 4.401786 4.400887 #> [331,] 2.159689 4.392009 4.395605 4.399458 4.400936 4.415062 4.401749 4.400849 #> [332,] 2.159707 4.392046 4.395642 4.399496 4.400974 4.415100 4.401711 4.400812 #> [333,] 2.159725 4.392083 4.395680 4.399533 4.401011 4.415137 4.401674 4.400774 #> [334,] 2.159744 4.392121 4.395717 4.399571 4.401049 4.415175 4.401636 4.400737 #> [335,] 2.159762 4.392158 4.395755 4.399608 4.401086 4.415213 4.401599 4.400699 #> [336,] 2.159780 4.392196 4.395792 4.399646 4.401124 4.415250 4.401561 4.400662 #> [337,] 2.159799 4.392233 4.395830 4.399683 4.401161 4.415288 4.401524 4.400624 #> [338,] 2.159817 4.392270 4.395867 4.399721 4.401199 4.415325 4.401487 4.400587 #> [339,] 2.159836 4.392308 4.395904 4.399758 4.401236 4.415363 4.401449 4.400549 #> [340,] 2.159854 4.392345 4.395942 4.399795 4.401274 4.415401 4.401412 4.400512 #> [341,] 2.159872 4.392383 4.395979 4.399833 4.401311 4.415438 4.401374 4.400474 #> [342,] 2.159891 4.392420 4.396017 4.399870 4.401349 4.415463 4.401337 4.400437 #> [343,] 2.159909 4.392457 4.396054 4.399908 4.401386 4.415425 4.401299 4.400400 #> [344,] 2.159928 4.392495 4.396092 4.399945 4.401424 4.415388 4.401262 4.400362 #> [345,] 2.159946 4.392532 4.396129 4.399983 4.401461 4.415350 4.401224 4.400325 #> [346,] 2.159964 4.392569 4.396166 4.400020 4.401498 4.415313 4.401187 4.400287 #> [347,] 2.159983 4.392607 4.396204 4.400058 4.401536 4.415275 4.401149 4.400250 #> [348,] 2.160001 4.392644 4.396241 4.400095 4.401573 4.415237 4.401112 4.400212 #> [349,] 2.160020 4.392682 4.396279 4.400133 4.401611 4.415200 4.401074 4.400175 #> [350,] 2.160038 4.392719 4.396316 4.400170 4.401648 4.415162 4.401037 4.400137 #> [351,] 2.160056 4.392756 4.396354 4.400208 4.401686 4.415125 4.400999 4.400100 #> [352,] 2.160075 4.392794 4.396391 4.400245 4.401723 4.415087 4.400962 4.400062 #> [353,] 2.160093 4.392831 4.396428 4.400282 4.401761 4.415049 4.400924 4.400025 #> [354,] 2.160111 4.392869 4.396466 4.400320 4.401798 4.415012 4.400887 4.399987 #> [355,] 2.160130 4.392906 4.396503 4.400357 4.401836 4.414974 4.400850 4.399950 #> [356,] 2.160148 4.392943 4.396541 4.400395 4.401873 4.414937 4.400812 4.399912 #> [357,] 2.160167 4.392981 4.396578 4.400432 4.401911 4.414899 4.400775 4.399875 #> [358,] 2.160185 4.393018 4.396616 4.400470 4.401948 4.414862 4.400737 4.399838 #> [359,] 2.160203 4.393056 4.396653 4.400507 4.401986 4.414824 4.400700 4.399800 #> [360,] 2.160222 4.393093 4.396690 4.400545 4.402023 4.414786 4.400662 4.399763 #> [361,] 2.151164 4.374672 4.378254 4.382092 4.383564 4.397634 4.411749 4.412651 #> [362,] 2.151182 4.374709 4.378291 4.382129 4.383602 4.397672 4.411787 4.412688 #> [363,] 2.151200 4.374746 4.378329 4.382167 4.383639 4.397709 4.411824 4.412726 #> [364,] 2.151218 4.374784 4.378366 4.382204 4.383676 4.397747 4.411862 4.412764 #> [365,] 2.151237 4.374821 4.378403 4.382241 4.383714 4.397784 4.411899 4.412801 #> [366,] 2.151255 4.374858 4.378440 4.382279 4.383751 4.397822 4.411937 4.412839 #> [367,] 2.151273 4.374895 4.378478 4.382316 4.383788 4.397859 4.411974 4.412876 #> [368,] 2.151292 4.374933 4.378515 4.382353 4.383826 4.397897 4.412012 4.412914 #> [369,] 2.151310 4.374970 4.378552 4.382391 4.383863 4.397934 4.412049 4.412951 #> [370,] 2.151328 4.375007 4.378590 4.382428 4.383900 4.397971 4.412087 4.412989 #> [371,] 2.151347 4.375044 4.378627 4.382465 4.383938 4.398009 4.412125 4.413027 #> [372,] 2.151365 4.375082 4.378664 4.382503 4.383975 4.398046 4.412162 4.413064 #> [373,] 2.151383 4.375119 4.378701 4.382540 4.384012 4.398084 4.412200 4.413102 #> [374,] 2.151402 4.375156 4.378739 4.382577 4.384050 4.398121 4.412237 4.413139 #> [375,] 2.152629 4.377652 4.381237 4.385078 4.386551 4.400631 4.414755 4.415281 #> [376,] 2.152648 4.377690 4.381275 4.385115 4.386589 4.400668 4.414792 4.415244 #> [377,] 2.152666 4.377727 4.381312 4.385153 4.386626 4.400706 4.414830 4.415206 #> [378,] 2.152684 4.377764 4.381349 4.385190 4.386663 4.400743 4.414868 4.415168 #> [379,] 2.152703 4.377802 4.381386 4.385227 4.386701 4.400781 4.414905 4.415131 #> [380,] 2.152721 4.377839 4.381424 4.385265 4.386738 4.400818 4.414943 4.415093 #> [381,] 2.152739 4.377876 4.381461 4.385302 4.386775 4.400856 4.414980 4.415056 #> [382,] 2.152758 4.377913 4.381498 4.385339 4.386813 4.400893 4.415018 4.415018 #> [383,] 2.152776 4.377951 4.381536 4.385377 4.386850 4.400931 4.415056 4.414981 #> [384,] 2.152794 4.377988 4.381573 4.385414 4.386887 4.400968 4.415093 4.414943 #> [385,] 2.152812 4.378025 4.381610 4.385451 4.386925 4.401005 4.415131 4.414905 #> [386,] 2.152831 4.378062 4.381648 4.385489 4.386962 4.401043 4.415168 4.414868 #> [387,] 2.152849 4.378100 4.381685 4.385526 4.386999 4.401080 4.415206 4.414830 #> [388,] 2.152867 4.378137 4.381722 4.385563 4.387037 4.401118 4.415244 4.414793 #> [389,] 2.152886 4.378174 4.381759 4.385601 4.387074 4.401155 4.415281 4.414755 #> [390,] 2.152904 4.378212 4.381797 4.385638 4.387111 4.401193 4.415319 4.414717 #> [391,] 2.152922 4.378249 4.381834 4.385675 4.387149 4.401230 4.415356 4.414680 #> [392,] 2.152941 4.378286 4.381871 4.385713 4.387186 4.401268 4.415394 4.414642 #> [393,] 2.152959 4.378323 4.381909 4.385750 4.387223 4.401305 4.415432 4.414605 #> [394,] 2.152977 4.378361 4.381946 4.385787 4.387261 4.401343 4.415469 4.414567 #> [395,] 2.152996 4.378398 4.381983 4.385825 4.387298 4.401380 4.415432 4.414529 #> [396,] 2.153014 4.378435 4.382021 4.385862 4.387336 4.401418 4.415394 4.414492 #> [397,] 2.153032 4.378473 4.382058 4.385899 4.387373 4.401455 4.415357 4.414454 #> [398,] 2.153051 4.378510 4.382095 4.385937 4.387410 4.401493 4.415319 4.414417 #> [399,] 2.153069 4.378547 4.382133 4.385974 4.387448 4.401530 4.415282 4.414379 #> [400,] 2.153087 4.378584 4.382170 4.386011 4.387485 4.401568 4.415244 4.414342 #> [401,] 2.153106 4.378622 4.382207 4.386049 4.387522 4.401605 4.415206 4.414304 #> [402,] 2.153124 4.378659 4.382244 4.386086 4.387560 4.401643 4.415169 4.414266 #> [403,] 2.153142 4.378696 4.382282 4.386123 4.387597 4.401680 4.415131 4.414229 #> [404,] 2.153161 4.378733 4.382319 4.386161 4.387634 4.401717 4.415094 4.414191 #> [405,] 2.153179 4.378771 4.382356 4.386198 4.387672 4.401755 4.415056 4.414154 #> [406,] 2.153197 4.378808 4.382394 4.386235 4.387709 4.401792 4.415019 4.414116 #> [407,] 2.153216 4.378845 4.382431 4.386273 4.387746 4.401830 4.414981 4.414078 #> [408,] 2.153234 4.378883 4.382468 4.386310 4.387784 4.401867 4.414943 4.414041 #> [409,] 2.153252 4.378920 4.382506 4.386347 4.387821 4.401905 4.414906 4.414003 #> [410,] 2.153271 4.378957 4.382543 4.386385 4.387858 4.401942 4.414868 4.413966 #> [411,] 2.153289 4.378994 4.382580 4.386422 4.387896 4.401980 4.414831 4.413928 #> [412,] 2.153307 4.379032 4.382618 4.386460 4.387933 4.402017 4.414793 4.413891 #> [413,] 2.153326 4.379069 4.382655 4.386497 4.387971 4.402055 4.414755 4.413853 #> [414,] 2.153344 4.379106 4.382692 4.386534 4.388008 4.402092 4.414718 4.413815 #> [415,] 2.153362 4.379144 4.382730 4.386572 4.388045 4.402130 4.414680 4.413778 #> [416,] 2.153381 4.379181 4.382767 4.386609 4.388083 4.402167 4.414643 4.413740 #> [417,] 2.153399 4.379218 4.382804 4.386646 4.388120 4.402205 4.414605 4.413703 #> [418,] 2.153417 4.379255 4.382841 4.386684 4.388157 4.402242 4.414567 4.413665 #> [419,] 2.153436 4.379293 4.382879 4.386721 4.388195 4.402280 4.414530 4.413628 #> [420,] 2.153454 4.379330 4.382916 4.386758 4.388232 4.402317 4.414492 4.413590 #> [421,] 2.153472 4.379367 4.382953 4.386796 4.388269 4.402355 4.414455 4.413552 #> [422,] 2.153491 4.379405 4.382991 4.386833 4.388307 4.402392 4.414417 4.413515 #> [423,] 2.153509 4.379442 4.383028 4.386870 4.388344 4.402430 4.414380 4.413477 #> [424,] 2.153527 4.379479 4.383065 4.386908 4.388382 4.402467 4.414342 4.413440 #> [425,] 2.153546 4.379516 4.383103 4.386945 4.388419 4.402505 4.414304 4.413402 #> [426,] 2.153564 4.379554 4.383140 4.386982 4.388456 4.402542 4.414267 4.413364 #> [427,] 2.153582 4.379591 4.383177 4.387020 4.388494 4.402579 4.414229 4.413327 #> [428,] 2.153601 4.379628 4.383215 4.387057 4.388531 4.402617 4.414192 4.413289 #> [429,] 2.153619 4.379666 4.383252 4.387094 4.388568 4.402654 4.414154 4.413252 #> [430,] 2.153637 4.379703 4.383289 4.387132 4.388606 4.402692 4.414116 4.413214 #> [431,] 2.153656 4.379740 4.383327 4.387169 4.388643 4.402729 4.414079 4.413177 #> [432,] 2.153674 4.379777 4.383364 4.387206 4.388680 4.402767 4.414041 4.413139 #> [433,] 2.153692 4.379815 4.383401 4.387244 4.388718 4.402804 4.414004 4.413101 #> [434,] 2.153711 4.379852 4.383439 4.387281 4.388755 4.402842 4.413966 4.413064 #> [435,] 2.153729 4.379889 4.383476 4.387319 4.388793 4.402879 4.413929 4.413026 #> [436,] 2.153747 4.379927 4.383513 4.387356 4.388830 4.402917 4.413891 4.412989 #> [437,] 2.153766 4.379964 4.383551 4.387393 4.388867 4.402954 4.413853 4.412951 #> [438,] 2.153784 4.380001 4.383588 4.387431 4.388905 4.402992 4.413816 4.412914 #> [439,] 2.153802 4.380038 4.383625 4.387468 4.388942 4.403029 4.413778 4.412876 #> [440,] 2.153821 4.380076 4.383663 4.387505 4.388979 4.403067 4.413741 4.412838 #> [441,] 2.153839 4.380113 4.383700 4.387543 4.389017 4.403104 4.413703 4.412801 #> [442,] 2.153858 4.380150 4.383737 4.387580 4.389054 4.403142 4.413666 4.412763 #> [443,] 2.153876 4.380188 4.383774 4.387617 4.389091 4.403179 4.413628 4.412726 #> [444,] 2.153894 4.380225 4.383812 4.387655 4.389129 4.403217 4.413590 4.412688 #> [445,] 2.153913 4.380262 4.383849 4.387692 4.389166 4.403254 4.413553 4.412651 #> [446,] 2.153931 4.380300 4.383886 4.387729 4.389204 4.403292 4.413515 4.412613 #> [447,] 2.153949 4.380337 4.383924 4.387767 4.389241 4.403329 4.413478 4.412575 #> [448,] 2.153968 4.380374 4.383961 4.387804 4.389278 4.403367 4.413440 4.412538 #> [449,] 2.153986 4.380411 4.383998 4.387842 4.389316 4.403404 4.413402 4.412500 #> [450,] 2.154004 4.380449 4.384036 4.387879 4.389353 4.403442 4.413365 4.412463 #> [451,] 2.154023 4.380486 4.384073 4.387916 4.389390 4.403479 4.413327 4.412425 #> [452,] 2.154041 4.380523 4.384110 4.387954 4.389428 4.403517 4.413290 4.412388 #> [453,] 2.154059 4.380561 4.384148 4.387991 4.389465 4.403554 4.413252 4.412350 #> [454,] 2.154078 4.380598 4.384185 4.388028 4.389503 4.403592 4.413215 4.412313 #> [455,] 2.154096 4.380635 4.384222 4.388066 4.389540 4.403629 4.413177 4.412275 #> [456,] 2.154114 4.380672 4.384260 4.388103 4.389577 4.403667 4.413139 4.412237 #> [457,] 2.154133 4.380710 4.384297 4.388140 4.389615 4.403704 4.413102 4.412200 #> [458,] 2.154151 4.380747 4.384334 4.388178 4.389652 4.403742 4.413064 4.412162 #> [459,] 2.154169 4.380784 4.384372 4.388215 4.389689 4.403779 4.413027 4.412125 #> [460,] 2.127075 4.325685 4.329227 4.333022 4.334478 4.348390 4.362347 4.363239 #> [461,] 2.127093 4.325722 4.329264 4.333059 4.334515 4.348427 4.362384 4.363276 #> [462,] 2.118559 4.308367 4.311895 4.315675 4.317125 4.330982 4.344882 4.345771 #> [463,] 2.118577 4.308404 4.311932 4.315712 4.317162 4.331019 4.344919 4.345808 #> [464,] 2.118595 4.308440 4.311968 4.315748 4.317198 4.331055 4.344956 4.345845 #> [465,] 2.118613 4.308477 4.312005 4.315785 4.317235 4.331092 4.344993 4.345882 #> [466,] 2.118632 4.308514 4.312042 4.315822 4.317272 4.331129 4.345030 4.345919 #> [467,] 2.118650 4.308550 4.312079 4.315859 4.317309 4.331166 4.345067 4.345956 #> [468,] 2.154188 4.380822 4.384409 4.388252 4.389727 4.403817 4.412989 4.412087 #> [469,] 2.154206 4.380859 4.384446 4.388290 4.389764 4.403854 4.412952 4.412050 #> [470,] 2.154224 4.380896 4.384484 4.388327 4.389802 4.403892 4.412914 4.412012 #> [471,] 2.118668 4.308587 4.312115 4.315895 4.317345 4.331203 4.345104 4.345993 #> [472,] 2.118686 4.308624 4.312152 4.315932 4.317382 4.331240 4.345141 4.346030 #> [473,] 2.118704 4.308660 4.312189 4.315969 4.317419 4.331277 4.345178 4.346067 #> [474,] 2.118722 4.308697 4.312225 4.316006 4.317456 4.331314 4.345215 4.346104 #> [475,] 2.118740 4.308734 4.312262 4.316042 4.317492 4.331350 4.345252 4.346141 #> [476,] 2.118758 4.308771 4.312299 4.316079 4.317529 4.331387 4.345289 4.346178 #> [477,] 2.118776 4.308807 4.312336 4.316116 4.317566 4.331424 4.345326 4.346215 #> [478,] 2.118794 4.308844 4.312372 4.316153 4.317603 4.331461 4.345363 4.346252 #> [479,] 2.118812 4.308881 4.312409 4.316189 4.317639 4.331498 4.345400 4.346289 #> [480,] 2.118830 4.308917 4.312446 4.316226 4.317676 4.331535 4.345437 4.346326 #> [481,] 2.118848 4.308954 4.312482 4.316263 4.317713 4.331572 4.345474 4.346363 #> [482,] 2.118866 4.308991 4.312519 4.316300 4.317750 4.331609 4.345511 4.346400 #> [483,] 2.118884 4.309027 4.312556 4.316336 4.317787 4.331645 4.345548 4.346437 #> [484,] 2.118902 4.309064 4.312593 4.316373 4.317823 4.331682 4.345585 4.346474 #> [485,] 2.118920 4.309101 4.312629 4.316410 4.317860 4.331719 4.345622 4.346511 #> [486,] 2.118938 4.309137 4.312666 4.316447 4.317897 4.331756 4.345659 4.346548 #> [487,] 2.118956 4.309174 4.312703 4.316483 4.317934 4.331793 4.345696 4.346585 #> [488,] 2.118974 4.309211 4.312739 4.316520 4.317970 4.331830 4.345733 4.346622 #> [489,] 2.118992 4.309247 4.312776 4.316557 4.318007 4.331867 4.345770 4.346659 #> [490,] 2.119010 4.309284 4.312813 4.316594 4.318044 4.331904 4.345807 4.346696 #> [491,] 2.119028 4.309321 4.312850 4.316630 4.318081 4.331940 4.345844 4.346733 #> [492,] 2.119046 4.309358 4.312886 4.316667 4.318117 4.331977 4.345881 4.346770 #> [493,] 2.119064 4.309394 4.312923 4.316704 4.318154 4.332014 4.345918 4.346807 #> [494,] 2.119083 4.309431 4.312960 4.316741 4.318191 4.332051 4.345955 4.346844 #> [495,] 2.119101 4.309468 4.312997 4.316777 4.318228 4.332088 4.345992 4.346881 #> [496,] 2.119119 4.309504 4.313033 4.316814 4.318264 4.332125 4.346029 4.346918 #> [497,] 2.119137 4.309541 4.313070 4.316851 4.318301 4.332162 4.346066 4.346955 #> [498,] 2.119155 4.309578 4.313107 4.316888 4.318338 4.332199 4.346103 4.346992 #> [499,] 2.119173 4.309614 4.313143 4.316924 4.318375 4.332236 4.346140 4.347029 #> [500,] 2.119191 4.309651 4.313180 4.316961 4.318412 4.332272 4.346177 4.347066 #> [501,] 2.119209 4.309688 4.313217 4.316998 4.318448 4.332309 4.346214 4.347103 #> [502,] 2.119227 4.309724 4.313254 4.317035 4.318485 4.332346 4.346251 4.347140 #> [503,] 2.119245 4.309761 4.313290 4.317071 4.318522 4.332383 4.346288 4.347177 #> [504,] 2.119263 4.309798 4.313327 4.317108 4.318559 4.332420 4.346325 4.347214 #> [505,] 2.119281 4.309835 4.313364 4.317145 4.318595 4.332457 4.346362 4.347251 #> [506,] 2.119299 4.309871 4.313400 4.317182 4.318632 4.332494 4.346399 4.347288 #> [507,] 2.119317 4.309908 4.313437 4.317218 4.318669 4.332531 4.346436 4.347325 #> [508,] 2.119335 4.309945 4.313474 4.317255 4.318706 4.332568 4.346473 4.347362 #> [509,] 2.171452 4.415007 4.411395 4.407531 4.406051 4.391954 4.377903 4.377008 #> [510,] 2.171471 4.414970 4.411357 4.407494 4.406013 4.391916 4.377865 4.376971 #> [511,] 2.171489 4.414932 4.411320 4.407456 4.405976 4.391879 4.377828 4.376933 #> [512,] 2.171508 4.414894 4.411282 4.407418 4.405938 4.391842 4.377791 4.376896 #> [513,] 2.171526 4.414857 4.411245 4.407381 4.405901 4.391804 4.377754 4.376859 #> [514,] 2.171545 4.414819 4.411207 4.407343 4.405863 4.391767 4.377716 4.376821 #> [515,] 2.171563 4.414782 4.411169 4.407306 4.405826 4.391729 4.377679 4.376784 #> [516,] 2.171582 4.414744 4.411132 4.407268 4.405788 4.391692 4.377642 4.376747 #> [517,] 2.171600 4.414706 4.411094 4.407231 4.405751 4.391655 4.377604 4.376710 #> [518,] 2.171619 4.414669 4.411057 4.407193 4.405713 4.391617 4.377567 4.376672 #> [519,] 2.160249 4.393147 4.396745 4.400599 4.402078 4.414732 4.400608 4.399708 #> [520,] 2.160267 4.393185 4.396782 4.400637 4.402115 4.414694 4.400570 4.399671 #> [521,] 2.160285 4.393222 4.396820 4.400674 4.402153 4.414657 4.400533 4.399633 #> [522,] 2.160304 4.393260 4.396857 4.400712 4.402190 4.414619 4.400495 4.399596 #> [523,] 2.160322 4.393297 4.396895 4.400749 4.402228 4.414581 4.400458 4.399558 #> [524,] 2.160341 4.393335 4.396932 4.400787 4.402265 4.414544 4.400420 4.399521 #> [525,] 2.160359 4.393372 4.396970 4.400824 4.402303 4.414506 4.400383 4.399483 #> [526,] 2.160377 4.393409 4.397007 4.400861 4.402340 4.414469 4.400345 4.399446 #> [527,] 2.160396 4.393447 4.397044 4.400899 4.402377 4.414431 4.400308 4.399409 #> [528,] 2.160414 4.393484 4.397082 4.400936 4.402415 4.414393 4.400271 4.399371 #> [529,] 2.160433 4.393522 4.397119 4.400974 4.402452 4.414356 4.400233 4.399334 #> [530,] 2.160451 4.393559 4.397157 4.401011 4.402490 4.414318 4.400196 4.399296 #> [531,] 2.151420 4.375193 4.378776 4.382615 4.384087 4.398159 4.412275 4.413177 #> [532,] 2.151438 4.375231 4.378813 4.382652 4.384124 4.398196 4.412312 4.413214 #> [533,] 2.151457 4.375268 4.378851 4.382689 4.384162 4.398234 4.412350 4.413252 #> [534,] 2.151475 4.375305 4.378888 4.382726 4.384199 4.398271 4.412388 4.413290 #> [535,] 2.151493 4.375342 4.378925 4.382764 4.384236 4.398308 4.412425 4.413327 #> [536,] 2.151512 4.375380 4.378962 4.382801 4.384274 4.398346 4.412463 4.413365 #> [537,] 2.151530 4.375417 4.379000 4.382838 4.384311 4.398383 4.412500 4.413402 #> [538,] 2.151548 4.375454 4.379037 4.382876 4.384348 4.398421 4.412538 4.413440 #> [539,] 2.151566 4.375491 4.379074 4.382913 4.384386 4.398458 4.412575 4.413478 #> [540,] 2.151585 4.375529 4.379112 4.382950 4.384423 4.398496 4.412613 4.413515 #> [541,] 2.151603 4.375566 4.379149 4.382988 4.384460 4.398533 4.412651 4.413553 #> [542,] 2.151621 4.375603 4.379186 4.383025 4.384498 4.398571 4.412688 4.413590 #> [543,] 2.151640 4.375640 4.379223 4.383062 4.384535 4.398608 4.412726 4.413628 #> [544,] 2.151658 4.375678 4.379261 4.383100 4.384572 4.398645 4.412763 4.413665 #> [545,] 2.151676 4.375715 4.379298 4.383137 4.384610 4.398683 4.412801 4.413703 #> [546,] 2.151695 4.375752 4.379335 4.383174 4.384647 4.398720 4.412838 4.413741 #> [547,] 2.151713 4.375789 4.379373 4.383212 4.384684 4.398758 4.412876 4.413778 #> [548,] 2.151731 4.375827 4.379410 4.383249 4.384722 4.398795 4.412914 4.413816 #> [549,] 2.151750 4.375864 4.379447 4.383286 4.384759 4.398833 4.412951 4.413853 #> [550,] 2.151768 4.375901 4.379484 4.383324 4.384796 4.398870 4.412989 4.413891 #> [551,] 2.151786 4.375938 4.379522 4.383361 4.384834 4.398908 4.413026 4.413928 #> [552,] 2.151805 4.375976 4.379559 4.383398 4.384871 4.398945 4.413064 4.413966 #> [553,] 2.151823 4.376013 4.379596 4.383435 4.384908 4.398983 4.413101 4.414004 #> [554,] 2.151841 4.376050 4.379634 4.383473 4.384946 4.399020 4.413139 4.414041 #> [555,] 2.151860 4.376087 4.379671 4.383510 4.384983 4.399057 4.413177 4.414079 #> [556,] 2.151878 4.376125 4.379708 4.383547 4.385020 4.399095 4.413214 4.414116 #> [557,] 2.151896 4.376162 4.379745 4.383585 4.385058 4.399132 4.413252 4.414154 #> [558,] 2.151915 4.376199 4.379783 4.383622 4.385095 4.399170 4.413289 4.414192 #> [559,] 2.151933 4.376236 4.379820 4.383659 4.385132 4.399207 4.413327 4.414229 #> [560,] 2.151951 4.376274 4.379857 4.383697 4.385170 4.399245 4.413364 4.414267 #> [561,] 2.151970 4.376311 4.379895 4.383734 4.385207 4.399282 4.413402 4.414304 #> [562,] 2.151988 4.376348 4.379932 4.383771 4.385244 4.399320 4.413440 4.414342 #> [563,] 2.152006 4.376385 4.379969 4.383809 4.385282 4.399357 4.413477 4.414379 #> [564,] 2.152024 4.376423 4.380006 4.383846 4.385319 4.399395 4.413515 4.414417 #> [565,] 2.152043 4.376460 4.380044 4.383883 4.385356 4.399432 4.413552 4.414455 #> [566,] 2.152061 4.376497 4.380081 4.383921 4.385394 4.399469 4.413590 4.414492 #> [567,] 2.152079 4.376534 4.380118 4.383958 4.385431 4.399507 4.413627 4.414530 #> [568,] 2.152098 4.376572 4.380156 4.383995 4.385468 4.399544 4.413665 4.414567 #> [569,] 2.152116 4.376609 4.380193 4.384033 4.385506 4.399582 4.413703 4.414605 #> [570,] 2.152134 4.376646 4.380230 4.384070 4.385543 4.399619 4.413740 4.414643 #> [571,] 2.152153 4.376684 4.380267 4.384107 4.385580 4.399657 4.413778 4.414680 #> [572,] 2.152171 4.376721 4.380305 4.384145 4.385618 4.399694 4.413815 4.414718 #> [573,] 2.152189 4.376758 4.380342 4.384182 4.385655 4.399732 4.413853 4.414755 #> [574,] 2.152208 4.376795 4.380379 4.384219 4.385692 4.399769 4.413890 4.414793 #> [575,] 2.152226 4.376833 4.380417 4.384257 4.385730 4.399807 4.413928 4.414830 #> [576,] 2.152244 4.376870 4.380454 4.384294 4.385767 4.399844 4.413966 4.414868 #> [577,] 2.152263 4.376907 4.380491 4.384331 4.385804 4.399882 4.414003 4.414906 #> [578,] 2.152281 4.376944 4.380529 4.384369 4.385842 4.399919 4.414041 4.414943 #> [579,] 2.152299 4.376982 4.380566 4.384406 4.385879 4.399956 4.414078 4.414981 #> [580,] 2.152318 4.377019 4.380603 4.384443 4.385916 4.399994 4.414116 4.415018 #> [581,] 2.152336 4.377056 4.380640 4.384481 4.385954 4.400031 4.414154 4.415056 #> [582,] 2.152354 4.377093 4.380678 4.384518 4.385991 4.400069 4.414191 4.415094 #> [583,] 2.152373 4.377131 4.380715 4.384555 4.386028 4.400106 4.414229 4.415131 #> [584,] 2.152391 4.377168 4.380752 4.384593 4.386066 4.400144 4.414266 4.415169 #> [585,] 2.152409 4.377205 4.380790 4.384630 4.386103 4.400181 4.414304 4.415206 #> [586,] 2.152428 4.377243 4.380827 4.384667 4.386140 4.400219 4.414341 4.415244 #> [587,] 2.152446 4.377280 4.380864 4.384705 4.386178 4.400256 4.414379 4.415282 #> [588,] 2.152464 4.377317 4.380902 4.384742 4.386215 4.400294 4.414417 4.415319 #> [589,] 2.152483 4.377354 4.380939 4.384779 4.386252 4.400331 4.414454 4.415357 #> [590,] 2.152501 4.377392 4.380976 4.384817 4.386290 4.400369 4.414492 4.415394 #> [591,] 2.152519 4.377429 4.381013 4.384854 4.386327 4.400406 4.414529 4.415432 #> [592,] 2.152538 4.377466 4.381051 4.384891 4.386364 4.400443 4.414567 4.415469 #> [593,] 2.152556 4.377503 4.381088 4.384929 4.386402 4.400481 4.414605 4.415432 #> [594,] 2.152574 4.377541 4.381125 4.384966 4.386439 4.400518 4.414642 4.415394 #> [595,] 2.152593 4.377578 4.381163 4.385003 4.386476 4.400556 4.414680 4.415356 #> [596,] 2.152611 4.377615 4.381200 4.385041 4.386514 4.400593 4.414717 4.415319 #> [597,] 2.154243 4.380934 4.384521 4.388365 4.389839 4.403929 4.412876 4.411974 #> [598,] 2.154261 4.380971 4.384558 4.388402 4.389876 4.403967 4.412839 4.411937 #> [599,] 2.154279 4.381008 4.384596 4.388439 4.389914 4.404004 4.412801 4.411899 #> [600,] 2.154298 4.381045 4.384633 4.388477 4.389951 4.404042 4.412764 4.411862 #> [601,] 2.154316 4.381083 4.384670 4.388514 4.389988 4.404079 4.412726 4.411824 #> [602,] 2.154334 4.381120 4.384708 4.388551 4.390026 4.404117 4.412689 4.411787 #> [603,] 2.154353 4.381157 4.384745 4.388589 4.390063 4.404154 4.412651 4.411749 #> [604,] 2.154371 4.381195 4.384782 4.388626 4.390101 4.404192 4.412613 4.411711 #> [605,] 2.154389 4.381232 4.384820 4.388663 4.390138 4.404229 4.412576 4.411674 #> [606,] 2.154408 4.381269 4.384857 4.388701 4.390175 4.404267 4.412538 4.411636 #> [607,] 2.154426 4.381307 4.384894 4.388738 4.390213 4.404304 4.412501 4.411599 #> [608,] 2.154444 4.381344 4.384932 4.388776 4.390250 4.404342 4.412463 4.411561 #> [609,] 2.154463 4.381381 4.384969 4.388813 4.390287 4.404379 4.412426 4.411524 #> [610,] 2.154481 4.381419 4.385006 4.388850 4.390325 4.404417 4.412388 4.411486 #> [611,] 2.154499 4.381456 4.385044 4.388888 4.390362 4.404454 4.412350 4.411449 #> [612,] 2.154518 4.381493 4.385081 4.388925 4.390400 4.404492 4.412313 4.411411 #> [613,] 2.154536 4.381530 4.385118 4.388962 4.390437 4.404529 4.412275 4.411373 #> [614,] 2.154554 4.381568 4.385156 4.389000 4.390474 4.404567 4.412238 4.411336 #> [615,] 2.154573 4.381605 4.385193 4.389037 4.390512 4.404604 4.412200 4.411298 #> [616,] 2.126369 4.324249 4.327790 4.331584 4.333039 4.346947 4.360898 4.361790 #> [617,] 2.126387 4.324285 4.327827 4.331620 4.333076 4.346984 4.360936 4.361827 #> [618,] 2.126405 4.324322 4.327863 4.331657 4.333113 4.347021 4.360973 4.361864 #> [619,] 2.126423 4.324359 4.327900 4.331694 4.333149 4.347058 4.361010 4.361901 #> [620,] 2.126441 4.324396 4.327937 4.331731 4.333186 4.347095 4.361047 4.361939 #> [621,] 2.126459 4.324433 4.327974 4.331768 4.333223 4.347132 4.361084 4.361976 #> [622,] 2.126478 4.324470 4.328011 4.331805 4.333260 4.347169 4.361121 4.362013 #> [623,] 2.126496 4.324506 4.328048 4.331842 4.333297 4.347206 4.361158 4.362050 #> [624,] 2.126514 4.324543 4.328084 4.331879 4.333334 4.347243 4.361195 4.362087 #> [625,] 2.126532 4.324580 4.328121 4.331915 4.333371 4.347280 4.361233 4.362124 #> [626,] 2.126550 4.324617 4.328158 4.331952 4.333408 4.347317 4.361270 4.362161 #> [627,] 2.126568 4.324654 4.328195 4.331989 4.333445 4.347354 4.361307 4.362199 #> [628,] 2.126586 4.324690 4.328232 4.332026 4.333482 4.347391 4.361344 4.362236 #> [629,] 2.126599 4.324717 4.328258 4.332053 4.333508 4.347417 4.361371 4.362262 #> [630,] 2.126622 4.324764 4.328306 4.332100 4.333555 4.347465 4.361418 4.362310 #> [631,] 2.126640 4.324801 4.328342 4.332137 4.333592 4.347502 4.361455 4.362347 #> [632,] 2.126659 4.324838 4.328379 4.332174 4.333629 4.347539 4.361493 4.362384 #> [633,] 2.126677 4.324875 4.328416 4.332210 4.333666 4.347576 4.361530 4.362421 #> [634,] 2.126695 4.324911 4.328453 4.332247 4.333703 4.347613 4.361567 4.362459 #> [635,] 2.126713 4.324948 4.328490 4.332284 4.333740 4.347650 4.361604 4.362496 #> [636,] 2.126731 4.324985 4.328527 4.332321 4.333777 4.347687 4.361641 4.362533 #> [637,] 2.126749 4.325022 4.328564 4.332358 4.333814 4.347724 4.361678 4.362570 #> [638,] 2.126767 4.325059 4.328600 4.332395 4.333850 4.347761 4.361715 4.362607 #> [639,] 2.126785 4.325096 4.328637 4.332432 4.333887 4.347798 4.361752 4.362644 #> [640,] 2.126803 4.325132 4.328674 4.332469 4.333924 4.347835 4.361790 4.362681 #> [641,] 2.126822 4.325169 4.328711 4.332506 4.333961 4.347872 4.361827 4.362719 #> [642,] 2.126840 4.325206 4.328748 4.332542 4.333998 4.347909 4.361864 4.362756 #> [643,] 2.126858 4.325243 4.328785 4.332579 4.334035 4.347946 4.361901 4.362793 #> [644,] 2.126876 4.325280 4.328822 4.332616 4.334072 4.347983 4.361938 4.362830 #> [645,] 2.126894 4.325316 4.328858 4.332653 4.334109 4.348020 4.361975 4.362867 #> [646,] 2.126912 4.325353 4.328895 4.332690 4.334146 4.348057 4.362012 4.362904 #> [647,] 2.126930 4.325390 4.328932 4.332727 4.334183 4.348094 4.362050 4.362941 #> [648,] 2.126948 4.325427 4.328969 4.332764 4.334219 4.348131 4.362087 4.362979 #> [649,] 2.126966 4.325464 4.329006 4.332801 4.334256 4.348168 4.362124 4.363016 #> [650,] 2.126984 4.325501 4.329043 4.332838 4.334293 4.348205 4.362161 4.363053 #> [651,] 2.127003 4.325537 4.329079 4.332874 4.334330 4.348242 4.362198 4.363090 #> [652,] 2.127021 4.325574 4.329116 4.332911 4.334367 4.348279 4.362235 4.363127 #> [653,] 2.127039 4.325611 4.329153 4.332948 4.334404 4.348316 4.362272 4.363164 #> [654,] 2.127057 4.325648 4.329190 4.332985 4.334441 4.348353 4.362310 4.363201 #>            [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] #>   [1,] 4.320037     0     0     0     0     0     0     0     0     0     0 #>   [2,] 4.320001     0     0     0     0     0     0     0     0     0     0 #>   [3,] 4.319964     0     0     0     0     0     0     0     0     0     0 #>   [4,] 4.319927     0     0     0     0     0     0     0     0     0     0 #>   [5,] 4.319890     0     0     0     0     0     0     0     0     0     0 #>   [6,] 4.319853     0     0     0     0     0     0     0     0     0     0 #>   [7,] 4.319817     0     0     0     0     0     0     0     0     0     0 #>   [8,] 4.319780     0     0     0     0     0     0     0     0     0     0 #>   [9,] 4.319743     0     0     0     0     0     0     0     0     0     0 #>  [10,] 4.319706     0     0     0     0     0     0     0     0     0     0 #>  [11,] 4.319670     0     0     0     0     0     0     0     0     0     0 #>  [12,] 4.319633     0     0     0     0     0     0     0     0     0     0 #>  [13,] 4.319596     0     0     0     0     0     0     0     0     0     0 #>  [14,] 4.319559     0     0     0     0     0     0     0     0     0     0 #>  [15,] 4.319522     0     0     0     0     0     0     0     0     0     0 #>  [16,] 4.319486     0     0     0     0     0     0     0     0     0     0 #>  [17,] 4.319449     0     0     0     0     0     0     0     0     0     0 #>  [18,] 4.319412     0     0     0     0     0     0     0     0     0     0 #>  [19,] 4.319375     0     0     0     0     0     0     0     0     0     0 #>  [20,] 4.319339     0     0     0     0     0     0     0     0     0     0 #>  [21,] 4.319302     0     0     0     0     0     0     0     0     0     0 #>  [22,] 4.319265     0     0     0     0     0     0     0     0     0     0 #>  [23,] 4.319228     0     0     0     0     0     0     0     0     0     0 #>  [24,] 4.319192     0     0     0     0     0     0     0     0     0     0 #>  [25,] 4.319155     0     0     0     0     0     0     0     0     0     0 #>  [26,] 4.319118     0     0     0     0     0     0     0     0     0     0 #>  [27,] 4.319081     0     0     0     0     0     0     0     0     0     0 #>  [28,] 4.319044     0     0     0     0     0     0     0     0     0     0 #>  [29,] 4.319008     0     0     0     0     0     0     0     0     0     0 #>  [30,] 4.318971     0     0     0     0     0     0     0     0     0     0 #>  [31,] 4.318934     0     0     0     0     0     0     0     0     0     0 #>  [32,] 4.318897     0     0     0     0     0     0     0     0     0     0 #>  [33,] 4.318861     0     0     0     0     0     0     0     0     0     0 #>  [34,] 4.318824     0     0     0     0     0     0     0     0     0     0 #>  [35,] 4.318787     0     0     0     0     0     0     0     0     0     0 #>  [36,] 4.318750     0     0     0     0     0     0     0     0     0     0 #>  [37,] 4.318713     0     0     0     0     0     0     0     0     0     0 #>  [38,] 4.318677     0     0     0     0     0     0     0     0     0     0 #>  [39,] 4.318640     0     0     0     0     0     0     0     0     0     0 #>  [40,] 4.318603     0     0     0     0     0     0     0     0     0     0 #>  [41,] 4.318566     0     0     0     0     0     0     0     0     0     0 #>  [42,] 4.318530     0     0     0     0     0     0     0     0     0     0 #>  [43,] 4.318493     0     0     0     0     0     0     0     0     0     0 #>  [44,] 4.318456     0     0     0     0     0     0     0     0     0     0 #>  [45,] 4.318419     0     0     0     0     0     0     0     0     0     0 #>  [46,] 4.318383     0     0     0     0     0     0     0     0     0     0 #>  [47,] 4.318346     0     0     0     0     0     0     0     0     0     0 #>  [48,] 4.318309     0     0     0     0     0     0     0     0     0     0 #>  [49,] 4.318272     0     0     0     0     0     0     0     0     0     0 #>  [50,] 4.318236     0     0     0     0     0     0     0     0     0     0 #>  [51,] 4.318199     0     0     0     0     0     0     0     0     0     0 #>  [52,] 4.318162     0     0     0     0     0     0     0     0     0     0 #>  [53,] 4.318125     0     0     0     0     0     0     0     0     0     0 #>  [54,] 4.318088     0     0     0     0     0     0     0     0     0     0 #>  [55,] 4.318052     0     0     0     0     0     0     0     0     0     0 #>  [56,] 4.318015     0     0     0     0     0     0     0     0     0     0 #>  [57,] 4.317978     0     0     0     0     0     0     0     0     0     0 #>  [58,] 4.317941     0     0     0     0     0     0     0     0     0     0 #>  [59,] 4.317905     0     0     0     0     0     0     0     0     0     0 #>  [60,] 4.317868     0     0     0     0     0     0     0     0     0     0 #>  [61,] 4.317831     0     0     0     0     0     0     0     0     0     0 #>  [62,] 4.317794     0     0     0     0     0     0     0     0     0     0 #>  [63,] 4.317758     0     0     0     0     0     0     0     0     0     0 #>  [64,] 4.317721     0     0     0     0     0     0     0     0     0     0 #>  [65,] 4.317684     0     0     0     0     0     0     0     0     0     0 #>  [66,] 4.317647     0     0     0     0     0     0     0     0     0     0 #>  [67,] 4.317611     0     0     0     0     0     0     0     0     0     0 #>  [68,] 4.317574     0     0     0     0     0     0     0     0     0     0 #>  [69,] 4.317537     0     0     0     0     0     0     0     0     0     0 #>  [70,] 4.317500     0     0     0     0     0     0     0     0     0     0 #>  [71,] 4.317464     0     0     0     0     0     0     0     0     0     0 #>  [72,] 4.317427     0     0     0     0     0     0     0     0     0     0 #>  [73,] 4.317390     0     0     0     0     0     0     0     0     0     0 #>  [74,] 4.317353     0     0     0     0     0     0     0     0     0     0 #>  [75,] 4.317316     0     0     0     0     0     0     0     0     0     0 #>  [76,] 4.317280     0     0     0     0     0     0     0     0     0     0 #>  [77,] 4.317243     0     0     0     0     0     0     0     0     0     0 #>  [78,] 4.317206     0     0     0     0     0     0     0     0     0     0 #>  [79,] 4.317169     0     0     0     0     0     0     0     0     0     0 #>  [80,] 4.317133     0     0     0     0     0     0     0     0     0     0 #>  [81,] 4.317096     0     0     0     0     0     0     0     0     0     0 #>  [82,] 4.317059     0     0     0     0     0     0     0     0     0     0 #>  [83,] 4.317022     0     0     0     0     0     0     0     0     0     0 #>  [84,] 4.316986     0     0     0     0     0     0     0     0     0     0 #>  [85,] 4.316949     0     0     0     0     0     0     0     0     0     0 #>  [86,] 4.316912     0     0     0     0     0     0     0     0     0     0 #>  [87,] 4.316875     0     0     0     0     0     0     0     0     0     0 #>  [88,] 4.316839     0     0     0     0     0     0     0     0     0     0 #>  [89,] 4.316802     0     0     0     0     0     0     0     0     0     0 #>  [90,] 4.316765     0     0     0     0     0     0     0     0     0     0 #>  [91,] 4.316728     0     0     0     0     0     0     0     0     0     0 #>  [92,] 4.316692     0     0     0     0     0     0     0     0     0     0 #>  [93,] 4.316655     0     0     0     0     0     0     0     0     0     0 #>  [94,] 4.316618     0     0     0     0     0     0     0     0     0     0 #>  [95,] 4.316581     0     0     0     0     0     0     0     0     0     0 #>  [96,] 4.316545     0     0     0     0     0     0     0     0     0     0 #>  [97,] 4.316508     0     0     0     0     0     0     0     0     0     0 #>  [98,] 4.316471     0     0     0     0     0     0     0     0     0     0 #>  [99,] 4.316434     0     0     0     0     0     0     0     0     0     0 #> [100,] 4.316398     0     0     0     0     0     0     0     0     0     0 #> [101,] 4.316361     0     0     0     0     0     0     0     0     0     0 #> [102,] 4.316324     0     0     0     0     0     0     0     0     0     0 #> [103,] 4.316287     0     0     0     0     0     0     0     0     0     0 #> [104,] 4.316251     0     0     0     0     0     0     0     0     0     0 #> [105,] 4.316214     0     0     0     0     0     0     0     0     0     0 #> [106,] 4.316177     0     0     0     0     0     0     0     0     0     0 #> [107,] 4.316140     0     0     0     0     0     0     0     0     0     0 #> [108,] 4.316104     0     0     0     0     0     0     0     0     0     0 #> [109,] 4.316067     0     0     0     0     0     0     0     0     0     0 #> [110,] 4.316030     0     0     0     0     0     0     0     0     0     0 #> [111,] 4.315993     0     0     0     0     0     0     0     0     0     0 #> [112,] 4.315957     0     0     0     0     0     0     0     0     0     0 #> [113,] 4.315920     0     0     0     0     0     0     0     0     0     0 #> [114,] 4.315883     0     0     0     0     0     0     0     0     0     0 #> [115,] 4.315846     0     0     0     0     0     0     0     0     0     0 #> [116,] 4.315810     0     0     0     0     0     0     0     0     0     0 #> [117,] 4.315773     0     0     0     0     0     0     0     0     0     0 #> [118,] 4.315736     0     0     0     0     0     0     0     0     0     0 #> [119,] 4.315699     0     0     0     0     0     0     0     0     0     0 #> [120,] 4.315663     0     0     0     0     0     0     0     0     0     0 #> [121,] 4.315626     0     0     0     0     0     0     0     0     0     0 #> [122,] 4.315589     0     0     0     0     0     0     0     0     0     0 #> [123,] 4.315552     0     0     0     0     0     0     0     0     0     0 #> [124,] 4.315516     0     0     0     0     0     0     0     0     0     0 #> [125,] 4.315479     0     0     0     0     0     0     0     0     0     0 #> [126,] 4.315442     0     0     0     0     0     0     0     0     0     0 #> [127,] 4.315406     0     0     0     0     0     0     0     0     0     0 #> [128,] 4.315369     0     0     0     0     0     0     0     0     0     0 #> [129,] 4.315332     0     0     0     0     0     0     0     0     0     0 #> [130,] 4.315295     0     0     0     0     0     0     0     0     0     0 #> [131,] 4.315259     0     0     0     0     0     0     0     0     0     0 #> [132,] 4.315222     0     0     0     0     0     0     0     0     0     0 #> [133,] 4.315185     0     0     0     0     0     0     0     0     0     0 #> [134,] 4.315148     0     0     0     0     0     0     0     0     0     0 #> [135,] 4.315112     0     0     0     0     0     0     0     0     0     0 #> [136,] 4.315075     0     0     0     0     0     0     0     0     0     0 #> [137,] 4.315038     0     0     0     0     0     0     0     0     0     0 #> [138,] 4.315001     0     0     0     0     0     0     0     0     0     0 #> [139,] 4.314965     0     0     0     0     0     0     0     0     0     0 #> [140,] 4.314928     0     0     0     0     0     0     0     0     0     0 #> [141,] 4.314891     0     0     0     0     0     0     0     0     0     0 #> [142,] 4.314854     0     0     0     0     0     0     0     0     0     0 #> [143,] 4.314818     0     0     0     0     0     0     0     0     0     0 #> [144,] 4.314781     0     0     0     0     0     0     0     0     0     0 #> [145,] 4.314744     0     0     0     0     0     0     0     0     0     0 #> [146,] 4.314707     0     0     0     0     0     0     0     0     0     0 #> [147,] 4.314671     0     0     0     0     0     0     0     0     0     0 #> [148,] 4.314634     0     0     0     0     0     0     0     0     0     0 #> [149,] 4.314597     0     0     0     0     0     0     0     0     0     0 #> [150,] 4.314561     0     0     0     0     0     0     0     0     0     0 #> [151,] 4.314524     0     0     0     0     0     0     0     0     0     0 #> [152,] 4.314487     0     0     0     0     0     0     0     0     0     0 #> [153,] 4.314450     0     0     0     0     0     0     0     0     0     0 #> [154,] 4.314414     0     0     0     0     0     0     0     0     0     0 #> [155,] 4.314377     0     0     0     0     0     0     0     0     0     0 #> [156,] 4.314340     0     0     0     0     0     0     0     0     0     0 #> [157,] 4.314303     0     0     0     0     0     0     0     0     0     0 #> [158,] 4.314267     0     0     0     0     0     0     0     0     0     0 #> [159,] 4.314230     0     0     0     0     0     0     0     0     0     0 #> [160,] 4.314193     0     0     0     0     0     0     0     0     0     0 #> [161,] 4.314156     0     0     0     0     0     0     0     0     0     0 #> [162,] 4.314120     0     0     0     0     0     0     0     0     0     0 #> [163,] 4.314083     0     0     0     0     0     0     0     0     0     0 #> [164,] 4.314046     0     0     0     0     0     0     0     0     0     0 #> [165,] 4.314010     0     0     0     0     0     0     0     0     0     0 #> [166,] 4.313973     0     0     0     0     0     0     0     0     0     0 #> [167,] 4.313936     0     0     0     0     0     0     0     0     0     0 #> [168,] 4.313899     0     0     0     0     0     0     0     0     0     0 #> [169,] 4.313863     0     0     0     0     0     0     0     0     0     0 #> [170,] 4.313826     0     0     0     0     0     0     0     0     0     0 #> [171,] 4.313789     0     0     0     0     0     0     0     0     0     0 #> [172,] 4.313752     0     0     0     0     0     0     0     0     0     0 #> [173,] 4.313716     0     0     0     0     0     0     0     0     0     0 #> [174,] 4.313679     0     0     0     0     0     0     0     0     0     0 #> [175,] 4.313642     0     0     0     0     0     0     0     0     0     0 #> [176,] 4.313606     0     0     0     0     0     0     0     0     0     0 #> [177,] 4.313569     0     0     0     0     0     0     0     0     0     0 #> [178,] 4.313532     0     0     0     0     0     0     0     0     0     0 #> [179,] 4.313495     0     0     0     0     0     0     0     0     0     0 #> [180,] 4.313459     0     0     0     0     0     0     0     0     0     0 #> [181,] 4.313422     0     0     0     0     0     0     0     0     0     0 #> [182,] 4.313385     0     0     0     0     0     0     0     0     0     0 #> [183,] 4.313348     0     0     0     0     0     0     0     0     0     0 #> [184,] 4.313312     0     0     0     0     0     0     0     0     0     0 #> [185,] 4.313275     0     0     0     0     0     0     0     0     0     0 #> [186,] 4.313238     0     0     0     0     0     0     0     0     0     0 #> [187,] 4.313202     0     0     0     0     0     0     0     0     0     0 #> [188,] 4.313165     0     0     0     0     0     0     0     0     0     0 #> [189,] 4.313128     0     0     0     0     0     0     0     0     0     0 #> [190,] 4.313091     0     0     0     0     0     0     0     0     0     0 #> [191,] 4.313055     0     0     0     0     0     0     0     0     0     0 #> [192,] 4.313018     0     0     0     0     0     0     0     0     0     0 #> [193,] 4.312981     0     0     0     0     0     0     0     0     0     0 #> [194,] 4.312945     0     0     0     0     0     0     0     0     0     0 #> [195,] 4.312908     0     0     0     0     0     0     0     0     0     0 #> [196,] 4.312871     0     0     0     0     0     0     0     0     0     0 #> [197,] 4.312834     0     0     0     0     0     0     0     0     0     0 #> [198,] 4.312798     0     0     0     0     0     0     0     0     0     0 #> [199,] 4.312761     0     0     0     0     0     0     0     0     0     0 #> [200,] 4.312724     0     0     0     0     0     0     0     0     0     0 #> [201,] 4.312688     0     0     0     0     0     0     0     0     0     0 #> [202,] 4.312651     0     0     0     0     0     0     0     0     0     0 #> [203,] 4.312614     0     0     0     0     0     0     0     0     0     0 #> [204,] 4.312577     0     0     0     0     0     0     0     0     0     0 #> [205,] 4.312541     0     0     0     0     0     0     0     0     0     0 #> [206,] 4.312504     0     0     0     0     0     0     0     0     0     0 #> [207,] 4.312467     0     0     0     0     0     0     0     0     0     0 #> [208,] 4.312430     0     0     0     0     0     0     0     0     0     0 #> [209,] 4.312394     0     0     0     0     0     0     0     0     0     0 #> [210,] 4.312357     0     0     0     0     0     0     0     0     0     0 #> [211,] 4.312320     0     0     0     0     0     0     0     0     0     0 #> [212,] 4.312284     0     0     0     0     0     0     0     0     0     0 #> [213,] 4.312247     0     0     0     0     0     0     0     0     0     0 #> [214,] 4.312210     0     0     0     0     0     0     0     0     0     0 #> [215,] 4.312173     0     0     0     0     0     0     0     0     0     0 #> [216,] 4.312137     0     0     0     0     0     0     0     0     0     0 #> [217,] 4.312100     0     0     0     0     0     0     0     0     0     0 #> [218,] 4.312063     0     0     0     0     0     0     0     0     0     0 #> [219,] 4.312027     0     0     0     0     0     0     0     0     0     0 #> [220,] 4.311990     0     0     0     0     0     0     0     0     0     0 #> [221,] 4.311953     0     0     0     0     0     0     0     0     0     0 #> [222,] 4.311916     0     0     0     0     0     0     0     0     0     0 #> [223,] 4.311880     0     0     0     0     0     0     0     0     0     0 #> [224,] 4.311843     0     0     0     0     0     0     0     0     0     0 #> [225,] 4.311806     0     0     0     0     0     0     0     0     0     0 #> [226,] 4.311770     0     0     0     0     0     0     0     0     0     0 #> [227,] 4.311733     0     0     0     0     0     0     0     0     0     0 #> [228,] 4.311696     0     0     0     0     0     0     0     0     0     0 #> [229,] 4.311660     0     0     0     0     0     0     0     0     0     0 #> [230,] 4.311623     0     0     0     0     0     0     0     0     0     0 #> [231,] 4.311586     0     0     0     0     0     0     0     0     0     0 #> [232,] 4.311549     0     0     0     0     0     0     0     0     0     0 #> [233,] 4.311513     0     0     0     0     0     0     0     0     0     0 #> [234,] 4.311476     0     0     0     0     0     0     0     0     0     0 #> [235,] 4.311439     0     0     0     0     0     0     0     0     0     0 #> [236,] 4.311403     0     0     0     0     0     0     0     0     0     0 #> [237,] 4.311366     0     0     0     0     0     0     0     0     0     0 #> [238,] 4.311329     0     0     0     0     0     0     0     0     0     0 #> [239,] 4.311292     0     0     0     0     0     0     0     0     0     0 #> [240,] 4.311256     0     0     0     0     0     0     0     0     0     0 #> [241,] 4.311219     0     0     0     0     0     0     0     0     0     0 #> [242,] 4.311182     0     0     0     0     0     0     0     0     0     0 #> [243,] 4.311146     0     0     0     0     0     0     0     0     0     0 #> [244,] 4.311109     0     0     0     0     0     0     0     0     0     0 #> [245,] 4.311072     0     0     0     0     0     0     0     0     0     0 #> [246,] 4.311036     0     0     0     0     0     0     0     0     0     0 #> [247,] 4.310999     0     0     0     0     0     0     0     0     0     0 #> [248,] 4.310962     0     0     0     0     0     0     0     0     0     0 #> [249,] 4.310925     0     0     0     0     0     0     0     0     0     0 #> [250,] 4.310889     0     0     0     0     0     0     0     0     0     0 #> [251,] 4.310852     0     0     0     0     0     0     0     0     0     0 #> [252,] 4.310815     0     0     0     0     0     0     0     0     0     0 #> [253,] 4.310779     0     0     0     0     0     0     0     0     0     0 #> [254,] 4.310742     0     0     0     0     0     0     0     0     0     0 #> [255,] 4.310705     0     0     0     0     0     0     0     0     0     0 #> [256,] 4.310668     0     0     0     0     0     0     0     0     0     0 #> [257,] 4.310632     0     0     0     0     0     0     0     0     0     0 #> [258,] 4.310595     0     0     0     0     0     0     0     0     0     0 #> [259,] 4.310558     0     0     0     0     0     0     0     0     0     0 #> [260,] 4.310522     0     0     0     0     0     0     0     0     0     0 #> [261,] 4.310485     0     0     0     0     0     0     0     0     0     0 #> [262,] 4.310448     0     0     0     0     0     0     0     0     0     0 #> [263,] 4.310412     0     0     0     0     0     0     0     0     0     0 #> [264,] 4.310375     0     0     0     0     0     0     0     0     0     0 #> [265,] 4.310338     0     0     0     0     0     0     0     0     0     0 #> [266,] 4.310301     0     0     0     0     0     0     0     0     0     0 #> [267,] 4.310265     0     0     0     0     0     0     0     0     0     0 #> [268,] 4.310228     0     0     0     0     0     0     0     0     0     0 #> [269,] 4.310191     0     0     0     0     0     0     0     0     0     0 #> [270,] 4.310155     0     0     0     0     0     0     0     0     0     0 #> [271,] 4.310118     0     0     0     0     0     0     0     0     0     0 #> [272,] 4.310081     0     0     0     0     0     0     0     0     0     0 #> [273,] 4.310045     0     0     0     0     0     0     0     0     0     0 #> [274,] 4.310008     0     0     0     0     0     0     0     0     0     0 #> [275,] 4.309971     0     0     0     0     0     0     0     0     0     0 #> [276,] 4.309935     0     0     0     0     0     0     0     0     0     0 #> [277,] 4.309898     0     0     0     0     0     0     0     0     0     0 #> [278,] 4.309861     0     0     0     0     0     0     0     0     0     0 #> [279,] 4.309824     0     0     0     0     0     0     0     0     0     0 #> [280,] 4.309788     0     0     0     0     0     0     0     0     0     0 #> [281,] 4.309751     0     0     0     0     0     0     0     0     0     0 #> [282,] 4.309714     0     0     0     0     0     0     0     0     0     0 #> [283,] 4.309678     0     0     0     0     0     0     0     0     0     0 #> [284,] 4.309641     0     0     0     0     0     0     0     0     0     0 #> [285,] 4.309604     0     0     0     0     0     0     0     0     0     0 #> [286,] 4.309568     0     0     0     0     0     0     0     0     0     0 #> [287,] 4.309531     0     0     0     0     0     0     0     0     0     0 #> [288,] 4.309494     0     0     0     0     0     0     0     0     0     0 #> [289,] 4.309458     0     0     0     0     0     0     0     0     0     0 #> [290,] 4.309421     0     0     0     0     0     0     0     0     0     0 #> [291,] 4.309384     0     0     0     0     0     0     0     0     0     0 #> [292,] 4.309347     0     0     0     0     0     0     0     0     0     0 #> [293,] 4.309311     0     0     0     0     0     0     0     0     0     0 #> [294,] 4.309274     0     0     0     0     0     0     0     0     0     0 #> [295,] 4.309237     0     0     0     0     0     0     0     0     0     0 #> [296,] 4.309201     0     0     0     0     0     0     0     0     0     0 #> [297,] 4.309164     0     0     0     0     0     0     0     0     0     0 #> [298,] 4.309127     0     0     0     0     0     0     0     0     0     0 #> [299,] 4.309091     0     0     0     0     0     0     0     0     0     0 #> [300,] 4.309054     0     0     0     0     0     0     0     0     0     0 #> [301,] 4.309017     0     0     0     0     0     0     0     0     0     0 #> [302,] 4.308981     0     0     0     0     0     0     0     0     0     0 #> [303,] 4.308944     0     0     0     0     0     0     0     0     0     0 #> [304,] 4.308907     0     0     0     0     0     0     0     0     0     0 #> [305,] 4.308871     0     0     0     0     0     0     0     0     0     0 #> [306,] 4.308834     0     0     0     0     0     0     0     0     0     0 #> [307,] 4.308797     0     0     0     0     0     0     0     0     0     0 #> [308,] 4.308760     0     0     0     0     0     0     0     0     0     0 #> [309,] 4.330628     0     0     0     0     0     0     0     0     0     0 #> [310,] 4.330591     0     0     0     0     0     0     0     0     0     0 #> [311,] 4.330554     0     0     0     0     0     0     0     0     0     0 #> [312,] 4.330517     0     0     0     0     0     0     0     0     0     0 #> [313,] 4.330480     0     0     0     0     0     0     0     0     0     0 #> [314,] 4.330443     0     0     0     0     0     0     0     0     0     0 #> [315,] 4.330406     0     0     0     0     0     0     0     0     0     0 #> [316,] 4.330369     0     0     0     0     0     0     0     0     0     0 #> [317,] 4.330333     0     0     0     0     0     0     0     0     0     0 #> [318,] 4.330296     0     0     0     0     0     0     0     0     0     0 #> [319,] 4.330259     0     0     0     0     0     0     0     0     0     0 #> [320,] 4.330222     0     0     0     0     0     0     0     0     0     0 #> [321,] 4.330185     0     0     0     0     0     0     0     0     0     0 #> [322,] 4.332525     0     0     0     0     0     0     0     0     0     0 #> [323,] 4.332488     0     0     0     0     0     0     0     0     0     0 #> [324,] 4.332451     0     0     0     0     0     0     0     0     0     0 #> [325,] 4.332414     0     0     0     0     0     0     0     0     0     0 #> [326,] 4.332378     0     0     0     0     0     0     0     0     0     0 #> [327,] 4.332341     0     0     0     0     0     0     0     0     0     0 #> [328,] 4.332304     0     0     0     0     0     0     0     0     0     0 #> [329,] 4.332267     0     0     0     0     0     0     0     0     0     0 #> [330,] 4.332230     0     0     0     0     0     0     0     0     0     0 #> [331,] 4.332193     0     0     0     0     0     0     0     0     0     0 #> [332,] 4.332156     0     0     0     0     0     0     0     0     0     0 #> [333,] 4.332119     0     0     0     0     0     0     0     0     0     0 #> [334,] 4.332083     0     0     0     0     0     0     0     0     0     0 #> [335,] 4.332046     0     0     0     0     0     0     0     0     0     0 #> [336,] 4.332009     0     0     0     0     0     0     0     0     0     0 #> [337,] 4.331972     0     0     0     0     0     0     0     0     0     0 #> [338,] 4.331935     0     0     0     0     0     0     0     0     0     0 #> [339,] 4.331898     0     0     0     0     0     0     0     0     0     0 #> [340,] 4.331861     0     0     0     0     0     0     0     0     0     0 #> [341,] 4.331824     0     0     0     0     0     0     0     0     0     0 #> [342,] 4.331787     0     0     0     0     0     0     0     0     0     0 #> [343,] 4.331751     0     0     0     0     0     0     0     0     0     0 #> [344,] 4.331714     0     0     0     0     0     0     0     0     0     0 #> [345,] 4.331677     0     0     0     0     0     0     0     0     0     0 #> [346,] 4.331640     0     0     0     0     0     0     0     0     0     0 #> [347,] 4.331603     0     0     0     0     0     0     0     0     0     0 #> [348,] 4.331566     0     0     0     0     0     0     0     0     0     0 #> [349,] 4.331529     0     0     0     0     0     0     0     0     0     0 #> [350,] 4.331492     0     0     0     0     0     0     0     0     0     0 #> [351,] 4.331456     0     0     0     0     0     0     0     0     0     0 #> [352,] 4.331419     0     0     0     0     0     0     0     0     0     0 #> [353,] 4.331382     0     0     0     0     0     0     0     0     0     0 #> [354,] 4.331345     0     0     0     0     0     0     0     0     0     0 #> [355,] 4.331308     0     0     0     0     0     0     0     0     0     0 #> [356,] 4.331271     0     0     0     0     0     0     0     0     0     0 #> [357,] 4.331234     0     0     0     0     0     0     0     0     0     0 #> [358,] 4.331197     0     0     0     0     0     0     0     0     0     0 #> [359,] 4.331161     0     0     0     0     0     0     0     0     0     0 #> [360,] 4.331124     0     0     0     0     0     0     0     0     0     0 #> [361,] 4.349362     0     0     0     0     0     0     0     0     0     0 #> [362,] 4.349325     0     0     0     0     0     0     0     0     0     0 #> [363,] 4.349288     0     0     0     0     0     0     0     0     0     0 #> [364,] 4.349250     0     0     0     0     0     0     0     0     0     0 #> [365,] 4.349213     0     0     0     0     0     0     0     0     0     0 #> [366,] 4.349176     0     0     0     0     0     0     0     0     0     0 #> [367,] 4.349139     0     0     0     0     0     0     0     0     0     0 #> [368,] 4.349102     0     0     0     0     0     0     0     0     0     0 #> [369,] 4.349065     0     0     0     0     0     0     0     0     0     0 #> [370,] 4.349028     0     0     0     0     0     0     0     0     0     0 #> [371,] 4.348991     0     0     0     0     0     0     0     0     0     0 #> [372,] 4.348954     0     0     0     0     0     0     0     0     0     0 #> [373,] 4.348917     0     0     0     0     0     0     0     0     0     0 #> [374,] 4.348880     0     0     0     0     0     0     0     0     0     0 #> [375,] 4.346400     0     0     0     0     0     0     0     0     0     0 #> [376,] 4.346363     0     0     0     0     0     0     0     0     0     0 #> [377,] 4.346326     0     0     0     0     0     0     0     0     0     0 #> [378,] 4.346289     0     0     0     0     0     0     0     0     0     0 #> [379,] 4.346252     0     0     0     0     0     0     0     0     0     0 #> [380,] 4.346215     0     0     0     0     0     0     0     0     0     0 #> [381,] 4.346178     0     0     0     0     0     0     0     0     0     0 #> [382,] 4.346141     0     0     0     0     0     0     0     0     0     0 #> [383,] 4.346104     0     0     0     0     0     0     0     0     0     0 #> [384,] 4.346067     0     0     0     0     0     0     0     0     0     0 #> [385,] 4.346030     0     0     0     0     0     0     0     0     0     0 #> [386,] 4.345993     0     0     0     0     0     0     0     0     0     0 #> [387,] 4.345956     0     0     0     0     0     0     0     0     0     0 #> [388,] 4.345919     0     0     0     0     0     0     0     0     0     0 #> [389,] 4.345882     0     0     0     0     0     0     0     0     0     0 #> [390,] 4.345845     0     0     0     0     0     0     0     0     0     0 #> [391,] 4.345808     0     0     0     0     0     0     0     0     0     0 #> [392,] 4.345771     0     0     0     0     0     0     0     0     0     0 #> [393,] 4.345734     0     0     0     0     0     0     0     0     0     0 #> [394,] 4.345697     0     0     0     0     0     0     0     0     0     0 #> [395,] 4.345660     0     0     0     0     0     0     0     0     0     0 #> [396,] 4.345623     0     0     0     0     0     0     0     0     0     0 #> [397,] 4.345586     0     0     0     0     0     0     0     0     0     0 #> [398,] 4.345549     0     0     0     0     0     0     0     0     0     0 #> [399,] 4.345512     0     0     0     0     0     0     0     0     0     0 #> [400,] 4.345475     0     0     0     0     0     0     0     0     0     0 #> [401,] 4.345438     0     0     0     0     0     0     0     0     0     0 #> [402,] 4.345401     0     0     0     0     0     0     0     0     0     0 #> [403,] 4.345364     0     0     0     0     0     0     0     0     0     0 #> [404,] 4.345327     0     0     0     0     0     0     0     0     0     0 #> [405,] 4.345290     0     0     0     0     0     0     0     0     0     0 #> [406,] 4.345253     0     0     0     0     0     0     0     0     0     0 #> [407,] 4.345216     0     0     0     0     0     0     0     0     0     0 #> [408,] 4.345179     0     0     0     0     0     0     0     0     0     0 #> [409,] 4.345142     0     0     0     0     0     0     0     0     0     0 #> [410,] 4.345105     0     0     0     0     0     0     0     0     0     0 #> [411,] 4.345068     0     0     0     0     0     0     0     0     0     0 #> [412,] 4.345031     0     0     0     0     0     0     0     0     0     0 #> [413,] 4.344994     0     0     0     0     0     0     0     0     0     0 #> [414,] 4.344957     0     0     0     0     0     0     0     0     0     0 #> [415,] 4.344920     0     0     0     0     0     0     0     0     0     0 #> [416,] 4.344883     0     0     0     0     0     0     0     0     0     0 #> [417,] 4.344846     0     0     0     0     0     0     0     0     0     0 #> [418,] 4.344809     0     0     0     0     0     0     0     0     0     0 #> [419,] 4.344772     0     0     0     0     0     0     0     0     0     0 #> [420,] 4.344735     0     0     0     0     0     0     0     0     0     0 #> [421,] 4.344698     0     0     0     0     0     0     0     0     0     0 #> [422,] 4.344661     0     0     0     0     0     0     0     0     0     0 #> [423,] 4.344624     0     0     0     0     0     0     0     0     0     0 #> [424,] 4.344587     0     0     0     0     0     0     0     0     0     0 #> [425,] 4.344550     0     0     0     0     0     0     0     0     0     0 #> [426,] 4.344513     0     0     0     0     0     0     0     0     0     0 #> [427,] 4.344476     0     0     0     0     0     0     0     0     0     0 #> [428,] 4.344439     0     0     0     0     0     0     0     0     0     0 #> [429,] 4.344402     0     0     0     0     0     0     0     0     0     0 #> [430,] 4.344365     0     0     0     0     0     0     0     0     0     0 #> [431,] 4.344328     0     0     0     0     0     0     0     0     0     0 #> [432,] 4.344291     0     0     0     0     0     0     0     0     0     0 #> [433,] 4.344254     0     0     0     0     0     0     0     0     0     0 #> [434,] 4.344217     0     0     0     0     0     0     0     0     0     0 #> [435,] 4.344180     0     0     0     0     0     0     0     0     0     0 #> [436,] 4.344143     0     0     0     0     0     0     0     0     0     0 #> [437,] 4.344106     0     0     0     0     0     0     0     0     0     0 #> [438,] 4.344069     0     0     0     0     0     0     0     0     0     0 #> [439,] 4.344032     0     0     0     0     0     0     0     0     0     0 #> [440,] 4.343995     0     0     0     0     0     0     0     0     0     0 #> [441,] 4.343958     0     0     0     0     0     0     0     0     0     0 #> [442,] 4.343922     0     0     0     0     0     0     0     0     0     0 #> [443,] 4.343885     0     0     0     0     0     0     0     0     0     0 #> [444,] 4.343848     0     0     0     0     0     0     0     0     0     0 #> [445,] 4.343811     0     0     0     0     0     0     0     0     0     0 #> [446,] 4.343774     0     0     0     0     0     0     0     0     0     0 #> [447,] 4.343737     0     0     0     0     0     0     0     0     0     0 #> [448,] 4.343700     0     0     0     0     0     0     0     0     0     0 #> [449,] 4.343663     0     0     0     0     0     0     0     0     0     0 #> [450,] 4.343626     0     0     0     0     0     0     0     0     0     0 #> [451,] 4.343589     0     0     0     0     0     0     0     0     0     0 #> [452,] 4.343552     0     0     0     0     0     0     0     0     0     0 #> [453,] 4.343515     0     0     0     0     0     0     0     0     0     0 #> [454,] 4.343478     0     0     0     0     0     0     0     0     0     0 #> [455,] 4.343441     0     0     0     0     0     0     0     0     0     0 #> [456,] 4.343404     0     0     0     0     0     0     0     0     0     0 #> [457,] 4.343367     0     0     0     0     0     0     0     0     0     0 #> [458,] 4.343330     0     0     0     0     0     0     0     0     0     0 #> [459,] 4.343293     0     0     0     0     0     0     0     0     0     0 #> [460,] 4.398617     0     0     0     0     0     0     0     0     0     0 #> [461,] 4.398579     0     0     0     0     0     0     0     0     0     0 #> [462,] 4.414642     0     0     0     0     0     0     0     0     0     0 #> [463,] 4.414679     0     0     0     0     0     0     0     0     0     0 #> [464,] 4.414717     0     0     0     0     0     0     0     0     0     0 #> [465,] 4.414754     0     0     0     0     0     0     0     0     0     0 #> [466,] 4.414792     0     0     0     0     0     0     0     0     0     0 #> [467,] 4.414830     0     0     0     0     0     0     0     0     0     0 #> [468,] 4.343256     0     0     0     0     0     0     0     0     0     0 #> [469,] 4.343219     0     0     0     0     0     0     0     0     0     0 #> [470,] 4.343182     0     0     0     0     0     0     0     0     0     0 #> [471,] 4.414867     0     0     0     0     0     0     0     0     0     0 #> [472,] 4.414905     0     0     0     0     0     0     0     0     0     0 #> [473,] 4.414942     0     0     0     0     0     0     0     0     0     0 #> [474,] 4.414980     0     0     0     0     0     0     0     0     0     0 #> [475,] 4.415018     0     0     0     0     0     0     0     0     0     0 #> [476,] 4.415055     0     0     0     0     0     0     0     0     0     0 #> [477,] 4.415093     0     0     0     0     0     0     0     0     0     0 #> [478,] 4.415130     0     0     0     0     0     0     0     0     0     0 #> [479,] 4.415168     0     0     0     0     0     0     0     0     0     0 #> [480,] 4.415205     0     0     0     0     0     0     0     0     0     0 #> [481,] 4.415243     0     0     0     0     0     0     0     0     0     0 #> [482,] 4.415281     0     0     0     0     0     0     0     0     0     0 #> [483,] 4.415318     0     0     0     0     0     0     0     0     0     0 #> [484,] 4.415356     0     0     0     0     0     0     0     0     0     0 #> [485,] 4.415393     0     0     0     0     0     0     0     0     0     0 #> [486,] 4.415431     0     0     0     0     0     0     0     0     0     0 #> [487,] 4.415469     0     0     0     0     0     0     0     0     0     0 #> [488,] 4.415433     0     0     0     0     0     0     0     0     0     0 #> [489,] 4.415395     0     0     0     0     0     0     0     0     0     0 #> [490,] 4.415357     0     0     0     0     0     0     0     0     0     0 #> [491,] 4.415320     0     0     0     0     0     0     0     0     0     0 #> [492,] 4.415282     0     0     0     0     0     0     0     0     0     0 #> [493,] 4.415245     0     0     0     0     0     0     0     0     0     0 #> [494,] 4.415207     0     0     0     0     0     0     0     0     0     0 #> [495,] 4.415169     0     0     0     0     0     0     0     0     0     0 #> [496,] 4.415132     0     0     0     0     0     0     0     0     0     0 #> [497,] 4.415094     0     0     0     0     0     0     0     0     0     0 #> [498,] 4.415057     0     0     0     0     0     0     0     0     0     0 #> [499,] 4.415019     0     0     0     0     0     0     0     0     0     0 #> [500,] 4.414981     0     0     0     0     0     0     0     0     0     0 #> [501,] 4.414944     0     0     0     0     0     0     0     0     0     0 #> [502,] 4.414906     0     0     0     0     0     0     0     0     0     0 #> [503,] 4.414869     0     0     0     0     0     0     0     0     0     0 #> [504,] 4.414831     0     0     0     0     0     0     0     0     0     0 #> [505,] 4.414793     0     0     0     0     0     0     0     0     0     0 #> [506,] 4.414756     0     0     0     0     0     0     0     0     0     0 #> [507,] 4.414718     0     0     0     0     0     0     0     0     0     0 #> [508,] 4.414681     0     0     0     0     0     0     0     0     0     0 #> [509,] 4.308724     0     0     0     0     0     0     0     0     0     0 #> [510,] 4.308687     0     0     0     0     0     0     0     0     0     0 #> [511,] 4.308650     0     0     0     0     0     0     0     0     0     0 #> [512,] 4.308614     0     0     0     0     0     0     0     0     0     0 #> [513,] 4.308577     0     0     0     0     0     0     0     0     0     0 #> [514,] 4.308540     0     0     0     0     0     0     0     0     0     0 #> [515,] 4.308504     0     0     0     0     0     0     0     0     0     0 #> [516,] 4.308467     0     0     0     0     0     0     0     0     0     0 #> [517,] 4.308430     0     0     0     0     0     0     0     0     0     0 #> [518,] 4.308394     0     0     0     0     0     0     0     0     0     0 #> [519,] 4.331070     0     0     0     0     0     0     0     0     0     0 #> [520,] 4.331033     0     0     0     0     0     0     0     0     0     0 #> [521,] 4.330996     0     0     0     0     0     0     0     0     0     0 #> [522,] 4.330959     0     0     0     0     0     0     0     0     0     0 #> [523,] 4.330923     0     0     0     0     0     0     0     0     0     0 #> [524,] 4.330886     0     0     0     0     0     0     0     0     0     0 #> [525,] 4.330849     0     0     0     0     0     0     0     0     0     0 #> [526,] 4.330812     0     0     0     0     0     0     0     0     0     0 #> [527,] 4.330775     0     0     0     0     0     0     0     0     0     0 #> [528,] 4.330738     0     0     0     0     0     0     0     0     0     0 #> [529,] 4.330701     0     0     0     0     0     0     0     0     0     0 #> [530,] 4.330664     0     0     0     0     0     0     0     0     0     0 #> [531,] 4.348843     0     0     0     0     0     0     0     0     0     0 #> [532,] 4.348806     0     0     0     0     0     0     0     0     0     0 #> [533,] 4.348769     0     0     0     0     0     0     0     0     0     0 #> [534,] 4.348732     0     0     0     0     0     0     0     0     0     0 #> [535,] 4.348695     0     0     0     0     0     0     0     0     0     0 #> [536,] 4.348658     0     0     0     0     0     0     0     0     0     0 #> [537,] 4.348621     0     0     0     0     0     0     0     0     0     0 #> [538,] 4.348584     0     0     0     0     0     0     0     0     0     0 #> [539,] 4.348547     0     0     0     0     0     0     0     0     0     0 #> [540,] 4.348510     0     0     0     0     0     0     0     0     0     0 #> [541,] 4.348473     0     0     0     0     0     0     0     0     0     0 #> [542,] 4.348436     0     0     0     0     0     0     0     0     0     0 #> [543,] 4.348399     0     0     0     0     0     0     0     0     0     0 #> [544,] 4.348362     0     0     0     0     0     0     0     0     0     0 #> [545,] 4.348325     0     0     0     0     0     0     0     0     0     0 #> [546,] 4.348288     0     0     0     0     0     0     0     0     0     0 #> [547,] 4.348251     0     0     0     0     0     0     0     0     0     0 #> [548,] 4.348214     0     0     0     0     0     0     0     0     0     0 #> [549,] 4.348177     0     0     0     0     0     0     0     0     0     0 #> [550,] 4.348140     0     0     0     0     0     0     0     0     0     0 #> [551,] 4.348103     0     0     0     0     0     0     0     0     0     0 #> [552,] 4.348066     0     0     0     0     0     0     0     0     0     0 #> [553,] 4.348029     0     0     0     0     0     0     0     0     0     0 #> [554,] 4.347992     0     0     0     0     0     0     0     0     0     0 #> [555,] 4.347955     0     0     0     0     0     0     0     0     0     0 #> [556,] 4.347918     0     0     0     0     0     0     0     0     0     0 #> [557,] 4.347881     0     0     0     0     0     0     0     0     0     0 #> [558,] 4.347844     0     0     0     0     0     0     0     0     0     0 #> [559,] 4.347807     0     0     0     0     0     0     0     0     0     0 #> [560,] 4.347770     0     0     0     0     0     0     0     0     0     0 #> [561,] 4.347733     0     0     0     0     0     0     0     0     0     0 #> [562,] 4.347696     0     0     0     0     0     0     0     0     0     0 #> [563,] 4.347659     0     0     0     0     0     0     0     0     0     0 #> [564,] 4.347621     0     0     0     0     0     0     0     0     0     0 #> [565,] 4.347584     0     0     0     0     0     0     0     0     0     0 #> [566,] 4.347547     0     0     0     0     0     0     0     0     0     0 #> [567,] 4.347510     0     0     0     0     0     0     0     0     0     0 #> [568,] 4.347473     0     0     0     0     0     0     0     0     0     0 #> [569,] 4.347436     0     0     0     0     0     0     0     0     0     0 #> [570,] 4.347399     0     0     0     0     0     0     0     0     0     0 #> [571,] 4.347362     0     0     0     0     0     0     0     0     0     0 #> [572,] 4.347325     0     0     0     0     0     0     0     0     0     0 #> [573,] 4.347288     0     0     0     0     0     0     0     0     0     0 #> [574,] 4.347251     0     0     0     0     0     0     0     0     0     0 #> [575,] 4.347214     0     0     0     0     0     0     0     0     0     0 #> [576,] 4.347177     0     0     0     0     0     0     0     0     0     0 #> [577,] 4.347140     0     0     0     0     0     0     0     0     0     0 #> [578,] 4.347103     0     0     0     0     0     0     0     0     0     0 #> [579,] 4.347066     0     0     0     0     0     0     0     0     0     0 #> [580,] 4.347029     0     0     0     0     0     0     0     0     0     0 #> [581,] 4.346992     0     0     0     0     0     0     0     0     0     0 #> [582,] 4.346955     0     0     0     0     0     0     0     0     0     0 #> [583,] 4.346918     0     0     0     0     0     0     0     0     0     0 #> [584,] 4.346881     0     0     0     0     0     0     0     0     0     0 #> [585,] 4.346844     0     0     0     0     0     0     0     0     0     0 #> [586,] 4.346807     0     0     0     0     0     0     0     0     0     0 #> [587,] 4.346770     0     0     0     0     0     0     0     0     0     0 #> [588,] 4.346733     0     0     0     0     0     0     0     0     0     0 #> [589,] 4.346696     0     0     0     0     0     0     0     0     0     0 #> [590,] 4.346659     0     0     0     0     0     0     0     0     0     0 #> [591,] 4.346622     0     0     0     0     0     0     0     0     0     0 #> [592,] 4.346585     0     0     0     0     0     0     0     0     0     0 #> [593,] 4.346548     0     0     0     0     0     0     0     0     0     0 #> [594,] 4.346511     0     0     0     0     0     0     0     0     0     0 #> [595,] 4.346474     0     0     0     0     0     0     0     0     0     0 #> [596,] 4.346437     0     0     0     0     0     0     0     0     0     0 #> [597,] 4.343145     0     0     0     0     0     0     0     0     0     0 #> [598,] 4.343108     0     0     0     0     0     0     0     0     0     0 #> [599,] 4.343071     0     0     0     0     0     0     0     0     0     0 #> [600,] 4.343034     0     0     0     0     0     0     0     0     0     0 #> [601,] 4.342997     0     0     0     0     0     0     0     0     0     0 #> [602,] 4.342960     0     0     0     0     0     0     0     0     0     0 #> [603,] 4.342923     0     0     0     0     0     0     0     0     0     0 #> [604,] 4.342886     0     0     0     0     0     0     0     0     0     0 #> [605,] 4.342849     0     0     0     0     0     0     0     0     0     0 #> [606,] 4.342812     0     0     0     0     0     0     0     0     0     0 #> [607,] 4.342775     0     0     0     0     0     0     0     0     0     0 #> [608,] 4.342738     0     0     0     0     0     0     0     0     0     0 #> [609,] 4.342701     0     0     0     0     0     0     0     0     0     0 #> [610,] 4.342664     0     0     0     0     0     0     0     0     0     0 #> [611,] 4.342627     0     0     0     0     0     0     0     0     0     0 #> [612,] 4.342590     0     0     0     0     0     0     0     0     0     0 #> [613,] 4.342553     0     0     0     0     0     0     0     0     0     0 #> [614,] 4.342516     0     0     0     0     0     0     0     0     0     0 #> [615,] 4.342479     0     0     0     0     0     0     0     0     0     0 #> [616,] 4.400078     0     0     0     0     0     0     0     0     0     0 #> [617,] 4.400040     0     0     0     0     0     0     0     0     0     0 #> [618,] 4.400003     0     0     0     0     0     0     0     0     0     0 #> [619,] 4.399965     0     0     0     0     0     0     0     0     0     0 #> [620,] 4.399928     0     0     0     0     0     0     0     0     0     0 #> [621,] 4.399890     0     0     0     0     0     0     0     0     0     0 #> [622,] 4.399853     0     0     0     0     0     0     0     0     0     0 #> [623,] 4.399815     0     0     0     0     0     0     0     0     0     0 #> [624,] 4.399778     0     0     0     0     0     0     0     0     0     0 #> [625,] 4.399740     0     0     0     0     0     0     0     0     0     0 #> [626,] 4.399703     0     0     0     0     0     0     0     0     0     0 #> [627,] 4.399665     0     0     0     0     0     0     0     0     0     0 #> [628,] 4.399628     0     0     0     0     0     0     0     0     0     0 #> [629,] 4.399601     0     0     0     0     0     0     0     0     0     0 #> [630,] 4.399553     0     0     0     0     0     0     0     0     0     0 #> [631,] 4.399516     0     0     0     0     0     0     0     0     0     0 #> [632,] 4.399478     0     0     0     0     0     0     0     0     0     0 #> [633,] 4.399441     0     0     0     0     0     0     0     0     0     0 #> [634,] 4.399403     0     0     0     0     0     0     0     0     0     0 #> [635,] 4.399366     0     0     0     0     0     0     0     0     0     0 #> [636,] 4.399328     0     0     0     0     0     0     0     0     0     0 #> [637,] 4.399291     0     0     0     0     0     0     0     0     0     0 #> [638,] 4.399253     0     0     0     0     0     0     0     0     0     0 #> [639,] 4.399216     0     0     0     0     0     0     0     0     0     0 #> [640,] 4.399179     0     0     0     0     0     0     0     0     0     0 #> [641,] 4.399141     0     0     0     0     0     0     0     0     0     0 #> [642,] 4.399104     0     0     0     0     0     0     0     0     0     0 #> [643,] 4.399066     0     0     0     0     0     0     0     0     0     0 #> [644,] 4.399029     0     0     0     0     0     0     0     0     0     0 #> [645,] 4.398991     0     0     0     0     0     0     0     0     0     0 #> [646,] 4.398954     0     0     0     0     0     0     0     0     0     0 #> [647,] 4.398916     0     0     0     0     0     0     0     0     0     0 #> [648,] 4.398879     0     0     0     0     0     0     0     0     0     0 #> [649,] 4.398841     0     0     0     0     0     0     0     0     0     0 #> [650,] 4.398804     0     0     0     0     0     0     0     0     0     0 #> [651,] 4.398767     0     0     0     0     0     0     0     0     0     0 #> [652,] 4.398729     0     0     0     0     0     0     0     0     0     0 #> [653,] 4.398692     0     0     0     0     0     0     0     0     0     0 #> [654,] 4.398654     0     0     0     0     0     0     0     0     0     0 #>        [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28]    [,29] [,30] #>   [1,]     0     0     0     0     0     0     0     0     0 3.892687     0 #>   [2,]     0     0     0     0     0     0     0     0     0 3.892654     0 #>   [3,]     0     0     0     0     0     0     0     0     0 3.892621     0 #>   [4,]     0     0     0     0     0     0     0     0     0 3.892588     0 #>   [5,]     0     0     0     0     0     0     0     0     0 3.892555     0 #>   [6,]     0     0     0     0     0     0     0     0     0 3.892522     0 #>   [7,]     0     0     0     0     0     0     0     0     0 3.892489     0 #>   [8,]     0     0     0     0     0     0     0     0     0 3.892455     0 #>   [9,]     0     0     0     0     0     0     0     0     0 3.892422     0 #>  [10,]     0     0     0     0     0     0     0     0     0 3.892389     0 #>  [11,]     0     0     0     0     0     0     0     0     0 3.892356     0 #>  [12,]     0     0     0     0     0     0     0     0     0 3.892323     0 #>  [13,]     0     0     0     0     0     0     0     0     0 3.892290     0 #>  [14,]     0     0     0     0     0     0     0     0     0 3.892257     0 #>  [15,]     0     0     0     0     0     0     0     0     0 3.892224     0 #>  [16,]     0     0     0     0     0     0     0     0     0 3.892190     0 #>  [17,]     0     0     0     0     0     0     0     0     0 3.892157     0 #>  [18,]     0     0     0     0     0     0     0     0     0 3.892124     0 #>  [19,]     0     0     0     0     0     0     0     0     0 3.892091     0 #>  [20,]     0     0     0     0     0     0     0     0     0 3.892058     0 #>  [21,]     0     0     0     0     0     0     0     0     0 3.892025     0 #>  [22,]     0     0     0     0     0     0     0     0     0 3.891992     0 #>  [23,]     0     0     0     0     0     0     0     0     0 3.891958     0 #>  [24,]     0     0     0     0     0     0     0     0     0 3.891925     0 #>  [25,]     0     0     0     0     0     0     0     0     0 3.891892     0 #>  [26,]     0     0     0     0     0     0     0     0     0 3.891859     0 #>  [27,]     0     0     0     0     0     0     0     0     0 3.891826     0 #>  [28,]     0     0     0     0     0     0     0     0     0 3.891793     0 #>  [29,]     0     0     0     0     0     0     0     0     0 3.891760     0 #>  [30,]     0     0     0     0     0     0     0     0     0 3.891726     0 #>  [31,]     0     0     0     0     0     0     0     0     0 3.891693     0 #>  [32,]     0     0     0     0     0     0     0     0     0 3.891660     0 #>  [33,]     0     0     0     0     0     0     0     0     0 3.891627     0 #>  [34,]     0     0     0     0     0     0     0     0     0 3.891594     0 #>  [35,]     0     0     0     0     0     0     0     0     0 3.891561     0 #>  [36,]     0     0     0     0     0     0     0     0     0 3.891528     0 #>  [37,]     0     0     0     0     0     0     0     0     0 3.891495     0 #>  [38,]     0     0     0     0     0     0     0     0     0 3.891461     0 #>  [39,]     0     0     0     0     0     0     0     0     0 3.891428     0 #>  [40,]     0     0     0     0     0     0     0     0     0 3.891395     0 #>  [41,]     0     0     0     0     0     0     0     0     0 3.891362     0 #>  [42,]     0     0     0     0     0     0     0     0     0 3.891329     0 #>  [43,]     0     0     0     0     0     0     0     0     0 3.891296     0 #>  [44,]     0     0     0     0     0     0     0     0     0 3.891263     0 #>  [45,]     0     0     0     0     0     0     0     0     0 3.891229     0 #>  [46,]     0     0     0     0     0     0     0     0     0 3.891196     0 #>  [47,]     0     0     0     0     0     0     0     0     0 3.891163     0 #>  [48,]     0     0     0     0     0     0     0     0     0 3.891130     0 #>  [49,]     0     0     0     0     0     0     0     0     0 3.891097     0 #>  [50,]     0     0     0     0     0     0     0     0     0 3.891064     0 #>  [51,]     0     0     0     0     0     0     0     0     0 3.891031     0 #>  [52,]     0     0     0     0     0     0     0     0     0 3.890998     0 #>  [53,]     0     0     0     0     0     0     0     0     0 3.890964     0 #>  [54,]     0     0     0     0     0     0     0     0     0 3.890931     0 #>  [55,]     0     0     0     0     0     0     0     0     0 3.890898     0 #>  [56,]     0     0     0     0     0     0     0     0     0 3.890865     0 #>  [57,]     0     0     0     0     0     0     0     0     0 3.890832     0 #>  [58,]     0     0     0     0     0     0     0     0     0 3.890799     0 #>  [59,]     0     0     0     0     0     0     0     0     0 3.890766     0 #>  [60,]     0     0     0     0     0     0     0     0     0 3.890733     0 #>  [61,]     0     0     0     0     0     0     0     0     0 3.890699     0 #>  [62,]     0     0     0     0     0     0     0     0     0 3.890666     0 #>  [63,]     0     0     0     0     0     0     0     0     0 3.890633     0 #>  [64,]     0     0     0     0     0     0     0     0     0 3.890600     0 #>  [65,]     0     0     0     0     0     0     0     0     0 3.890567     0 #>  [66,]     0     0     0     0     0     0     0     0     0 3.890534     0 #>  [67,]     0     0     0     0     0     0     0     0     0 3.890501     0 #>  [68,]     0     0     0     0     0     0     0     0     0 3.890468     0 #>  [69,]     0     0     0     0     0     0     0     0     0 3.890434     0 #>  [70,]     0     0     0     0     0     0     0     0     0 3.890401     0 #>  [71,]     0     0     0     0     0     0     0     0     0 3.890368     0 #>  [72,]     0     0     0     0     0     0     0     0     0 3.890335     0 #>  [73,]     0     0     0     0     0     0     0     0     0 3.890302     0 #>  [74,]     0     0     0     0     0     0     0     0     0 3.890269     0 #>  [75,]     0     0     0     0     0     0     0     0     0 3.890236     0 #>  [76,]     0     0     0     0     0     0     0     0     0 3.890203     0 #>  [77,]     0     0     0     0     0     0     0     0     0 3.890169     0 #>  [78,]     0     0     0     0     0     0     0     0     0 3.890136     0 #>  [79,]     0     0     0     0     0     0     0     0     0 3.890103     0 #>  [80,]     0     0     0     0     0     0     0     0     0 3.890070     0 #>  [81,]     0     0     0     0     0     0     0     0     0 3.890037     0 #>  [82,]     0     0     0     0     0     0     0     0     0 3.890004     0 #>  [83,]     0     0     0     0     0     0     0     0     0 3.889971     0 #>  [84,]     0     0     0     0     0     0     0     0     0 3.889938     0 #>  [85,]     0     0     0     0     0     0     0     0     0 3.889905     0 #>  [86,]     0     0     0     0     0     0     0     0     0 3.889871     0 #>  [87,]     0     0     0     0     0     0     0     0     0 3.889838     0 #>  [88,]     0     0     0     0     0     0     0     0     0 3.889805     0 #>  [89,]     0     0     0     0     0     0     0     0     0 3.889772     0 #>  [90,]     0     0     0     0     0     0     0     0     0 3.889739     0 #>  [91,]     0     0     0     0     0     0     0     0     0 3.889706     0 #>  [92,]     0     0     0     0     0     0     0     0     0 3.889673     0 #>  [93,]     0     0     0     0     0     0     0     0     0 3.889640     0 #>  [94,]     0     0     0     0     0     0     0     0     0 3.889606     0 #>  [95,]     0     0     0     0     0     0     0     0     0 3.889573     0 #>  [96,]     0     0     0     0     0     0     0     0     0 3.889540     0 #>  [97,]     0     0     0     0     0     0     0     0     0 3.889507     0 #>  [98,]     0     0     0     0     0     0     0     0     0 3.889474     0 #>  [99,]     0     0     0     0     0     0     0     0     0 3.889441     0 #> [100,]     0     0     0     0     0     0     0     0     0 3.889408     0 #> [101,]     0     0     0     0     0     0     0     0     0 3.889375     0 #> [102,]     0     0     0     0     0     0     0     0     0 3.889342     0 #> [103,]     0     0     0     0     0     0     0     0     0 3.889308     0 #> [104,]     0     0     0     0     0     0     0     0     0 3.889275     0 #> [105,]     0     0     0     0     0     0     0     0     0 3.889242     0 #> [106,]     0     0     0     0     0     0     0     0     0 3.889209     0 #> [107,]     0     0     0     0     0     0     0     0     0 3.889176     0 #> [108,]     0     0     0     0     0     0     0     0     0 3.889143     0 #> [109,]     0     0     0     0     0     0     0     0     0 3.889110     0 #> [110,]     0     0     0     0     0     0     0     0     0 3.889077     0 #> [111,]     0     0     0     0     0     0     0     0     0 3.889044     0 #> [112,]     0     0     0     0     0     0     0     0     0 3.889010     0 #> [113,]     0     0     0     0     0     0     0     0     0 3.888977     0 #> [114,]     0     0     0     0     0     0     0     0     0 3.888944     0 #> [115,]     0     0     0     0     0     0     0     0     0 3.888911     0 #> [116,]     0     0     0     0     0     0     0     0     0 3.888878     0 #> [117,]     0     0     0     0     0     0     0     0     0 3.888845     0 #> [118,]     0     0     0     0     0     0     0     0     0 3.888812     0 #> [119,]     0     0     0     0     0     0     0     0     0 3.888779     0 #> [120,]     0     0     0     0     0     0     0     0     0 3.888746     0 #> [121,]     0     0     0     0     0     0     0     0     0 3.888712     0 #> [122,]     0     0     0     0     0     0     0     0     0 3.888679     0 #> [123,]     0     0     0     0     0     0     0     0     0 3.888646     0 #> [124,]     0     0     0     0     0     0     0     0     0 3.888613     0 #> [125,]     0     0     0     0     0     0     0     0     0 3.888580     0 #> [126,]     0     0     0     0     0     0     0     0     0 3.888547     0 #> [127,]     0     0     0     0     0     0     0     0     0 3.888514     0 #> [128,]     0     0     0     0     0     0     0     0     0 3.888481     0 #> [129,]     0     0     0     0     0     0     0     0     0 3.888448     0 #> [130,]     0     0     0     0     0     0     0     0     0 3.888414     0 #> [131,]     0     0     0     0     0     0     0     0     0 3.888381     0 #> [132,]     0     0     0     0     0     0     0     0     0 3.888348     0 #> [133,]     0     0     0     0     0     0     0     0     0 3.888315     0 #> [134,]     0     0     0     0     0     0     0     0     0 3.888282     0 #> [135,]     0     0     0     0     0     0     0     0     0 3.888249     0 #> [136,]     0     0     0     0     0     0     0     0     0 3.888216     0 #> [137,]     0     0     0     0     0     0     0     0     0 3.888183     0 #> [138,]     0     0     0     0     0     0     0     0     0 3.888150     0 #> [139,]     0     0     0     0     0     0     0     0     0 3.888117     0 #> [140,]     0     0     0     0     0     0     0     0     0 3.888083     0 #> [141,]     0     0     0     0     0     0     0     0     0 3.888050     0 #> [142,]     0     0     0     0     0     0     0     0     0 3.888017     0 #> [143,]     0     0     0     0     0     0     0     0     0 3.887984     0 #> [144,]     0     0     0     0     0     0     0     0     0 3.887951     0 #> [145,]     0     0     0     0     0     0     0     0     0 3.887918     0 #> [146,]     0     0     0     0     0     0     0     0     0 3.887885     0 #> [147,]     0     0     0     0     0     0     0     0     0 3.887852     0 #> [148,]     0     0     0     0     0     0     0     0     0 3.887819     0 #> [149,]     0     0     0     0     0     0     0     0     0 3.887786     0 #> [150,]     0     0     0     0     0     0     0     0     0 3.887752     0 #> [151,]     0     0     0     0     0     0     0     0     0 3.887719     0 #> [152,]     0     0     0     0     0     0     0     0     0 3.887686     0 #> [153,]     0     0     0     0     0     0     0     0     0 3.887653     0 #> [154,]     0     0     0     0     0     0     0     0     0 3.887620     0 #> [155,]     0     0     0     0     0     0     0     0     0 3.887587     0 #> [156,]     0     0     0     0     0     0     0     0     0 3.887554     0 #> [157,]     0     0     0     0     0     0     0     0     0 3.887521     0 #> [158,]     0     0     0     0     0     0     0     0     0 3.887488     0 #> [159,]     0     0     0     0     0     0     0     0     0 3.887455     0 #> [160,]     0     0     0     0     0     0     0     0     0 3.887421     0 #> [161,]     0     0     0     0     0     0     0     0     0 3.887388     0 #> [162,]     0     0     0     0     0     0     0     0     0 3.887355     0 #> [163,]     0     0     0     0     0     0     0     0     0 3.887322     0 #> [164,]     0     0     0     0     0     0     0     0     0 3.887289     0 #> [165,]     0     0     0     0     0     0     0     0     0 3.887256     0 #> [166,]     0     0     0     0     0     0     0     0     0 3.887223     0 #> [167,]     0     0     0     0     0     0     0     0     0 3.887190     0 #> [168,]     0     0     0     0     0     0     0     0     0 3.887157     0 #> [169,]     0     0     0     0     0     0     0     0     0 3.887124     0 #> [170,]     0     0     0     0     0     0     0     0     0 3.887090     0 #> [171,]     0     0     0     0     0     0     0     0     0 3.887057     0 #> [172,]     0     0     0     0     0     0     0     0     0 3.887024     0 #> [173,]     0     0     0     0     0     0     0     0     0 3.886991     0 #> [174,]     0     0     0     0     0     0     0     0     0 3.886958     0 #> [175,]     0     0     0     0     0     0     0     0     0 3.886925     0 #> [176,]     0     0     0     0     0     0     0     0     0 3.886892     0 #> [177,]     0     0     0     0     0     0     0     0     0 3.886859     0 #> [178,]     0     0     0     0     0     0     0     0     0 3.886826     0 #> [179,]     0     0     0     0     0     0     0     0     0 3.886793     0 #> [180,]     0     0     0     0     0     0     0     0     0 3.886760     0 #> [181,]     0     0     0     0     0     0     0     0     0 3.886726     0 #> [182,]     0     0     0     0     0     0     0     0     0 3.886693     0 #> [183,]     0     0     0     0     0     0     0     0     0 3.886660     0 #> [184,]     0     0     0     0     0     0     0     0     0 3.886627     0 #> [185,]     0     0     0     0     0     0     0     0     0 3.886594     0 #> [186,]     0     0     0     0     0     0     0     0     0 3.886561     0 #> [187,]     0     0     0     0     0     0     0     0     0 3.886528     0 #> [188,]     0     0     0     0     0     0     0     0     0 3.886495     0 #> [189,]     0     0     0     0     0     0     0     0     0 3.886462     0 #> [190,]     0     0     0     0     0     0     0     0     0 3.886429     0 #> [191,]     0     0     0     0     0     0     0     0     0 3.886396     0 #> [192,]     0     0     0     0     0     0     0     0     0 3.886362     0 #> [193,]     0     0     0     0     0     0     0     0     0 3.886329     0 #> [194,]     0     0     0     0     0     0     0     0     0 3.886296     0 #> [195,]     0     0     0     0     0     0     0     0     0 3.886263     0 #> [196,]     0     0     0     0     0     0     0     0     0 3.886230     0 #> [197,]     0     0     0     0     0     0     0     0     0 3.886197     0 #> [198,]     0     0     0     0     0     0     0     0     0 3.886164     0 #> [199,]     0     0     0     0     0     0     0     0     0 3.886131     0 #> [200,]     0     0     0     0     0     0     0     0     0 3.886098     0 #> [201,]     0     0     0     0     0     0     0     0     0 3.886065     0 #> [202,]     0     0     0     0     0     0     0     0     0 3.886032     0 #> [203,]     0     0     0     0     0     0     0     0     0 3.885999     0 #> [204,]     0     0     0     0     0     0     0     0     0 3.885965     0 #> [205,]     0     0     0     0     0     0     0     0     0 3.885932     0 #> [206,]     0     0     0     0     0     0     0     0     0 3.885899     0 #> [207,]     0     0     0     0     0     0     0     0     0 3.885866     0 #> [208,]     0     0     0     0     0     0     0     0     0 3.885833     0 #> [209,]     0     0     0     0     0     0     0     0     0 3.885800     0 #> [210,]     0     0     0     0     0     0     0     0     0 3.885767     0 #> [211,]     0     0     0     0     0     0     0     0     0 3.885734     0 #> [212,]     0     0     0     0     0     0     0     0     0 3.885701     0 #> [213,]     0     0     0     0     0     0     0     0     0 3.885668     0 #> [214,]     0     0     0     0     0     0     0     0     0 3.885635     0 #> [215,]     0     0     0     0     0     0     0     0     0 3.885601     0 #> [216,]     0     0     0     0     0     0     0     0     0 3.885568     0 #> [217,]     0     0     0     0     0     0     0     0     0 3.885535     0 #> [218,]     0     0     0     0     0     0     0     0     0 3.885502     0 #> [219,]     0     0     0     0     0     0     0     0     0 3.885469     0 #> [220,]     0     0     0     0     0     0     0     0     0 3.885436     0 #> [221,]     0     0     0     0     0     0     0     0     0 3.885403     0 #> [222,]     0     0     0     0     0     0     0     0     0 3.885370     0 #> [223,]     0     0     0     0     0     0     0     0     0 3.885337     0 #> [224,]     0     0     0     0     0     0     0     0     0 3.885304     0 #> [225,]     0     0     0     0     0     0     0     0     0 3.885271     0 #> [226,]     0     0     0     0     0     0     0     0     0 3.885238     0 #> [227,]     0     0     0     0     0     0     0     0     0 3.885205     0 #> [228,]     0     0     0     0     0     0     0     0     0 3.885171     0 #> [229,]     0     0     0     0     0     0     0     0     0 3.885138     0 #> [230,]     0     0     0     0     0     0     0     0     0 3.885105     0 #> [231,]     0     0     0     0     0     0     0     0     0 3.885072     0 #> [232,]     0     0     0     0     0     0     0     0     0 3.885039     0 #> [233,]     0     0     0     0     0     0     0     0     0 3.885006     0 #> [234,]     0     0     0     0     0     0     0     0     0 3.884973     0 #> [235,]     0     0     0     0     0     0     0     0     0 3.884940     0 #> [236,]     0     0     0     0     0     0     0     0     0 3.884907     0 #> [237,]     0     0     0     0     0     0     0     0     0 3.884874     0 #> [238,]     0     0     0     0     0     0     0     0     0 3.884841     0 #> [239,]     0     0     0     0     0     0     0     0     0 3.884808     0 #> [240,]     0     0     0     0     0     0     0     0     0 3.884775     0 #> [241,]     0     0     0     0     0     0     0     0     0 3.884741     0 #> [242,]     0     0     0     0     0     0     0     0     0 3.884708     0 #> [243,]     0     0     0     0     0     0     0     0     0 3.884675     0 #> [244,]     0     0     0     0     0     0     0     0     0 3.884642     0 #> [245,]     0     0     0     0     0     0     0     0     0 3.884609     0 #> [246,]     0     0     0     0     0     0     0     0     0 3.884576     0 #> [247,]     0     0     0     0     0     0     0     0     0 3.884543     0 #> [248,]     0     0     0     0     0     0     0     0     0 3.884510     0 #> [249,]     0     0     0     0     0     0     0     0     0 3.884477     0 #> [250,]     0     0     0     0     0     0     0     0     0 3.884444     0 #> [251,]     0     0     0     0     0     0     0     0     0 3.884411     0 #> [252,]     0     0     0     0     0     0     0     0     0 3.884378     0 #> [253,]     0     0     0     0     0     0     0     0     0 3.884345     0 #> [254,]     0     0     0     0     0     0     0     0     0 3.884312     0 #> [255,]     0     0     0     0     0     0     0     0     0 3.884278     0 #> [256,]     0     0     0     0     0     0     0     0     0 3.884245     0 #> [257,]     0     0     0     0     0     0     0     0     0 3.884212     0 #> [258,]     0     0     0     0     0     0     0     0     0 3.884179     0 #> [259,]     0     0     0     0     0     0     0     0     0 3.884146     0 #> [260,]     0     0     0     0     0     0     0     0     0 3.884113     0 #> [261,]     0     0     0     0     0     0     0     0     0 3.884080     0 #> [262,]     0     0     0     0     0     0     0     0     0 3.884047     0 #> [263,]     0     0     0     0     0     0     0     0     0 3.884014     0 #> [264,]     0     0     0     0     0     0     0     0     0 3.883981     0 #> [265,]     0     0     0     0     0     0     0     0     0 3.883948     0 #> [266,]     0     0     0     0     0     0     0     0     0 3.883915     0 #> [267,]     0     0     0     0     0     0     0     0     0 3.883882     0 #> [268,]     0     0     0     0     0     0     0     0     0 3.883849     0 #> [269,]     0     0     0     0     0     0     0     0     0 3.883815     0 #> [270,]     0     0     0     0     0     0     0     0     0 3.883782     0 #> [271,]     0     0     0     0     0     0     0     0     0 3.883749     0 #> [272,]     0     0     0     0     0     0     0     0     0 3.883716     0 #> [273,]     0     0     0     0     0     0     0     0     0 3.883683     0 #> [274,]     0     0     0     0     0     0     0     0     0 3.883650     0 #> [275,]     0     0     0     0     0     0     0     0     0 3.883617     0 #> [276,]     0     0     0     0     0     0     0     0     0 3.883584     0 #> [277,]     0     0     0     0     0     0     0     0     0 3.883551     0 #> [278,]     0     0     0     0     0     0     0     0     0 3.883518     0 #> [279,]     0     0     0     0     0     0     0     0     0 3.883485     0 #> [280,]     0     0     0     0     0     0     0     0     0 3.883452     0 #> [281,]     0     0     0     0     0     0     0     0     0 3.883419     0 #> [282,]     0     0     0     0     0     0     0     0     0 3.883386     0 #> [283,]     0     0     0     0     0     0     0     0     0 3.883353     0 #> [284,]     0     0     0     0     0     0     0     0     0 3.883320     0 #> [285,]     0     0     0     0     0     0     0     0     0 3.883286     0 #> [286,]     0     0     0     0     0     0     0     0     0 3.883253     0 #> [287,]     0     0     0     0     0     0     0     0     0 3.883220     0 #> [288,]     0     0     0     0     0     0     0     0     0 3.883187     0 #> [289,]     0     0     0     0     0     0     0     0     0 3.883154     0 #> [290,]     0     0     0     0     0     0     0     0     0 3.883121     0 #> [291,]     0     0     0     0     0     0     0     0     0 3.883088     0 #> [292,]     0     0     0     0     0     0     0     0     0 3.883055     0 #> [293,]     0     0     0     0     0     0     0     0     0 3.883022     0 #> [294,]     0     0     0     0     0     0     0     0     0 3.882989     0 #> [295,]     0     0     0     0     0     0     0     0     0 3.882956     0 #> [296,]     0     0     0     0     0     0     0     0     0 3.882923     0 #> [297,]     0     0     0     0     0     0     0     0     0 3.882890     0 #> [298,]     0     0     0     0     0     0     0     0     0 3.882857     0 #> [299,]     0     0     0     0     0     0     0     0     0 3.882824     0 #> [300,]     0     0     0     0     0     0     0     0     0 3.882791     0 #> [301,]     0     0     0     0     0     0     0     0     0 3.882757     0 #> [302,]     0     0     0     0     0     0     0     0     0 3.882724     0 #> [303,]     0     0     0     0     0     0     0     0     0 3.882691     0 #> [304,]     0     0     0     0     0     0     0     0     0 3.882658     0 #> [305,]     0     0     0     0     0     0     0     0     0 3.882625     0 #> [306,]     0     0     0     0     0     0     0     0     0 3.882592     0 #> [307,]     0     0     0     0     0     0     0     0     0 3.882559     0 #> [308,]     0     0     0     0     0     0     0     0     0 3.882526     0 #> [309,]     0     0     0     0     0     0     0     0     0 3.902230     0 #> [310,]     0     0     0     0     0     0     0     0     0 3.902197     0 #> [311,]     0     0     0     0     0     0     0     0     0 3.902164     0 #> [312,]     0     0     0     0     0     0     0     0     0 3.902130     0 #> [313,]     0     0     0     0     0     0     0     0     0 3.902097     0 #> [314,]     0     0     0     0     0     0     0     0     0 3.902064     0 #> [315,]     0     0     0     0     0     0     0     0     0 3.902031     0 #> [316,]     0     0     0     0     0     0     0     0     0 3.901997     0 #> [317,]     0     0     0     0     0     0     0     0     0 3.901964     0 #> [318,]     0     0     0     0     0     0     0     0     0 3.901931     0 #> [319,]     0     0     0     0     0     0     0     0     0 3.901898     0 #> [320,]     0     0     0     0     0     0     0     0     0 3.901865     0 #> [321,]     0     0     0     0     0     0     0     0     0 3.901831     0 #> [322,]     0     0     0     0     0     0     0     0     0 3.903940     0 #> [323,]     0     0     0     0     0     0     0     0     0 3.903907     0 #> [324,]     0     0     0     0     0     0     0     0     0 3.903873     0 #> [325,]     0     0     0     0     0     0     0     0     0 3.903840     0 #> [326,]     0     0     0     0     0     0     0     0     0 3.903807     0 #> [327,]     0     0     0     0     0     0     0     0     0 3.903774     0 #> [328,]     0     0     0     0     0     0     0     0     0 3.903740     0 #> [329,]     0     0     0     0     0     0     0     0     0 3.903707     0 #> [330,]     0     0     0     0     0     0     0     0     0 3.903674     0 #> [331,]     0     0     0     0     0     0     0     0     0 3.903641     0 #> [332,]     0     0     0     0     0     0     0     0     0 3.903608     0 #> [333,]     0     0     0     0     0     0     0     0     0 3.903574     0 #> [334,]     0     0     0     0     0     0     0     0     0 3.903541     0 #> [335,]     0     0     0     0     0     0     0     0     0 3.903508     0 #> [336,]     0     0     0     0     0     0     0     0     0 3.903475     0 #> [337,]     0     0     0     0     0     0     0     0     0 3.903441     0 #> [338,]     0     0     0     0     0     0     0     0     0 3.903408     0 #> [339,]     0     0     0     0     0     0     0     0     0 3.903375     0 #> [340,]     0     0     0     0     0     0     0     0     0 3.903342     0 #> [341,]     0     0     0     0     0     0     0     0     0 3.903308     0 #> [342,]     0     0     0     0     0     0     0     0     0 3.903275     0 #> [343,]     0     0     0     0     0     0     0     0     0 3.903242     0 #> [344,]     0     0     0     0     0     0     0     0     0 3.903209     0 #> [345,]     0     0     0     0     0     0     0     0     0 3.903176     0 #> [346,]     0     0     0     0     0     0     0     0     0 3.903142     0 #> [347,]     0     0     0     0     0     0     0     0     0 3.903109     0 #> [348,]     0     0     0     0     0     0     0     0     0 3.903076     0 #> [349,]     0     0     0     0     0     0     0     0     0 3.903043     0 #> [350,]     0     0     0     0     0     0     0     0     0 3.903009     0 #> [351,]     0     0     0     0     0     0     0     0     0 3.902976     0 #> [352,]     0     0     0     0     0     0     0     0     0 3.902943     0 #> [353,]     0     0     0     0     0     0     0     0     0 3.902910     0 #> [354,]     0     0     0     0     0     0     0     0     0 3.902876     0 #> [355,]     0     0     0     0     0     0     0     0     0 3.902843     0 #> [356,]     0     0     0     0     0     0     0     0     0 3.902810     0 #> [357,]     0     0     0     0     0     0     0     0     0 3.902777     0 #> [358,]     0     0     0     0     0     0     0     0     0 3.902744     0 #> [359,]     0     0     0     0     0     0     0     0     0 3.902710     0 #> [360,]     0     0     0     0     0     0     0     0     0 3.902677     0 #> [361,]     0     0     0     0     0     0     0     0     0 3.919111     0 #> [362,]     0     0     0     0     0     0     0     0     0 3.919077     0 #> [363,]     0     0     0     0     0     0     0     0     0 3.919044     0 #> [364,]     0     0     0     0     0     0     0     0     0 3.919011     0 #> [365,]     0     0     0     0     0     0     0     0     0 3.918977     0 #> [366,]     0     0     0     0     0     0     0     0     0 3.918944     0 #> [367,]     0     0     0     0     0     0     0     0     0 3.918911     0 #> [368,]     0     0     0     0     0     0     0     0     0 3.918877     0 #> [369,]     0     0     0     0     0     0     0     0     0 3.918844     0 #> [370,]     0     0     0     0     0     0     0     0     0 3.918811     0 #> [371,]     0     0     0     0     0     0     0     0     0 3.918777     0 #> [372,]     0     0     0     0     0     0     0     0     0 3.918744     0 #> [373,]     0     0     0     0     0     0     0     0     0 3.918710     0 #> [374,]     0     0     0     0     0     0     0     0     0 3.918677     0 #> [375,]     0     0     0     0     0     0     0     0     0 3.916442     0 #> [376,]     0     0     0     0     0     0     0     0     0 3.916409     0 #> [377,]     0     0     0     0     0     0     0     0     0 3.916376     0 #> [378,]     0     0     0     0     0     0     0     0     0 3.916342     0 #> [379,]     0     0     0     0     0     0     0     0     0 3.916309     0 #> [380,]     0     0     0     0     0     0     0     0     0 3.916276     0 #> [381,]     0     0     0     0     0     0     0     0     0 3.916242     0 #> [382,]     0     0     0     0     0     0     0     0     0 3.916209     0 #> [383,]     0     0     0     0     0     0     0     0     0 3.916176     0 #> [384,]     0     0     0     0     0     0     0     0     0 3.916142     0 #> [385,]     0     0     0     0     0     0     0     0     0 3.916109     0 #> [386,]     0     0     0     0     0     0     0     0     0 3.916076     0 #> [387,]     0     0     0     0     0     0     0     0     0 3.916042     0 #> [388,]     0     0     0     0     0     0     0     0     0 3.916009     0 #> [389,]     0     0     0     0     0     0     0     0     0 3.915976     0 #> [390,]     0     0     0     0     0     0     0     0     0 3.915942     0 #> [391,]     0     0     0     0     0     0     0     0     0 3.915909     0 #> [392,]     0     0     0     0     0     0     0     0     0 3.915876     0 #> [393,]     0     0     0     0     0     0     0     0     0 3.915842     0 #> [394,]     0     0     0     0     0     0     0     0     0 3.915809     0 #> [395,]     0     0     0     0     0     0     0     0     0 3.915776     0 #> [396,]     0     0     0     0     0     0     0     0     0 3.915742     0 #> [397,]     0     0     0     0     0     0     0     0     0 3.915709     0 #> [398,]     0     0     0     0     0     0     0     0     0 3.915676     0 #> [399,]     0     0     0     0     0     0     0     0     0 3.915642     0 #> [400,]     0     0     0     0     0     0     0     0     0 3.915609     0 #> [401,]     0     0     0     0     0     0     0     0     0 3.915576     0 #> [402,]     0     0     0     0     0     0     0     0     0 3.915542     0 #> [403,]     0     0     0     0     0     0     0     0     0 3.915509     0 #> [404,]     0     0     0     0     0     0     0     0     0 3.915475     0 #> [405,]     0     0     0     0     0     0     0     0     0 3.915442     0 #> [406,]     0     0     0     0     0     0     0     0     0 3.915409     0 #> [407,]     0     0     0     0     0     0     0     0     0 3.915375     0 #> [408,]     0     0     0     0     0     0     0     0     0 3.915342     0 #> [409,]     0     0     0     0     0     0     0     0     0 3.915309     0 #> [410,]     0     0     0     0     0     0     0     0     0 3.915275     0 #> [411,]     0     0     0     0     0     0     0     0     0 3.915242     0 #> [412,]     0     0     0     0     0     0     0     0     0 3.915209     0 #> [413,]     0     0     0     0     0     0     0     0     0 3.915175     0 #> [414,]     0     0     0     0     0     0     0     0     0 3.915142     0 #> [415,]     0     0     0     0     0     0     0     0     0 3.915109     0 #> [416,]     0     0     0     0     0     0     0     0     0 3.915075     0 #> [417,]     0     0     0     0     0     0     0     0     0 3.915042     0 #> [418,]     0     0     0     0     0     0     0     0     0 3.915009     0 #> [419,]     0     0     0     0     0     0     0     0     0 3.914975     0 #> [420,]     0     0     0     0     0     0     0     0     0 3.914942     0 #> [421,]     0     0     0     0     0     0     0     0     0 3.914909     0 #> [422,]     0     0     0     0     0     0     0     0     0 3.914875     0 #> [423,]     0     0     0     0     0     0     0     0     0 3.914842     0 #> [424,]     0     0     0     0     0     0     0     0     0 3.914809     0 #> [425,]     0     0     0     0     0     0     0     0     0 3.914775     0 #> [426,]     0     0     0     0     0     0     0     0     0 3.914742     0 #> [427,]     0     0     0     0     0     0     0     0     0 3.914709     0 #> [428,]     0     0     0     0     0     0     0     0     0 3.914676     0 #> [429,]     0     0     0     0     0     0     0     0     0 3.914642     0 #> [430,]     0     0     0     0     0     0     0     0     0 3.914609     0 #> [431,]     0     0     0     0     0     0     0     0     0 3.914576     0 #> [432,]     0     0     0     0     0     0     0     0     0 3.914542     0 #> [433,]     0     0     0     0     0     0     0     0     0 3.914509     0 #> [434,]     0     0     0     0     0     0     0     0     0 3.914476     0 #> [435,]     0     0     0     0     0     0     0     0     0 3.914442     0 #> [436,]     0     0     0     0     0     0     0     0     0 3.914409     0 #> [437,]     0     0     0     0     0     0     0     0     0 3.914376     0 #> [438,]     0     0     0     0     0     0     0     0     0 3.914342     0 #> [439,]     0     0     0     0     0     0     0     0     0 3.914309     0 #> [440,]     0     0     0     0     0     0     0     0     0 3.914276     0 #> [441,]     0     0     0     0     0     0     0     0     0 3.914242     0 #> [442,]     0     0     0     0     0     0     0     0     0 3.914209     0 #> [443,]     0     0     0     0     0     0     0     0     0 3.914176     0 #> [444,]     0     0     0     0     0     0     0     0     0 3.914142     0 #> [445,]     0     0     0     0     0     0     0     0     0 3.914109     0 #> [446,]     0     0     0     0     0     0     0     0     0 3.914076     0 #> [447,]     0     0     0     0     0     0     0     0     0 3.914042     0 #> [448,]     0     0     0     0     0     0     0     0     0 3.914009     0 #> [449,]     0     0     0     0     0     0     0     0     0 3.913976     0 #> [450,]     0     0     0     0     0     0     0     0     0 3.913942     0 #> [451,]     0     0     0     0     0     0     0     0     0 3.913909     0 #> [452,]     0     0     0     0     0     0     0     0     0 3.913876     0 #> [453,]     0     0     0     0     0     0     0     0     0 3.913842     0 #> [454,]     0     0     0     0     0     0     0     0     0 3.913809     0 #> [455,]     0     0     0     0     0     0     0     0     0 3.913776     0 #> [456,]     0     0     0     0     0     0     0     0     0 3.913742     0 #> [457,]     0     0     0     0     0     0     0     0     0 3.913709     0 #> [458,]     0     0     0     0     0     0     0     0     0 3.913676     0 #> [459,]     0     0     0     0     0     0     0     0     0 3.913642     0 #> [460,]     0     0     0     0     0     0     0     0     0 3.963494     0 #> [461,]     0     0     0     0     0     0     0     0     0 3.963460     0 #> [462,]     0     0     0     0     0     0     0     0     0 3.979425     0 #> [463,]     0     0     0     0     0     0     0     0     0 3.979391     0 #> [464,]     0     0     0     0     0     0     0     0     0 3.979357     0 #> [465,]     0     0     0     0     0     0     0     0     0 3.979323     0 #> [466,]     0     0     0     0     0     0     0     0     0 3.979290     0 #> [467,]     0     0     0     0     0     0     0     0     0 3.979256     0 #> [468,]     0     0     0     0     0     0     0     0     0 3.913609     0 #> [469,]     0     0     0     0     0     0     0     0     0 3.913576     0 #> [470,]     0     0     0     0     0     0     0     0     0 3.913542     0 #> [471,]     0     0     0     0     0     0     0     0     0 3.979222     0 #> [472,]     0     0     0     0     0     0     0     0     0 3.979188     0 #> [473,]     0     0     0     0     0     0     0     0     0 3.979154     0 #> [474,]     0     0     0     0     0     0     0     0     0 3.979120     0 #> [475,]     0     0     0     0     0     0     0     0     0 3.979086     0 #> [476,]     0     0     0     0     0     0     0     0     0 3.979052     0 #> [477,]     0     0     0     0     0     0     0     0     0 3.979018     0 #> [478,]     0     0     0     0     0     0     0     0     0 3.978985     0 #> [479,]     0     0     0     0     0     0     0     0     0 3.978951     0 #> [480,]     0     0     0     0     0     0     0     0     0 3.978917     0 #> [481,]     0     0     0     0     0     0     0     0     0 3.978883     0 #> [482,]     0     0     0     0     0     0     0     0     0 3.978849     0 #> [483,]     0     0     0     0     0     0     0     0     0 3.978815     0 #> [484,]     0     0     0     0     0     0     0     0     0 3.978781     0 #> [485,]     0     0     0     0     0     0     0     0     0 3.978747     0 #> [486,]     0     0     0     0     0     0     0     0     0 3.978714     0 #> [487,]     0     0     0     0     0     0     0     0     0 3.978680     0 #> [488,]     0     0     0     0     0     0     0     0     0 3.978646     0 #> [489,]     0     0     0     0     0     0     0     0     0 3.978612     0 #> [490,]     0     0     0     0     0     0     0     0     0 3.978578     0 #> [491,]     0     0     0     0     0     0     0     0     0 3.978544     0 #> [492,]     0     0     0     0     0     0     0     0     0 3.978510     0 #> [493,]     0     0     0     0     0     0     0     0     0 3.978476     0 #> [494,]     0     0     0     0     0     0     0     0     0 3.978443     0 #> [495,]     0     0     0     0     0     0     0     0     0 3.978409     0 #> [496,]     0     0     0     0     0     0     0     0     0 3.978375     0 #> [497,]     0     0     0     0     0     0     0     0     0 3.978341     0 #> [498,]     0     0     0     0     0     0     0     0     0 3.978307     0 #> [499,]     0     0     0     0     0     0     0     0     0 3.978273     0 #> [500,]     0     0     0     0     0     0     0     0     0 3.978239     0 #> [501,]     0     0     0     0     0     0     0     0     0 3.978206     0 #> [502,]     0     0     0     0     0     0     0     0     0 3.978172     0 #> [503,]     0     0     0     0     0     0     0     0     0 3.978138     0 #> [504,]     0     0     0     0     0     0     0     0     0 3.978104     0 #> [505,]     0     0     0     0     0     0     0     0     0 3.978070     0 #> [506,]     0     0     0     0     0     0     0     0     0 3.978036     0 #> [507,]     0     0     0     0     0     0     0     0     0 3.978002     0 #> [508,]     0     0     0     0     0     0     0     0     0 3.977968     0 #> [509,]     0     0     0     0     0     0     0     0     0 3.882493     0 #> [510,]     0     0     0     0     0     0     0     0     0 3.882460     0 #> [511,]     0     0     0     0     0     0     0     0     0 3.882427     0 #> [512,]     0     0     0     0     0     0     0     0     0 3.882394     0 #> [513,]     0     0     0     0     0     0     0     0     0 3.882361     0 #> [514,]     0     0     0     0     0     0     0     0     0 3.882328     0 #> [515,]     0     0     0     0     0     0     0     0     0 3.882295     0 #> [516,]     0     0     0     0     0     0     0     0     0 3.882262     0 #> [517,]     0     0     0     0     0     0     0     0     0 3.882229     0 #> [518,]     0     0     0     0     0     0     0     0     0 3.882196     0 #> [519,]     0     0     0     0     0     0     0     0     0 3.902629     0 #> [520,]     0     0     0     0     0     0     0     0     0 3.902596     0 #> [521,]     0     0     0     0     0     0     0     0     0 3.902562     0 #> [522,]     0     0     0     0     0     0     0     0     0 3.902529     0 #> [523,]     0     0     0     0     0     0     0     0     0 3.902496     0 #> [524,]     0     0     0     0     0     0     0     0     0 3.902463     0 #> [525,]     0     0     0     0     0     0     0     0     0 3.902429     0 #> [526,]     0     0     0     0     0     0     0     0     0 3.902396     0 #> [527,]     0     0     0     0     0     0     0     0     0 3.902363     0 #> [528,]     0     0     0     0     0     0     0     0     0 3.902330     0 #> [529,]     0     0     0     0     0     0     0     0     0 3.902297     0 #> [530,]     0     0     0     0     0     0     0     0     0 3.902263     0 #> [531,]     0     0     0     0     0     0     0     0     0 3.918644     0 #> [532,]     0     0     0     0     0     0     0     0     0 3.918610     0 #> [533,]     0     0     0     0     0     0     0     0     0 3.918577     0 #> [534,]     0     0     0     0     0     0     0     0     0 3.918544     0 #> [535,]     0     0     0     0     0     0     0     0     0 3.918510     0 #> [536,]     0     0     0     0     0     0     0     0     0 3.918477     0 #> [537,]     0     0     0     0     0     0     0     0     0 3.918444     0 #> [538,]     0     0     0     0     0     0     0     0     0 3.918410     0 #> [539,]     0     0     0     0     0     0     0     0     0 3.918377     0 #> [540,]     0     0     0     0     0     0     0     0     0 3.918343     0 #> [541,]     0     0     0     0     0     0     0     0     0 3.918310     0 #> [542,]     0     0     0     0     0     0     0     0     0 3.918277     0 #> [543,]     0     0     0     0     0     0     0     0     0 3.918243     0 #> [544,]     0     0     0     0     0     0     0     0     0 3.918210     0 #> [545,]     0     0     0     0     0     0     0     0     0 3.918177     0 #> [546,]     0     0     0     0     0     0     0     0     0 3.918143     0 #> [547,]     0     0     0     0     0     0     0     0     0 3.918110     0 #> [548,]     0     0     0     0     0     0     0     0     0 3.918077     0 #> [549,]     0     0     0     0     0     0     0     0     0 3.918043     0 #> [550,]     0     0     0     0     0     0     0     0     0 3.918010     0 #> [551,]     0     0     0     0     0     0     0     0     0 3.917977     0 #> [552,]     0     0     0     0     0     0     0     0     0 3.917943     0 #> [553,]     0     0     0     0     0     0     0     0     0 3.917910     0 #> [554,]     0     0     0     0     0     0     0     0     0 3.917876     0 #> [555,]     0     0     0     0     0     0     0     0     0 3.917843     0 #> [556,]     0     0     0     0     0     0     0     0     0 3.917810     0 #> [557,]     0     0     0     0     0     0     0     0     0 3.917776     0 #> [558,]     0     0     0     0     0     0     0     0     0 3.917743     0 #> [559,]     0     0     0     0     0     0     0     0     0 3.917710     0 #> [560,]     0     0     0     0     0     0     0     0     0 3.917676     0 #> [561,]     0     0     0     0     0     0     0     0     0 3.917643     0 #> [562,]     0     0     0     0     0     0     0     0     0 3.917610     0 #> [563,]     0     0     0     0     0     0     0     0     0 3.917576     0 #> [564,]     0     0     0     0     0     0     0     0     0 3.917543     0 #> [565,]     0     0     0     0     0     0     0     0     0 3.917510     0 #> [566,]     0     0     0     0     0     0     0     0     0 3.917476     0 #> [567,]     0     0     0     0     0     0     0     0     0 3.917443     0 #> [568,]     0     0     0     0     0     0     0     0     0 3.917409     0 #> [569,]     0     0     0     0     0     0     0     0     0 3.917376     0 #> [570,]     0     0     0     0     0     0     0     0     0 3.917343     0 #> [571,]     0     0     0     0     0     0     0     0     0 3.917309     0 #> [572,]     0     0     0     0     0     0     0     0     0 3.917276     0 #> [573,]     0     0     0     0     0     0     0     0     0 3.917243     0 #> [574,]     0     0     0     0     0     0     0     0     0 3.917209     0 #> [575,]     0     0     0     0     0     0     0     0     0 3.917176     0 #> [576,]     0     0     0     0     0     0     0     0     0 3.917143     0 #> [577,]     0     0     0     0     0     0     0     0     0 3.917109     0 #> [578,]     0     0     0     0     0     0     0     0     0 3.917076     0 #> [579,]     0     0     0     0     0     0     0     0     0 3.917043     0 #> [580,]     0     0     0     0     0     0     0     0     0 3.917009     0 #> [581,]     0     0     0     0     0     0     0     0     0 3.916976     0 #> [582,]     0     0     0     0     0     0     0     0     0 3.916943     0 #> [583,]     0     0     0     0     0     0     0     0     0 3.916909     0 #> [584,]     0     0     0     0     0     0     0     0     0 3.916876     0 #> [585,]     0     0     0     0     0     0     0     0     0 3.916843     0 #> [586,]     0     0     0     0     0     0     0     0     0 3.916809     0 #> [587,]     0     0     0     0     0     0     0     0     0 3.916776     0 #> [588,]     0     0     0     0     0     0     0     0     0 3.916742     0 #> [589,]     0     0     0     0     0     0     0     0     0 3.916709     0 #> [590,]     0     0     0     0     0     0     0     0     0 3.916676     0 #> [591,]     0     0     0     0     0     0     0     0     0 3.916642     0 #> [592,]     0     0     0     0     0     0     0     0     0 3.916609     0 #> [593,]     0     0     0     0     0     0     0     0     0 3.916576     0 #> [594,]     0     0     0     0     0     0     0     0     0 3.916542     0 #> [595,]     0     0     0     0     0     0     0     0     0 3.916509     0 #> [596,]     0     0     0     0     0     0     0     0     0 3.916476     0 #> [597,]     0     0     0     0     0     0     0     0     0 3.913509     0 #> [598,]     0     0     0     0     0     0     0  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0     0     0     0     0     0     0 3.913043     0 #> [612,]     0     0     0     0     0     0     0     0     0 3.913009     0 #> [613,]     0     0     0     0     0     0     0     0     0 3.912976     0 #> [614,]     0     0     0     0     0     0     0     0     0 3.912943     0 #> [615,]     0     0     0     0     0     0     0     0     0 3.912909     0 #> [616,]     0     0     0     0     0     0     0     0     0 3.964810     0 #> [617,]     0     0     0     0     0     0     0     0     0 3.964776     0 #> [618,]     0     0     0     0     0     0     0     0     0 3.964742     0 #> [619,]     0     0     0     0     0     0     0     0     0 3.964709     0 #> [620,]     0     0     0     0     0     0     0     0     0 3.964675     0 #> [621,]     0     0     0     0     0     0     0     0     0 3.964641     0 #> [622,]     0     0     0     0     0     0     0     0     0 3.964607     0 #> [623,]     0     0     0     0     0     0     0     0     0 3.964574     0 #> [624,]     0     0     0     0     0     0     0     0     0 3.964540     0 #> [625,]     0     0     0     0     0     0     0     0     0 3.964506     0 #> [626,]     0     0     0     0     0     0     0     0     0 3.964472     0 #> [627,]     0     0     0     0     0     0     0     0     0 3.964439     0 #> [628,]     0     0     0     0     0     0     0     0     0 3.964405     0 #> [629,]     0     0     0     0     0     0     0     0     0 3.964381     0 #> [630,]     0     0     0     0     0     0     0     0     0 3.964337     0 #> [631,]     0     0     0     0     0     0     0     0     0 3.964304     0 #> [632,]     0     0     0     0     0     0     0     0     0 3.964270     0 #> [633,]     0     0     0     0     0     0     0     0     0 3.964236     0 #> [634,]     0     0     0     0     0     0     0     0     0 3.964202     0 #> [635,]     0     0     0     0     0     0     0     0     0 3.964169     0 #> [636,]     0     0     0     0     0     0     0     0     0 3.964135     0 #> [637,]     0     0     0     0     0     0     0     0     0 3.964101     0 #> [638,]     0     0     0     0     0     0     0     0     0 3.964067     0 #> [639,]     0     0     0     0     0     0     0     0     0 3.964034     0 #> [640,]     0     0     0     0     0     0     0     0     0 3.964000     0 #> [641,]     0     0     0     0     0     0     0     0     0 3.963966     0 #> [642,]     0     0     0     0     0     0     0     0     0 3.963932     0 #> [643,]     0     0     0     0     0     0     0     0     0 3.963899     0 #> [644,]     0     0     0     0     0     0     0     0     0 3.963865     0 #> [645,]     0     0     0     0     0     0     0     0     0 3.963831     0 #> [646,]     0     0     0     0     0     0     0     0     0 3.963797     0 #> [647,]     0     0     0     0     0     0     0     0     0 3.963764     0 #> [648,]     0     0     0     0     0     0     0     0     0 3.963730     0 #> [649,]     0     0     0     0     0     0     0     0     0 3.963696     0 #> [650,]     0     0     0     0     0     0     0     0     0 3.963662     0 #> [651,]     0     0     0     0     0     0     0     0     0 3.963629     0 #> [652,]     0     0     0     0     0     0     0     0     0 3.963595     0 #> [653,]     0     0     0     0     0     0     0     0     0 3.963561     0 #> [654,]     0     0     0     0     0     0     0     0     0 3.963527     0 #>        [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [,41] [,42] #>   [1,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [2,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [3,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [4,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [5,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [6,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [7,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [8,]     0     0     0     0     0     0     0     0     0     0     0     0 #>   [9,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [10,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [11,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [12,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [13,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [14,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [15,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [16,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [17,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [18,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [19,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [20,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [21,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [22,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [23,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [24,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [25,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [26,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [27,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [28,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [29,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [30,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [31,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [32,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [33,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [34,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [35,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [36,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [37,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [38,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [39,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [40,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [41,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [42,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [43,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [44,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [45,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [46,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [47,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [48,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [49,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [50,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [51,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [52,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [53,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [54,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [55,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [56,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [57,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [58,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [59,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [60,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [61,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [62,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [63,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [64,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [65,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [66,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [67,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [68,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [69,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [70,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [71,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [72,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [73,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [74,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [75,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [76,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [77,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [78,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [79,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [80,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [81,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [82,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [83,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [84,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [85,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [86,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [87,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [88,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [89,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [90,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [91,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [92,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [93,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [94,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [95,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [96,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [97,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [98,]     0     0     0     0     0     0     0     0     0     0     0     0 #>  [99,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [100,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [101,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [102,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [103,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [104,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [105,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [106,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [107,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [108,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [109,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [110,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [111,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [112,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [113,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [114,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [115,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [116,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [117,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [118,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [119,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [120,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [121,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [122,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [123,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [124,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [125,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [126,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [127,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [128,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [129,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [130,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [131,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [132,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [133,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [134,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [135,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [136,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [137,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [138,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [139,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [140,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [141,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [142,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [143,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [144,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [145,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [146,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [147,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [148,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [149,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [150,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [151,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [152,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [153,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [154,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [155,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [156,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [157,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [158,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [159,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [160,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [161,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [162,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [163,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [164,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [165,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [166,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [167,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [168,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [169,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [170,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [171,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [172,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [173,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [174,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [175,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [176,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [177,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [178,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [179,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [180,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [181,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [182,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [183,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [184,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [185,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [186,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [187,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [188,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [189,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [190,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [191,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [192,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [193,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [194,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [195,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [196,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [197,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [198,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [199,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [200,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [201,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [202,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [203,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [204,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [205,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [206,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [207,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [208,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [209,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [210,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [211,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [212,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [213,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [214,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [215,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [216,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [217,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [218,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [219,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [220,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [221,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [222,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [223,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [224,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [225,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [226,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [227,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [228,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [229,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [230,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [231,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [232,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [233,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [234,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [235,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [236,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [237,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [238,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [239,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [240,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [241,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [242,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [243,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [244,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [245,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [246,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [247,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [248,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [249,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [250,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [251,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [252,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [253,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [254,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [255,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [256,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [257,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [258,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [259,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [260,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [261,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [262,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [263,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [264,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [265,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [266,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [267,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [268,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [269,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [270,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [271,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [272,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [273,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [274,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [275,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [276,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [277,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [278,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [279,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [280,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [281,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [282,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [283,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [284,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [285,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [286,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [287,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [288,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [289,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [290,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [291,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [292,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [293,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [294,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [295,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [296,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [297,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [298,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [299,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [300,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [301,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [302,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [303,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [304,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [305,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [306,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [307,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [308,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [309,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [310,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [311,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [312,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [313,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [314,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [315,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [316,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [317,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [318,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [319,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [320,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [321,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [322,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [323,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [324,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [325,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [326,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [327,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [328,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [329,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [330,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [331,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [332,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [333,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [334,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [335,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [336,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [337,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [338,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [339,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [340,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [341,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [342,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [343,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [344,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [345,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [346,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [347,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [348,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [349,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [350,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [351,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [352,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [353,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [354,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [355,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [356,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [357,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [358,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [359,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [360,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [361,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [362,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [363,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [364,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [365,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [366,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [367,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [368,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [369,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [370,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [371,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [372,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [373,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [374,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [375,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [376,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [377,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [378,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [379,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [380,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [381,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [382,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [383,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [384,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [385,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [386,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [387,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [388,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [389,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [390,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [391,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [392,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [393,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [394,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [395,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [396,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [397,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [398,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [399,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [400,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [401,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [402,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [403,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [404,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [405,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [406,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [407,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [408,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [409,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [410,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [411,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [412,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [413,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [414,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [415,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [416,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [417,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [418,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [419,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [420,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [421,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [422,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [423,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [424,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [425,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [426,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [427,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [428,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [429,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [430,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [431,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [432,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [433,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [434,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [435,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [436,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [437,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [438,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [439,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [440,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [441,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [442,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [443,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [444,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [445,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [446,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [447,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [448,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [449,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [450,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [451,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [452,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [453,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [454,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [455,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [456,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [457,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [458,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [459,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [460,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [461,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [462,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [463,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [464,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [465,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [466,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [467,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [468,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [469,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [470,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [471,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [472,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [473,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [474,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [475,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [476,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [477,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [478,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [479,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [480,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [481,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [482,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [483,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [484,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [485,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [486,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [487,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [488,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [489,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [490,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [491,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [492,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [493,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [494,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [495,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [496,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [497,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [498,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [499,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [500,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [501,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [502,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [503,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [504,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [505,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [506,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [507,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [508,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [509,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [510,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [511,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [512,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [513,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [514,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [515,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [516,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [517,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [518,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [519,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [520,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [521,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [522,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [523,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [524,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [525,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [526,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [527,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [528,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [529,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [530,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [531,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [532,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [533,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [534,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [535,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [536,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [537,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [538,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [539,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [540,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [541,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [542,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [543,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [544,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [545,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [546,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [547,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [548,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [549,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [550,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [551,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [552,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [553,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [554,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [555,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [556,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [557,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [558,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [559,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [560,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [561,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [562,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [563,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [564,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [565,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [566,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [567,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [568,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [569,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [570,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [571,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [572,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [573,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [574,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [575,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [576,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [577,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [578,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [579,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [580,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [581,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [582,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [583,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [584,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [585,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [586,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [587,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [588,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [589,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [590,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [591,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [592,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [593,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [594,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [595,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [596,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [597,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [598,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [599,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [600,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [601,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [602,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [603,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [604,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [605,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [606,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [607,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [608,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [609,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [610,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [611,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [612,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [613,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [614,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [615,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [616,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [617,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [618,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [619,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [620,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [621,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [622,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [623,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [624,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [625,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [626,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [627,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [628,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [629,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [630,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [631,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [632,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [633,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [634,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [635,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [636,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [637,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [638,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [639,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [640,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [641,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [642,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [643,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [644,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [645,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [646,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [647,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [648,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [649,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [650,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [651,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [652,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [653,]     0     0     0     0     0     0     0     0     0     0     0     0 #> [654,]     0     0     0     0     0     0     0     0     0     0     0     0 #>        [,43] [,44] [,45] #>   [1,]     0     0     0 #>   [2,]     0     0     0 #>   [3,]     0     0     0 #>   [4,]     0     0     0 #>   [5,]     0     0     0 #>   [6,]     0     0     0 #>   [7,]     0     0     0 #>   [8,]     0     0     0 #>   [9,]     0     0     0 #>  [10,]     0     0     0 #>  [11,]     0     0     0 #>  [12,]     0     0     0 #>  [13,]     0     0     0 #>  [14,]     0     0     0 #>  [15,]     0     0     0 #>  [16,]     0     0     0 #>  [17,]     0     0     0 #>  [18,]     0     0     0 #>  [19,]     0     0     0 #>  [20,]     0     0     0 #>  [21,]     0     0     0 #>  [22,]     0     0     0 #>  [23,]     0     0     0 #>  [24,]     0     0     0 #>  [25,]     0     0     0 #>  [26,]     0     0     0 #>  [27,]     0     0     0 #>  [28,]     0     0     0 #>  [29,]     0     0     0 #>  [30,]     0     0     0 #>  [31,]     0     0     0 #>  [32,]     0     0     0 #>  [33,]     0     0     0 #>  [34,]     0     0     0 #>  [35,]     0     0     0 #>  [36,]     0     0     0 #>  [37,]     0     0     0 #>  [38,]     0     0     0 #>  [39,]     0     0     0 #>  [40,]     0     0     0 #>  [41,]     0     0     0 #>  [42,]     0     0     0 #>  [43,]     0     0     0 #>  [44,]     0     0     0 #>  [45,]     0     0     0 #>  [46,]     0     0     0 #>  [47,]     0     0     0 #>  [48,]     0     0     0 #>  [49,]     0     0     0 #>  [50,]     0     0     0 #>  [51,]     0     0     0 #>  [52,]     0     0     0 #>  [53,]     0     0     0 #>  [54,]     0     0     0 #>  [55,]     0     0     0 #>  [56,]     0     0     0 #>  [57,]     0     0     0 #>  [58,]     0     0     0 #>  [59,]     0     0     0 #>  [60,]     0     0     0 #>  [61,]     0     0     0 #>  [62,]     0     0     0 #>  [63,]     0     0     0 #>  [64,]     0     0     0 #>  [65,]     0     0     0 #>  [66,]     0     0     0 #>  [67,]     0     0     0 #>  [68,]     0     0     0 #>  [69,]     0     0     0 #>  [70,]     0     0     0 #>  [71,]     0     0     0 #>  [72,]     0     0     0 #>  [73,]     0     0     0 #>  [74,]     0     0     0 #>  [75,]     0     0     0 #>  [76,]     0     0     0 #>  [77,]     0     0     0 #>  [78,]     0     0     0 #>  [79,]     0     0     0 #>  [80,]     0     0     0 #>  [81,]     0     0     0 #>  [82,]     0     0     0 #>  [83,]     0     0     0 #>  [84,]     0     0     0 #>  [85,]     0     0     0 #>  [86,]     0     0     0 #>  [87,]     0     0     0 #>  [88,]     0     0     0 #>  [89,]     0     0     0 #>  [90,]     0     0     0 #>  [91,]     0     0     0 #>  [92,]     0     0     0 #>  [93,]     0     0     0 #>  [94,]     0     0     0 #>  [95,]     0     0     0 #>  [96,]     0     0     0 #>  [97,]     0     0     0 #>  [98,]     0     0     0 #>  [99,]     0     0     0 #> [100,]     0     0     0 #> [101,]     0     0     0 #> [102,]     0     0     0 #> [103,]     0     0     0 #> [104,]     0     0     0 #> [105,]     0     0     0 #> [106,]     0     0     0 #> [107,]     0     0     0 #> [108,]     0     0     0 #> [109,]     0     0     0 #> [110,]     0     0     0 #> [111,]     0     0     0 #> [112,]     0     0     0 #> [113,]     0     0     0 #> [114,]     0     0     0 #> [115,]     0     0     0 #> [116,]     0     0     0 #> [117,]     0     0     0 #> [118,]     0     0     0 #> [119,]     0     0     0 #> [120,]     0     0     0 #> [121,]     0     0     0 #> [122,]     0     0     0 #> [123,]     0     0     0 #> [124,]     0     0     0 #> [125,]     0     0     0 #> [126,]     0     0     0 #> [127,]     0     0     0 #> [128,]     0     0     0 #> [129,]     0     0     0 #> [130,]     0     0     0 #> [131,]     0     0     0 #> [132,]     0     0     0 #> [133,]     0     0     0 #> [134,]     0     0     0 #> [135,]     0     0     0 #> [136,]     0     0     0 #> [137,]     0     0     0 #> [138,] 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  0 #> [639,]     0     0     0 #> [640,]     0     0     0 #> [641,]     0     0     0 #> [642,]     0     0     0 #> [643,]     0     0     0 #> [644,]     0     0     0 #> [645,]     0     0     0 #> [646,]     0     0     0 #> [647,]     0     0     0 #> [648,]     0     0     0 #> [649,]     0     0     0 #> [650,]     0     0     0 #> [651,]     0     0     0 #> [652,]     0     0     0 #> [653,]     0     0     0 #> [654,]     0     0     0"},{"path":"/reference/deviance.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitted model deviance — deviance.SSN2","title":"Fitted model deviance — deviance.SSN2","text":"Returns deviance fitted model object.","code":""},{"path":"/reference/deviance.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitted model deviance — deviance.SSN2","text":"","code":"# S3 method for ssn_lm deviance(object, ...)  # S3 method for ssn_glm deviance(object, ...)"},{"path":"/reference/deviance.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitted model deviance — deviance.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/deviance.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitted model deviance — deviance.SSN2","text":"deviance.","code":""},{"path":"/reference/deviance.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitted model deviance — deviance.SSN2","text":"deviance twice difference log-likelihoods saturated (perfect-fit) model fitted model.","code":""},{"path":"/reference/deviance.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitted model deviance — deviance.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_glm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   family = \"Gamma\",   tailup_type = \"exponential\",   additive = \"afvArea\" ) deviance(ssn_mod) #> [1] 0.00200104"},{"path":"/reference/fitted.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract model fitted values — fitted.SSN2","title":"Extract model fitted values — fitted.SSN2","text":"Extract fitted values fitted model objects. fitted.values alias.","code":""},{"path":"/reference/fitted.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract model fitted values — fitted.SSN2","text":"","code":"# S3 method for ssn_lm fitted(object, type = \"response\", ...)  # S3 method for ssn_lm fitted.values(object, type = \"response\", ...)  # S3 method for ssn_glm fitted(object, type = \"response\", ...)  # S3 method for ssn_glm fitted.values(object, type = \"response\", ...)"},{"path":"/reference/fitted.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract model fitted values — fitted.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). type \"response\" fitted values response, \"tailup\" fitted values tailup random errors, \"taildown\" fitted values taildown random errors, \"euclid\" fitted values Euclidean random errors, \"nugget\" fitted values nugget random errors, \"randcov\" fitted values random effects. ssn_glm(), \"link\" fitted values link scale. default \"response\". ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/fitted.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract model fitted values — fitted.SSN2","text":"fitted values according type.","code":""},{"path":"/reference/fitted.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract model fitted values — fitted.SSN2","text":"type \"response\", fitted values observation standard fitted values \\(X \\hat{\\beta}\\). type \"tailup\", \"taildown\", \"euclid\", \"nugget\" fitted values observation (generally) best linear unbiased predictors respective random error. type \"randcov\", fitted values level random effect (generally) best linear unbiased predictors corresponding random effect. fitted values type \"tailup\", \"taildown\", \"euclid\", \"nugget\", \"randcov\" can generally used check assumptions component fitted model object (e.g., check Gaussian assumption). ssn_glm(), type \"response\", fitted values observation standard fitted values inverse link scale: \\(g^{-1}\\)(\\(X \\hat{\\beta} + \\nu\\)), \\(g(.)\\) link function, \\(\\beta\\) fixed effects, \\(\\nu\\) spatial random effects.","code":""},{"path":"/reference/fitted.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract model fitted values — fitted.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) fitted(ssn_mod) #>        1        2        3        4        5        6        7        8  #> 14.34158 14.13982 13.88042 13.47691 13.41927 13.33280 13.01576 13.01576  #>        9       10       11       12       13       14       15       16  #> 12.92929 14.02453 13.30398 13.24633 12.58342 12.17991 11.77640 11.22878  #>       17       18       19       20       21       22       23       24  #> 14.39922 14.19747 13.70749 13.62102 13.76513 13.50573 12.92929 12.64107  #>       25       26       27       28       29       30       31       32  #> 12.35284 12.23755 11.63229 13.21751 12.69871 12.46813 14.88920 15.20625  #>       33       34       35       36       37       38       39       40  #> 15.06213 15.89798 15.63858 15.40800 15.40800 15.26389 15.17742 15.11978  #>       41       42       43       44       45  #> 15.14860 15.11978 15.09096 13.24633 11.45935  fitted.values(ssn_mod) #>        1        2        3        4        5        6        7        8  #> 14.34158 14.13982 13.88042 13.47691 13.41927 13.33280 13.01576 13.01576  #>        9       10       11       12       13       14       15       16  #> 12.92929 14.02453 13.30398 13.24633 12.58342 12.17991 11.77640 11.22878  #>       17       18       19       20       21       22       23       24  #> 14.39922 14.19747 13.70749 13.62102 13.76513 13.50573 12.92929 12.64107  #>       25       26       27       28       29       30       31       32  #> 12.35284 12.23755 11.63229 13.21751 12.69871 12.46813 14.88920 15.20625  #>       33       34       35       36       37       38       39       40  #> 15.06213 15.89798 15.63858 15.40800 15.40800 15.26389 15.17742 15.11978  #>       41       42       43       44       45  #> 15.14860 15.11978 15.09096 13.24633 11.45935"},{"path":"/reference/formula.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Model formulae — formula.SSN2","title":"Model formulae — formula.SSN2","text":"Return formula used fitted model object.","code":""},{"path":"/reference/formula.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model formulae — formula.SSN2","text":"","code":"# S3 method for ssn_lm formula(x, ...)  # S3 method for ssn_glm formula(x, ...)"},{"path":"/reference/formula.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model formulae — formula.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/formula.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model formulae — formula.SSN2","text":"formula used fitted model object.","code":""},{"path":"/reference/formula.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model formulae — formula.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) formula(ssn_mod) #> Summer_mn ~ ELEV_DEM #> <environment: 0x000001546ff7a278>"},{"path":"/reference/glance.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance at a fitted model object — glance.SSN2","title":"Glance at a fitted model object — glance.SSN2","text":"Returns row model summaries fitted model object. Glance returns number columns models estimation methods.","code":""},{"path":"/reference/glance.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance at a fitted model object — glance.SSN2","text":"","code":"# S3 method for ssn_lm glance(x, ...)  # S3 method for ssn_glm glance(x, ...)"},{"path":"/reference/glance.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance at a fitted model object — glance.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/glance.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance at a fitted model object — glance.SSN2","text":"single-row tibble columns n sample size. p number fixed effects. npar number estimated covariance parameters. value optimized value fitting function AIC AIC. AICc AICc. logLik log-likelihood deviance deviance. pseudo.r.squared pseudo r-squared","code":""},{"path":"/reference/glance.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance at a fitted model object — glance.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) glance(ssn_mod) #> # A tibble: 1 × 9 #>       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1    45     2     3  54.2  60.2  60.8  -27.1     43.5            0.669"},{"path":"/reference/glances.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance at many fitted model objects — glances.SSN2","title":"Glance at many fitted model objects — glances.SSN2","text":"glances() repeatedly calls glance() several fitted model objects binds output together, sorted column interest.","code":""},{"path":"/reference/glances.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance at many fitted model objects — glances.SSN2","text":"","code":"# S3 method for ssn_lm glances(object, ..., sort_by = \"AICc\", decreasing = FALSE)  # S3 method for ssn_glm glances(object, ..., sort_by = \"AICc\", decreasing = FALSE)"},{"path":"/reference/glances.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance at many fitted model objects — glances.SSN2","text":"object Fitted model object ssn_lm() ssn_glm(). ... Additional fitted model objects ssn_lm() ssn_glm(). sort_by Sort glance statistic (.e., name column output glance() order model input (sort_by = \"order\"). default \"AICc\". decreasing sort_by decreasing ? default FALSE.","code":""},{"path":"/reference/glances.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance at many fitted model objects — glances.SSN2","text":"tibble row represents output glance() fitted model object.","code":""},{"path":"/reference/glances.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance at many fitted model objects — glances.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  # tailup only ssn_mod1 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) # taildown only ssn_mod2 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   taildown_type = \"exponential\" ) glances(ssn_mod1, ssn_mod2) #> # A tibble: 2 × 10 #>   model        n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <chr>    <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1 ssn_mod1    45     2     3  54.2  60.2  60.8  -27.1     43.5           0.669  #> 2 ssn_mod2    45     2     3 123.  129.  129.   -61.4     43.4           0.0917"},{"path":"/reference/hatvalues.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute leverage (hat) values — hatvalues.SSN2","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"Compute leverage (hat) value observation fitted model object.","code":""},{"path":"/reference/hatvalues.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"","code":"# S3 method for ssn_lm hatvalues(model, ...)  # S3 method for ssn_glm hatvalues(model, ...)"},{"path":"/reference/hatvalues.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"model fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/hatvalues.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"vector leverage (hat) values observation fitted model object.","code":""},{"path":"/reference/hatvalues.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"Leverage values measure far observation's explanatory variables relative average explanatory variables. words, observations high leverage typically considered extreme unusual combination explanatory variables. Leverage values diagonal hat (projection) matrix. larger hat value, larger leverage.","code":""},{"path":[]},{"path":"/reference/hatvalues.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) hatvalues(ssn_mod) #>            1            2            3            4            5            6  #> 1.565733e-01 9.237419e-02 3.014500e-02 7.981493e-03 1.046372e-02 3.495971e-03  #>            7            8            9           10           11           12  #> 1.793629e-02 1.693222e-02 8.311967e-03 5.612972e-02 1.031498e-01 6.860116e-02  #>           13           14           15           16           17           18  #> 1.109540e-02 4.019243e-02 1.253507e-01 9.102368e-02 7.023914e-02 2.202866e-02  #>           19           20           21           22           23           24  #> 4.509632e-03 2.293475e-03 3.162038e-03 2.476739e-03 1.356383e-02 5.100491e-02  #>           25           26           27           28           29           30  #> 1.395086e-02 2.156846e-02 9.548471e-02 1.553130e-01 8.095946e-03 3.568117e-02  #>           31           32           33           34           35           36  #> 3.536055e-02 1.067164e-01 4.896040e-02 2.389906e-02 7.092277e-05 1.122144e-02  #>           37           38           39           40           41           42  #> 2.463285e-02 1.287318e-01 8.437105e-02 6.881851e-02 1.810541e-02 1.149500e-02  #>           43           44           45  #> 9.801228e-03 7.212308e-02 1.656265e-02"},{"path":"/reference/influence.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Regression diagnostics — influence.SSN2","title":"Regression diagnostics — influence.SSN2","text":"Provides basic quantities used forming wide variety diagnostics checking quality fitted model objects.","code":""},{"path":"/reference/influence.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regression diagnostics — influence.SSN2","text":"","code":"# S3 method for ssn_lm influence(model, ...)  # S3 method for ssn_glm influence(model, ...)"},{"path":"/reference/influence.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Regression diagnostics — influence.SSN2","text":"model fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/influence.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Regression diagnostics — influence.SSN2","text":"tibble residuals (.resid), leverage values (.hat), cook's distance (.cooksd), standardized residuals (.std.resid).","code":""},{"path":"/reference/influence.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Regression diagnostics — influence.SSN2","text":"function calls residuals.SSN2(), hatvalues.SSN2(), cooks.distance.SSN2() puts results tibble. primarily used calling augment.SSN2().","code":""},{"path":[]},{"path":"/reference/influence.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regression diagnostics — influence.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) influence(ssn_mod) #> # A tibble: 45 × 4 #>    .resid    .hat    .cooksd .std.resid #>     <dbl>   <dbl>      <dbl>      <dbl> #>  1  -2.96 0.157   0.135         -1.31   #>  2  -3.42 0.0924  0.0184        -0.631  #>  3  -3.45 0.0301  0.0103        -0.827  #>  4  -3.35 0.00798 0.000239      -0.245  #>  5  -3.30 0.0105  0.00000674     0.0359 #>  6  -3.52 0.00350 0.00285       -1.28   #>  7  -3.26 0.0179  0.0000144      0.0401 #>  8  -3.25 0.0169  0.0000741      0.0935 #>  9  -3.40 0.00831 0.000891      -0.463  #> 10  -1.45 0.0561  0.0290        -1.02   #> # ℹ 35 more rows"},{"path":"/reference/labels.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Find labels from object — labels.SSN2","title":"Find labels from object — labels.SSN2","text":"Find suitable set labels fitted model object.","code":""},{"path":"/reference/labels.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find labels from object — labels.SSN2","text":"","code":"# S3 method for ssn_lm labels(object, ...)  # S3 method for ssn_glm labels(object, ...)"},{"path":"/reference/labels.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find labels from object — labels.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/labels.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find labels from object — labels.SSN2","text":"character vector containing terms used fixed effects fitted model object.","code":""},{"path":"/reference/labels.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find labels from object — labels.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) labels(ssn_mod) #> [1] \"ELEV_DEM\""},{"path":"/reference/logLik.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract log-likelihood — logLik.SSN2","title":"Extract log-likelihood — logLik.SSN2","text":"Find log-likelihood fitted model.","code":""},{"path":"/reference/logLik.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract log-likelihood — logLik.SSN2","text":"","code":"# S3 method for ssn_lm logLik(object, ...)  # S3 method for ssn_glm logLik(object, ...)"},{"path":"/reference/logLik.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract log-likelihood — logLik.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/logLik.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract log-likelihood — logLik.SSN2","text":"log-likelihood.","code":""},{"path":"/reference/logLik.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract log-likelihood — logLik.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) logLik(ssn_mod) #> [1] -27.09128"},{"path":"/reference/loocv.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform leave-one-out cross validation — loocv.SSN2","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"Perform leave-one-cross validation options computationally efficient approximations big data.","code":""},{"path":"/reference/loocv.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"","code":"# S3 method for ssn_lm loocv(object, cv_predict = FALSE, se.fit = FALSE, ...)  # S3 method for ssn_glm loocv(object, cv_predict = FALSE, se.fit = FALSE, ...)"},{"path":"/reference/loocv.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). cv_predict logical indicating whether leave-one-fitted values returned. Defaults FALSE. se.fit logical indicating whether leave-one-prediction standard errors returned. Defaults FALSE. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/loocv.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"cv_predict = FALSE se.fit = FALSE, tibble indicating several leave-one-cross validation error metrics. cv_predict = TRUE se.fit = TRUE, list elements: stats, tibble indicating several leave-one-cross validation metrics; cv_predict, numeric vector leave-one-predictions observation (cv_predict = TRUE); se.fit, numeric vector leave-one-prediction standard errors observation (se.fit = TRUE). ssn_lm object, cross validation error metrics : bias: average difference predicted value true value std.bias: average standardized difference predicted value true value MSPE: average squared difference predicted value true value RMSPE: root average squared difference predicted value true value std.MSPE: average standardized squared difference predicted value true value RAV: root average estimated variance predicted value cor2: squared correlation predicted true values cover.80: Coverage rates 80% prediction intervals built true values cover.90: Coverage rates 90% prediction intervals built true values cover.95: Coverage rates 95% prediction intervals built true values ssn_glm object, cross validation error metrics : bias: average difference predicted value true value MSPE: average squared difference predicted value true value RMSPE: root average squared difference predicted value true value RAV: root average estimated variance predicted value (link scale)","code":""},{"path":"/reference/loocv.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"observation held-data set remaining data used make prediction held-observation. compared true value observation several model-fit statistics computed across observations.","code":""},{"path":"/reference/loocv.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) loocv(ssn_mod) #> # A tibble: 1 × 10 #>     bias std.bias  MSPE RMSPE std.MSPE   RAV  cor2 cover.80 cover.90 cover.95 #>    <dbl>    <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>    <dbl>    <dbl>    <dbl> #> 1 0.0917   0.0846 0.235 0.485     1.14 0.384 0.942    0.733    0.844    0.889"},{"path":"/reference/mf04p.html","id":null,"dir":"Reference","previous_headings":"","what":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","title":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","text":"MiddleFork04.ssn data folder contains spatial, attribute, topological information needed construct spatial stream network object using SSN2 package. mf04p created using ssn_import().","code":""},{"path":"/reference/mf04p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","text":"","code":"mf04p"},{"path":"/reference/mf04p.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","text":"object class SSN length 4.","code":""},{"path":[]},{"path":"/reference/MiddleFork04.ssn.html","id":null,"dir":"Reference","previous_headings":"","what":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","title":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","text":"MiddleFork04.ssn data folder contains spatial, attribute, topological information needed construct SSN object using SSN2 package.","code":""},{"path":"/reference/MiddleFork04.ssn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","text":"edges modified version United States National Hydrography Dataset (http://nhd.usgs.gov/). sites, pred1km, CapeHorn Knapp unpublished United States Forest Service data.","code":""},{"path":"/reference/MiddleFork04.ssn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","text":"MiddleFork04.ssn folder contains five shapefiles: edges: polyline shapefile representing stream network sites: point shapefile representing observed site locations pred1km: point shapefile representing prediction site locations approximately 1km intervals throughout stream network Knapp: point shapefile representing prediction site locations Knapp River CapeHorn: point shapefile representing prediction site locations Cape Horn River MiddleFork04.ssn includes one text file, netID1.txt, contains topological information stream network Middle Fork 2004 dataset. distance folder contains four folders store hydrologic distance matrices point shapefiles (obs, CapeHorn, Knapp, pred1km). See ssn_create_distmat() detailed description distance matrix file structure. Attribute data also stored within spatial datasets. column names defined follows: edges: COMID: Common identifier NHD feature relationship GNIS_Name: Feature name found Geographic Names Information System REACHCODE: Unique identifier reach. first 8 digits contain identfier HUC8 last 6 digits unique within-HUC8 identifier reach FTYPE: three-digit integer used classify hydrography features NHD define subtypes FCODE: Numeric code contains feature type attributes found NHDFCode lookup table CDRAINAG: Cumulative drainage area (km2) lowermost location edge AREAWTMAP: Area weighted mean annual precipitation (mm) lowermost location edge SLOPE: Slope edge (cm/cm) h2oAreaKm2: Watershed area (km2) lowermost location line segment rid: Reach identifier areaPI: Segment proportional influence value, calculated using watershed area (h2oAreaKm2) afvArea: Additive function value, calculated using areaPI upDist: Distance stream outlet (downstream location stream network) uppermost location line segment Length: Length line segment (m) netID: Network identifier sites: STREAMNAME: Stream name COMID: Common identifier NHD feature relationship CDRAINAG: Cumulative drainage area (km2) AREAWTMAP: Area weighted mean annual precipitation (mm) lowermost location line segment site resides SLOPE: Slope line segment (cm/cm) site resides ELEV_DEM: Elevation site based 30m DEM Source: Source data - relates ID field source table Summer_mn: Overall summer mean termperature (C) deployment MaxOver20: Binary variable: 1 represents maximum summer temperature greater 20C 0 indicates less 20C C16: Number times daily stream temperature exceeded 16C C20: Number times daily stream temperature exceeded 20C C24: Number times daily stream temperature exceeded 24C FlowCMS: Average stream flow (cubic meters per sec) August, year, 1950-2010 across 9 USGS gauges region AirMEANc: Average mean air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain AirMWMTc: Average maximum air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain. MWMT = maximum 7-day moving average maximum daily temperature (.e. maximum 7-day maximums) NEAR_X: x coordinate NEAR_Y: y coordinate rid: Reach identifier edge site resides ratio: Site ratio value; provides proportional distance along edge site location upDist: Distance upstream stream outlet (m) afvArea: Additive function value calculated using waterhsed area (h2oAreaKm2) locID: Location identifier netID: Stream network identifier pid: Point identifier pred1km, CapeHorn, Knapp: COMID: Common identifier NHD feature relationship GNIS_Name: Feature name edge site resides , found Geographic Names Information System CDRAINAG: Cumulative drainage area (km2) AREAWTMAP: Area weighted mean annual precipitation (mm) lowermost location line segment site resides SLOPE: Slope line segment (cm/cm) site resides ELEV_DEM: Elevation site based 30m DEM NEAR_X: x coordinate NEAR_Y: y coordinate rid: Reach identifier edge site resides ratio: Site ratio value; provides proportional distance along edge site location upDist: Distance upstream stream outlet (m) afvArea: Additive function value calculated using watershed area (h2oAreaKm2) locID: Location identifier netID: Stream network identifier pid: Point identifier FlowCMS: Average stream flow (cubic meters per sec) August, year, 1950-2010 across 9 USGS gauges region AirMEANc: Average mean air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain AirMWMTc: Average maximum air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain. MWMT = maximum 7-day moving average maximum daily temperature(.e. maximum 7-day maximums)","code":""},{"path":[]},{"path":"/reference/model.frame.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model frame from a fitted model object — model.frame.SSN2","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"Extract model frame fitted model object.","code":""},{"path":"/reference/model.frame.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"","code":"# S3 method for ssn_lm model.frame(formula, ...)  # S3 method for ssn_glm model.frame(formula, ...)"},{"path":"/reference/model.frame.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"formula fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/model.frame.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"model frame contains variables used formula fitted model object.","code":""},{"path":[]},{"path":"/reference/model.frame.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) model.frame(ssn_mod) #>    Summer_mn ELEV_DEM #> 1      11.38     1977 #> 2      10.72     1984 #> 3      10.43     1993 #> 4      10.13     2007 #> 5      10.12     2009 #> 6       9.81     2012 #> 7       9.76     2023 #> 8       9.77     2023 #> 9       9.53     2026 #> 10     12.57     1988 #> 11     12.42     2013 #> 12     14.10     2015 #> 13     13.22     2038 #> 14     13.08     2052 #> 15     12.19     2066 #> 16     11.32     2085 #> 17     12.11     1975 #> 18     11.82     1982 #> 19     11.91     1999 #> 20     11.41     2002 #> 21     11.71     1997 #> 22     11.54     2006 #> 23     11.14     2026 #> 24     12.06     2036 #> 25     12.09     2046 #> 26     11.96     2050 #> 27     11.02     2071 #> 28     11.16     2016 #> 29      8.75     2034 #> 30      9.02     2042 #> 31     14.61     1958 #> 32     14.92     1947 #> 33     14.72     1952 #> 34     15.22     1923 #> 35     14.49     1932 #> 36     15.29     1940 #> 37     15.07     1940 #> 38     14.94     1945 #> 39     14.99     1948 #> 40     15.02     1950 #> 41     15.11     1949 #> 42     14.70     1950 #> 43     14.58     1951 #> 44     13.70     2015 #> 45     10.21     2077"},{"path":"/reference/model.matrix.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model matrix from a fitted model object — model.matrix.SSN2","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"Extract model matrix (X) fitted model object.","code":""},{"path":"/reference/model.matrix.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"","code":"# S3 method for ssn_lm model.matrix(object, ...)  # S3 method for ssn_glm model.matrix(object, ...)"},{"path":"/reference/model.matrix.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/model.matrix.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"model matrix (fixed effects), whose rows represent observations whose columns represent explanatory variables corresponding fixed effect.","code":""},{"path":[]},{"path":"/reference/model.matrix.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) model.matrix(ssn_mod) #>    (Intercept) ELEV_DEM #> 1            1     1977 #> 2            1     1984 #> 3            1     1993 #> 4            1     2007 #> 5            1     2009 #> 6            1     2012 #> 7            1     2023 #> 8            1     2023 #> 9            1     2026 #> 10           1     1988 #> 11           1     2013 #> 12           1     2015 #> 13           1     2038 #> 14           1     2052 #> 15           1     2066 #> 16           1     2085 #> 17           1     1975 #> 18           1     1982 #> 19           1     1999 #> 20           1     2002 #> 21           1     1997 #> 22           1     2006 #> 23           1     2026 #> 24           1     2036 #> 25           1     2046 #> 26           1     2050 #> 27           1     2071 #> 28           1     2016 #> 29           1     2034 #> 30           1     2042 #> 31           1     1958 #> 32           1     1947 #> 33           1     1952 #> 34           1     1923 #> 35           1     1932 #> 36           1     1940 #> 37           1     1940 #> 38           1     1945 #> 39           1     1948 #> 40           1     1950 #> 41           1     1949 #> 42           1     1950 #> 43           1     1951 #> 44           1     2015 #> 45           1     2077 #> attr(,\"assign\") #> [1] 0 1"},{"path":"/reference/names.SSN.html","id":null,"dir":"Reference","previous_headings":"","what":"names SSN object — names.SSN","title":"names SSN object — names.SSN","text":"Extract print names SSN object","code":""},{"path":"/reference/names.SSN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"names SSN object — names.SSN","text":"","code":"# S3 method for SSN names(x, ...)"},{"path":"/reference/names.SSN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"names SSN object — names.SSN","text":"x SSN object. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/names.SSN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"names SSN object — names.SSN","text":"Print variable names console","code":""},{"path":"/reference/plot.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot fitted model diagnostics — plot.SSN2","title":"Plot fitted model diagnostics — plot.SSN2","text":"Plot fitted model diagnostics residuals vs fitted values, quantile-quantile, scale-location, Cook's distance, residuals vs leverage, Cook's distance vs leverage.","code":""},{"path":"/reference/plot.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot fitted model diagnostics — plot.SSN2","text":"","code":"# S3 method for ssn_lm plot(x, which, ...)  # S3 method for ssn_glm plot(x, which, ...)"},{"path":"/reference/plot.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot fitted model diagnostics — plot.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). integer vector taking values 1 6, indicates plots return. Available plots described Details. length greater one, additional plots stepped order using <Return>. default = c(1, 2) ... arguments passed methods.","code":""},{"path":"/reference/plot.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot fitted model diagnostics — plot.SSN2","text":"return value. Function called plotting side effects.","code":""},{"path":"/reference/plot.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot fitted model diagnostics — plot.SSN2","text":"fitted model objects,, values make corresponding plot: 1: Standardized residuals vs fitted values (response) 2: Normal quantile-quantile plot standardized residuals 3: Scale-location plot standardized residuals 4: Cook's distance 5: Standardized residuals vs leverage 6: Cook's distance vs leverage","code":""},{"path":[]},{"path":"/reference/plot.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot fitted model diagnostics — plot.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) plot(ssn_mod, which = 1)"},{"path":"/reference/plot.Torgegram.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Torgegram — plot.Torgegram","title":"Plot Torgegram — plot.Torgegram","text":"Plot Torgegram","code":""},{"path":"/reference/plot.Torgegram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Torgegram — plot.Torgegram","text":"","code":"# S3 method for Torgegram plot(x, type, separate = FALSE, ...)"},{"path":"/reference/plot.Torgegram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Torgegram — plot.Torgegram","text":"x Torgegram object Torgegram(). type type semivariogram. Can take character values subset objects x. default names(x). separate type length greater one, whether type placed separate plot. default FALSE. ... arguments passed methods.","code":""},{"path":"/reference/plot.Torgegram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Torgegram — plot.Torgegram","text":"return value. Function called plotting side effects.","code":""},{"path":[]},{"path":"/reference/plot.Torgegram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Torgegram — plot.Torgegram","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  tg <- Torgegram(Summer_mn ~ 1, mf04p) plot(tg)"},{"path":"/reference/predict.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Model predictions (Kriging) — predict.SSN2","title":"Model predictions (Kriging) — predict.SSN2","text":"Predicted values intervals based fitted model object.","code":""},{"path":"/reference/predict.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model predictions (Kriging) — predict.SSN2","text":"","code":"# S3 method for ssn_lm predict(   object,   newdata,   se.fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   level = 0.95,   block = FALSE,   ... )  # S3 method for ssn_glm predict(   object,   newdata,   type = c(\"link\", \"response\"),   se.fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   newdata_size,   level = 0.95,   var_correct = TRUE,   ... )"},{"path":"/reference/predict.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model predictions (Kriging) — predict.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). newdata character vector indicates name prediction data set SSN object predictions desired. omitted, predictions prediction data sets returned. Note name \".missing\" indicates prediction data set contains missing observations data used fit model. se.fit logical indicating standard errors returned. default FALSE. interval Type interval calculation. default \"none\". options \"confidence\" (confidence intervals) \"prediction\" (prediction intervals). level Tolerance/confidence level. default 0.95. block logical indicating whether block prediction entire region newdata returned. default FALSE, returns point predictions location newdata. Currently available model fit using ssn_lm() models fit using ssn_glm() family \"gaussian\". ... arguments. used (needed generic consistency). type scale (response link) predictions obtained using ssn_glm objects. newdata_size size value observation newdata used predicting binomial family. var_correct logical indicating whether return corrected prediction variances predicting via models fit using ssn_glm. default TRUE.","code":""},{"path":"/reference/predict.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model predictions (Kriging) — predict.SSN2","text":"se.fit FALSE, predict.ssn() returns vector predictions matrix predictions column names fit, lwr, upr interval \"confidence\" \"prediction\". se.fit TRUE, list following components returned: fit: vector matrix se.fit: standard error fit","code":""},{"path":"/reference/predict.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model predictions (Kriging) — predict.SSN2","text":"(empirical) best linear unbiased predictions (.e., Kriging predictions) site returned interval \"none\" \"prediction\" alongside standard errors. Prediction intervals also returned interval \"prediction\". interval \"confidence\", estimated mean returned alongside standard errors confidence intervals mean.","code":""},{"path":"/reference/predict.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model predictions (Kriging) — predict.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"pred1km\", overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) predict(ssn_mod, \"pred1km\") #>          1          2          3          4          5          6          7  #> 14.6443346 15.0150979 14.7513549 14.9717229 15.1513874 15.1159790 15.1069520  #>          8          9         10         11         12         13         14  #> 14.9863186 14.9038332 14.8702627 14.7308979 14.6992062 14.7579446 14.8764063  #>         15         16         17         18         19         20         21  #> 14.3287719 14.4149823 14.0532325 13.9397314 10.9750633 13.1067606 13.6244111  #>         22         23         24         25         26         27         28  #> 10.0996588  5.6054226  8.1409677 12.9269053 13.5019980 11.3419554 11.9747664  #>         29         30         31         32         33         34         35  #> 11.2154309 10.2149138 10.4816809 10.7348106  9.7103402 11.3759625 12.2120558  #>         36         37         38         39         40         41         42  #> 12.9616826 12.5664805 12.3028149 13.8079249 13.5395082 12.9715817 13.2152150  #>         43         44         45         46         47         48         49  #> 12.3273384 12.5850380 10.7406072 10.9713095 11.2308341  8.6629974  9.3260313  #>         50         51         52         53         54         55         56  #> 10.2772878 12.1296929 11.7154947 11.1310275 11.5790357 11.7855844 12.0521877  #>         57         58         59         60         61         62         63  #> 11.2655590  9.7898915  8.0483307  8.7966695  9.1037886  8.5118636 11.4956913  #>         64         65         66         67         68         69         70  #> 11.9862160 11.6161901 12.6246359 12.4406987 10.6278901 11.1747555 11.6063313  #>         71         72         73         74         75         76         77  #> 13.9269581 14.4813989 14.5959969 10.1604842 12.4655751 13.8195311 10.1977736  #>         78         79         80         81         82         83         84  #> 14.5789082 15.4149064 15.5879931 13.6734603 14.5383442 15.1150059 15.3746230  #>         85         86         87         88         89         90         91  #> 10.0788306  7.9453413  8.7202052  7.5438136 11.1468478  9.4793850 10.2847461  #>         92         93         94         95         96         97         98  #>  1.5531965  5.4715321  7.2281978  8.6101730  5.0354923 11.3558953 11.7586313  #>         99        100        101        102        103        104        105  #> 12.2766556 12.5929234  9.8923651  9.3683314  6.6259182 12.7363403  4.3346894  #>        106        107        108        109        110        111        112  #>  3.8724173  3.5215224  7.0452774  8.3977344  7.1561650 -1.1426926  1.4202968  #>        113        114        115        116        117        118        119  #>  4.0985733  6.4886255  7.8698972  7.5852381  9.9485049  3.7673801  5.5245583  #>        120        121        122        123        124        125        126  #>  8.5264446 15.2798374 14.2197123  9.2990140 11.5461684 15.5970950 14.1134907  #>        127        128        129        130        131        132        133  #> 15.2664073  6.0647130  8.3993355 10.3304465  8.0105836 10.6329256 12.2753107  #>        134        135        136        137        138        139        140  #> 12.3778096 14.7132922 16.0976413 14.2078071  9.4203169 12.1022897  5.5831669  #>        141        142        143        144        145        146        147  #>  8.6941805 11.3728589  9.3402885 11.3857715 15.1158602 15.2888149 11.5265537  #>        148        149        150        151        152        153        154  #> 12.5079422 10.3691975 -0.6748125  2.9279685  6.7325051  9.3265074 12.2087322  #>        155        156        157        158        159        160        161  #> 14.2262895  7.5221930 10.5192746 12.5652217  6.8460851 12.3513293  1.0470183  #>        162        163        164        165        166        167        168  #> 12.9732758  8.9524078 11.7760066 11.4752712  7.9069483 11.0199167 11.3624102  #>        169        170        171        172        173        174        175  #> 11.8228944 11.9951556 12.2250606  8.7431663  9.2898993  2.3113801  6.7492847"},{"path":"/reference/print.SSN.html","id":null,"dir":"Reference","previous_headings":"","what":"Print SSN object — print.SSN","title":"Print SSN object — print.SSN","text":"Print information data found SSN object.","code":""},{"path":"/reference/print.SSN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print SSN object — print.SSN","text":"","code":"# S3 method for SSN print(x, ...)"},{"path":"/reference/print.SSN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print SSN object — print.SSN","text":"x SSN object. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/print.SSN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print SSN object — print.SSN","text":"Print summary console","code":""},{"path":"/reference/print.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Print values — print.SSN2","title":"Print values — print.SSN2","text":"Print fitted model objects summaries.","code":""},{"path":"/reference/print.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print values — print.SSN2","text":"","code":"# S3 method for ssn_lm print(x, digits = max(3L, getOption(\"digits\") - 3L), ...)  # S3 method for ssn_glm print(x, digits = max(3L, getOption(\"digits\") - 3L), ...)  # S3 method for summary.ssn_lm print(   x,   digits = max(3L, getOption(\"digits\") - 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )  # S3 method for summary.ssn_glm print(   x,   digits = max(3L, getOption(\"digits\") - 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )  # S3 method for anova.ssn_lm print(   x,   digits = max(getOption(\"digits\") - 2L, 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )  # S3 method for anova.ssn_glm print(   x,   digits = max(getOption(\"digits\") - 2L, 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )"},{"path":"/reference/print.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print values — print.SSN2","text":"x fitted model object ssn_lm(), fitted model object ssn_glm(), output summary(x) anova(x). digits number significant digits use printing. ... arguments passed methods. signif.stars Logical. TRUE, significance stars printed coefficient","code":""},{"path":"/reference/print.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print values — print.SSN2","text":"Printed fitted model objects summaries formatting.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a pseudo r-squared — pseudoR2.SSN2","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"Compute pseudo r-squared fitted model object.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"","code":"# S3 method for ssn_lm pseudoR2(object, adjust = FALSE, ...)  # S3 method for ssn_glm pseudoR2(object, adjust = FALSE, ...)"},{"path":"/reference/pseudoR2.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). adjust logical indicating whether pseudo r-squared adjusted account number explanatory variables. default FALSE. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"pseudo r-squared numeric vector.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"Several pseudo r-squared statistics exist literature. define pseudo r-squared one minus ratio deviance full model relative deviance null (intercept ) model. pseudo r-squared can viewed generalization classical r-squared definition seen one minus ratio error sums squares full model relative error sums squares null model. adjusted, adjustment analogous classical r-squared adjustment.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) pseudoR2(ssn_mod) #> [1] 0.6688124"},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics augment, glance, tidy spmodel AICc, covmatrix, dispersion_initial, dispersion_params, glances, loocv, pseudoR2, randcov_initial, randcov_params, varcomp","code":""},{"path":"/reference/residuals.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted model residuals — residuals.SSN2","title":"Extract fitted model residuals — residuals.SSN2","text":"Extract residuals fitted model object. resid alias.","code":""},{"path":"/reference/residuals.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted model residuals — residuals.SSN2","text":"","code":"# S3 method for ssn_lm residuals(object, type = \"response\", ...)  # S3 method for ssn_lm resid(object, type = \"response\", ...)  # S3 method for ssn_lm rstandard(model, ...)  # S3 method for ssn_glm residuals(object, type = \"deviance\", ...)  # S3 method for ssn_glm resid(object, type = \"deviance\", ...)  # S3 method for ssn_glm rstandard(model, ...)"},{"path":"/reference/residuals.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted model residuals — residuals.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). type \"response\" response residuals, \"pearson\" Pearson residuals, \"standardized\" standardized residuals. ssn_lm() fitted model objects, default \"response\". ssn_glm() fitted model objects, deviance residuals also available (\"deviance\") default residual type. ... arguments. used (needed generic consistency). model fitted model object ssn_lm() ssn_glm().","code":""},{"path":"/reference/residuals.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract fitted model residuals — residuals.SSN2","text":"residuals numeric vector.","code":""},{"path":"/reference/residuals.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract fitted model residuals — residuals.SSN2","text":"response residuals taken response minus fitted values response: \\(y - X \\hat{\\beta}\\). Pearson residuals response residuals pre-multiplied inverse square root. standardized residuals Pearson residuals divided square root one minus leverage (hat) value. standardized residuals often used check model assumptions, mean zero variance approximately one. rstandard() alias residuals(model, type = \"standardized\").","code":""},{"path":"/reference/residuals.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted model residuals — residuals.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) residuals(ssn_mod) #>           1           2           3           4           5           6  #> -2.96157851 -3.41982277 -3.45042255 -3.34691108 -3.29926659 -3.52279984  #>           7           8           9          10          11          12  #> -3.25575512 -3.24575512 -3.39928838 -1.45453378 -0.88397759  0.85366690  #>          13          14          15          16          17          18  #>  0.63657859  0.90009006  0.41360153  0.09122423 -2.28922300 -2.37746727  #>          19          20          21          22          23          24  #> -1.79748906 -2.21102232 -2.05513356 -1.96573333 -1.78928838 -0.58106590  #>          25          26          27          28          29          30  #> -0.26284342 -0.27755443 -0.61228724 -2.05751085 -3.94871040 -3.44813242  #>          31          32          33          34          35          36  #> -0.27920121 -0.28624593 -0.34213470 -0.67797988 -1.14857965 -0.11800167  #>          37          38          39          40          41          42  #> -0.33800167 -0.32389043 -0.18742369 -0.09977919 -0.03860144 -0.41977919  #>          43          44          45  #> -0.51095694  0.45366690 -1.24935375  resid(ssn_mod) #>           1           2           3           4           5           6  #> -2.96157851 -3.41982277 -3.45042255 -3.34691108 -3.29926659 -3.52279984  #>           7           8           9          10          11          12  #> -3.25575512 -3.24575512 -3.39928838 -1.45453378 -0.88397759  0.85366690  #>          13          14          15          16          17          18  #>  0.63657859  0.90009006  0.41360153  0.09122423 -2.28922300 -2.37746727  #>          19          20          21          22          23          24  #> -1.79748906 -2.21102232 -2.05513356 -1.96573333 -1.78928838 -0.58106590  #>          25          26          27          28          29          30  #> -0.26284342 -0.27755443 -0.61228724 -2.05751085 -3.94871040 -3.44813242  #>          31          32          33          34          35          36  #> -0.27920121 -0.28624593 -0.34213470 -0.67797988 -1.14857965 -0.11800167  #>          37          38          39          40          41          42  #> -0.33800167 -0.32389043 -0.18742369 -0.09977919 -0.03860144 -0.41977919  #>          43          44          45  #> -0.51095694  0.45366690 -1.24935375  rstandard(ssn_mod) #>           1           2           3           4           5           6  #> -1.31417513 -0.63086161 -0.82725055 -0.24495775  0.03588943 -1.27769827  #>           7           8           9          10          11          12  #>  0.04013139  0.09354178 -0.46311916 -1.01690582 -1.17757570  2.07589613  #>          13          14          15          16          17          18  #>  0.48880470  2.10969532 -0.63391890 -0.79209177 -1.13939584 -1.70158717  #>          19          20          21          22          23          24  #>  1.19810814 -1.25985662 -0.31945666 -0.45882772 -0.27215334  1.97924217  #>          25          26          27          28          29          30  #>  0.44190300  0.40506009 -0.76461961  0.17969367 -1.35425063 -1.70206611  #>          31          32          33          34          35          36  #>  0.13160477  0.08275306 -0.25793553  0.49251121 -2.33954032  1.22724195  #>          37          38          39          40          41          42  #> -0.06535745  0.03484793 -0.03803679  0.52173449  1.15188186 -0.57040629  #>          43          44          45  #> -1.03295041 -0.49183235 -0.56160115"},{"path":"/reference/SSN2-package.html","id":null,"dir":"Reference","previous_headings":"","what":"SSN2: Spatial Modeling on Stream Networks — SSN2-package","title":"SSN2: Spatial Modeling on Stream Networks — SSN2-package","text":"Spatial statistical modeling prediction data stream networks, including models based -stream distance (Ver Hoef, J.M. Peterson, E.E., (2010) doi:10.1198/jasa.2009.ap08248 .) Models created using moving average constructions. Spatial linear models, including explanatory variables, can fit (restricted) maximum likelihood. Mapping graphical functions included.","code":""},{"path":[]},{"path":"/reference/SSN2-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SSN2: Spatial Modeling on Stream Networks — SSN2-package","text":"Maintainer: Michael Dumelle Dumelle.Michael@epa.gov (ORCID) Authors: Jay M. Ver Hoef jay.verhoef@noaa.gov Erin Peterson contributors: Alan Pearse [contributor] Dan Isaak [contributor]","code":""},{"path":"/reference/ssn_create_distmat.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"Creates collection (non-symmetric) matrices containing pairwise downstream hydrologic distances sites SSN object","code":""},{"path":"/reference/ssn_create_distmat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"","code":"ssn_create_distmat(   ssn.object,   predpts = NULL,   overwrite = FALSE,   among_predpts = FALSE,   only_predpts = FALSE )"},{"path":"/reference/ssn_create_distmat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"ssn.object SSN object predpts name prediction points SSN object. vector length greater one, name iterated upon. Default NULL. overwrite Logical. TRUE, overwrite existing distance matrices. Defaults FALSE. among_predpts Logical. TRUE, compute pairwise distances prediction sites. Defaults FALSE. only_predpts Logical. TRUE, compute distances prediction sites. Defaults FALSE.","code":""},{"path":"/reference/ssn_create_distmat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"ssn_create_distmat function creates collection hierarchical directories ssn$path directory, store pairwise distances sites associated SSN object. See details section additional information.","code":""},{"path":"/reference/ssn_create_distmat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"distance matrix contains hydrologic distance two sites SSN object needed fit spatial statistical model using tail-tail-autocovariance functions described Ver Hoef Peterson (2010). models implemented R via ssn_lm ssn_glm theSSN2 package. hydrologic distance information needed model covariance flow-connected (.e. water flows one location ) flow-unconnected (.e. water flow one location , reside network) locations differs. total hydrologic distance directionless measure; represents hydrologic distance two sites, ignoring flow direction. hydrologic distance site common downstream stream junction used creating models flow-unconnected pairs, term downstream hydrologic distance. contrast, total hydrologic distance used modeling flow-connected pairs, term total hydrologic distance. downstream hydrologic distance matrix provides enough information meet data requirements tail-tail-models. two locations flow-connected, downstream hydrologic distance upstream location downstream location greater zero, zero direction. two locations flow-unconnected downstream hydrologic distance greater zero directions. site's downstream hydrologic distance equal zero. format downstream hydrologic distance matrix efficient distance information needed fit tail-tail-models stored . example, matrix containing total hydrologic distance sites easily calculated adding downstream distance matrix transpose. downstream hydrologic distances calculated based binaryIDs stored matrices. matrices stored directory named ‘distance’, created ssn_create_distmat function within .ssn directory. distance directory always contain least one directory named ‘obs’, contains number .RData files, one network observed sites residing . naming convention files based netID number (e.g. dist.net1.RData). matrix ‘obs’ folder contains information form square matrix, contains downstream hydrologic distance pair observed sites network. Direction preserved, columns representing site rows representing site. Row column names correspond pid attribute site. argument predpts specified call function, downstream hydrologic distances observed prediction sites also computed. new directory created within distance directory, name corresponding names attribute preds (e.g. attributes(ssn.object$preds)$names). sequence .RData files created within directory, similar structure observed sites, except two objects stored network contains observed prediction sites. letters b used naming convention distinguish two objects (e.g. dist.net1.dist.net1.b). matrices objects represent necessarily square. matrices type , rows correspond observed locations columns prediction locations. contrast, rows correspond prediction locations columns observed locations matrices type b. Direction also preserved, columns representing site rows representing site object types. , row column names correspond pid attribute site. among_predpts = TRUE, downstream hydrologic distances also computed prediction sites, network. stored within distance directory name corresponding prediction points dataset. naming convention prediction prediction site distance matrices distance matrices stored ‘obs’ directory (e.g. dist.net1.RData). extra distance matrices needed perform block Kriging using predict.ssn_lm. only_predpts = TRUE, downstream hydrologic distances calculated observed sites . Pairwise distances calculated observed prediction locations . Pairwise distances prediction locations also calculated among_predpts = TRUE.","code":""},{"path":"/reference/ssn_create_distmat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"","code":"## Copy the MiddleForke04.ssn data to a local temporary directory. ## Only needed for this example. copy_lsn_to_temp() ## Import SSN data mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(\"pred1km.shp\", \"Knapp\"),   overwrite = TRUE )  ## Create distance matrices for observations and one set of prediction sites ## Include hydrologic distance matrices among prediction sites. ssn_create_distmat(mf04p,   predpts = \"pred1km\", overwrite = TRUE,   among_predpts = TRUE )  ## Create distance matrices for an additional set of prediction points. ## Distance matrices for observations and pred1km prediction sites are ## not recalculated. ssn_create_distmat(mf04p,   predpts = \"Knapp\", overwrite = TRUE,   among_predpts = TRUE, only_predpts = TRUE )"},{"path":"/reference/ssn_get_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"ssn_get_data function extracts sf data.frame observation prediction data SSN, ssn_lm, ssn_glm object.","code":""},{"path":"/reference/ssn_get_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"","code":"ssn_get_data(x, name = \"obs\")"},{"path":"/reference/ssn_get_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"x object class SSN, ssn_lm, ssn_glm. name internal name dataset object x. observed values, always \"obs\", default.","code":""},{"path":"/reference/ssn_get_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"sf data.frame","code":""},{"path":"/reference/ssn_get_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"internal name observed data objects class SSN \"obs\" default. another name specified, must represent prediction data set SSN, ssn_lm, ssn_glm object. SSN objects, names obtained using call names(x$preds). object classes, names obtained using call names(x$ssn.object$preds).","code":""},{"path":[]},{"path":"/reference/ssn_get_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"","code":"## Extract observed data from an SSN object # Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"pred1km\", overwrite = TRUE)  obs.df <- ssn_get_data(mf04p) dim(obs.df) #> [1] 45 26  ## Extract prediction data from an SSN object names(mf04p$preds) #> [1] \"pred1km\" pred1km.df <- ssn_get_data(mf04p, name = \"pred1km\") names(pred1km.df) #>  [1] \"COMID\"     \"GNIS_NAME\" \"CDRAINAG\"  \"AREAWTMAP\" \"SLOPE\"     \"ELEV_DEM\"  #>  [7] \"FlowCMS\"   \"AirMEANc\"  \"AirMWMTc\"  \"NEAR_X\"    \"NEAR_Y\"    \"rid\"       #> [13] \"ratio\"     \"afvArea\"   \"upDist\"    \"locID\"     \"netID\"     \"pid\"       #> [19] \"geometry\"  \"netgeom\"    ## extract observed data from an ssn_lm object ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) obs.mod.df <- ssn_get_data(ssn_mod) summary(obs.mod.df) #>   STREAMNAME            COMID             CDRAINAG         AREAWTMAP      #>  Length:45          Min.   :23519297   Min.   :  8.643   Min.   : 786.4   #>  Class :character   1st Qu.:23519365   1st Qu.: 60.136   1st Qu.: 968.2   #>  Mode  :character   Median :23519479   Median : 93.295   Median : 995.2   #>                     Mean   :23519557   Mean   :136.482   Mean   : 998.6   #>                     3rd Qu.:23519529   3rd Qu.:171.534   3rd Qu.:1032.7   #>                     Max.   :23522805   Max.   :477.696   Max.   :1130.4   #>      SLOPE             ELEV_DEM       Source            Summer_mn     #>  Min.   :0.000000   Min.   :1923   Length:45          Min.   : 8.75   #>  1st Qu.:0.002740   1st Qu.:1952   Class :character   1st Qu.:11.02   #>  Median :0.005680   Median :2006   Mode  :character   Median :12.06   #>  Mean   :0.006743   Mean   :1999                      Mean   :12.35   #>  3rd Qu.:0.008430   3rd Qu.:2026                      3rd Qu.:14.58   #>  Max.   :0.044260   Max.   :2085                      Max.   :15.29   #>    MaxOver20           C16            C20              C24       FlowCMS      #>  Min.   :0.0000   Min.   : 0.0   Min.   : 0.000   Min.   :0   Min.   :28.67   #>  1st Qu.:0.0000   1st Qu.:17.0   1st Qu.: 0.000   1st Qu.:0   1st Qu.:28.67   #>  Median :0.0000   Median :32.0   Median : 0.000   Median :0   Median :28.67   #>  Mean   :0.3111   Mean   :26.4   Mean   : 2.867   Mean   :0   Mean   :28.67   #>  3rd Qu.:1.0000   3rd Qu.:39.0   3rd Qu.: 3.000   3rd Qu.:0   3rd Qu.:28.67   #>  Max.   :1.0000   Max.   :41.0   Max.   :19.000   Max.   :0   Max.   :28.67   #>     AirMEANc        AirMWMTc        NEAR_X             NEAR_Y        #>  Min.   :21.12   Min.   :35.1   Min.   :-1530805   Min.   :2527111   #>  1st Qu.:21.12   1st Qu.:35.1   1st Qu.:-1520336   1st Qu.:2529903   #>  Median :21.12   Median :35.1   Median :-1512691   Median :2533285   #>  Mean   :21.12   Mean   :35.1   Mean   :-1514996   Mean   :2532817   #>  3rd Qu.:21.12   3rd Qu.:35.1   3rd Qu.:-1508544   3rd Qu.:2535462   #>  Max.   :21.12   Max.   :35.1   Max.   :-1503079   Max.   :2537823   #>       rid             ratio          afvArea              upDist        #>  Min.   :  0.00   Min.   :0.0143   Length:45          Min.   :  909.9   #>  1st Qu.: 20.00   1st Qu.:0.1967   Class :character   1st Qu.: 6281.2   #>  Median : 41.00   Median :0.4720   Mode  :character   Median :10020.0   #>  Mean   : 41.27   Mean   :0.4564                      Mean   :10176.7   #>  3rd Qu.: 60.00   3rd Qu.:0.6936                      3rd Qu.:14295.2   #>  Max.   :109.00   Max.   :0.9739                      Max.   :19566.2   #>      locID        netID            pid              geometry  #>  Min.   : 1   Min.   :1.000   Min.   : 1   POINT        :45   #>  1st Qu.:12   1st Qu.:1.000   1st Qu.:12   epsg:NA      : 0   #>  Median :23   Median :2.000   Median :23   +proj=aea ...: 0   #>  Mean   :23   Mean   :1.711   Mean   :23                      #>  3rd Qu.:34   3rd Qu.:2.000   3rd Qu.:34                      #>  Max.   :45   Max.   :2.000   Max.   :45                      #>    netgeom          #>  Length:45          #>  Class :character   #>  Mode  :character   #>                     #>                     #>"},{"path":"/reference/ssn_get_netgeom.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract netgeom column — ssn_get_netgeom","title":"Extract netgeom column — ssn_get_netgeom","text":"Extract topological information netgeom column","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract netgeom column — ssn_get_netgeom","text":"","code":"ssn_get_netgeom(x, netvars = \"all\", reformat = FALSE)"},{"path":"/reference/ssn_get_netgeom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract netgeom column — ssn_get_netgeom","text":"x sf data.frame found SSN object netgeom column vector netvars Network coordinate variables return. Default \"\". edges, valid column names include: \"NetworkID\", \"SegmentID\", \"DistanceUpstream\". point datasets, valid column names include \"NetworkID\", \"SegmentID\", \"DistanceUpstream\", \"ratio\", \"pid\", \"locID\". reformat Convert network coordinate variables character numeric.","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract netgeom column — ssn_get_netgeom","text":"one column requested using netvars, function returns data.frame (default). one column requested, result vector.","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract netgeom column — ssn_get_netgeom","text":"SSN object generated using importSSN function, text column named \"netgeom\" added edges, observed sites, prediction sites (exist) data.frames. netgeom column contains data used describe edge site features relate one another topological space. edges, netgeom values contain \"ENETWORK\" prefix, 3 space delimited values parentheses: \"ENETWORK (NetworkID SegmentID DistanceUpstream)\". point datasets (observed prediction sites), values contain \"SNETWORK\" prefix, followed 6 space delimited values parentheses: \"SNETWORK (NetworkID SegmentID DistanceUpstream ratio pid locID)\". ssn_get_netgeom function extracts converts values text numeric, returning either data.frame (default) vector containing variables requested via netvars.","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract netgeom column — ssn_get_netgeom","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_get_netgeom(mf04p$obs) #>    NetworkID SegmentID DistanceUpstream           ratio pid locID #> 1          2        32    3194.82758834  0.321185040918   1     1 #> 2          2        33    3896.10674037 0.0690361937938   2     2 #> 3          2        33    4857.50889816  0.370693074276   3     3 #> 4          2        33    5886.68489225  0.693615191033   4     4 #> 5          2        33    6281.22686216   0.81740969149   5     5 #> 6          2        35    7300.18896771  0.472020290969   6     6 #> 7          2        36    8386.87883527  0.467609736278   7     7 #> 8          2        36    8626.98482356  0.595431065021   8     8 #> 9          2        38    10019.9727397  0.481795562545   9     9 #> 10         2        41    4049.94559903  0.241892607278  10    10 #> 11         2        47    8914.29845837  0.356571475119  11    11 #> 12         2        48     9295.3266467  0.223896901083  12    12 #> 13         2        51    12659.9947331  0.171145194729  13    13 #> 14         2        51    14103.2031154  0.799102017173  14    14 #> 15         2        52    16019.2025746  0.601882501114  15    15 #> 16         2        54    18447.5789988  0.132889204232  16    16 #> 17         2        57    3405.72135314  0.609185232313  17    17 #> 18         2        57    4046.04417336  0.973908806041  18    18 #> 19         2        59    5365.42224132  0.845110196768  19    19 #> 20         2        60    6489.59766227  0.885802604708  20    20 #> 21         2        60    5794.51481162  0.258397975972  21    21 #> 22         2        62    7547.78605505  0.128723349502  22    22 #> 23         2        63    10022.9334715   0.70355047499  23    23 #> 24         2        65     10879.708177  0.078434677104  24    24 #> 25         2        66    11985.9719107 0.0893958570497  25    25 #> 26         2        66    12357.0088971  0.242744463686  26    26 #> 27         2        67    15080.7113189  0.665890884643  27    27 #> 28         2        70    9921.48731634  0.881800162966  28    28 #> 29         2        94     10871.508857  0.196749884783  29    29 #> 30         2       109    11795.0008893 0.0160819902947  30    30 #> 31         1         0    16786.5620802   0.79067935202  31    31 #> 32         1         0    14295.1963452 0.0143033570212  32    32 #> 33         1         0    16258.1596546  0.626015066404  33    33 #> 34         1         4    909.868496941  0.231794110614  34    34 #> 35         1         4    1692.25764135  0.515501826192  35    35 #> 36         1         9    8046.69965981  0.280932180239  36    36 #> 37         1        12    10443.8838685  0.613552417712  37    37 #> 38         1        14    13865.3815622  0.907439930469  38    38 #> 39         1        16    14323.7279441  0.182376231574  39    39 #> 40         1        16     14580.785008  0.812239349337  40    40 #> 41         1        18    15652.4273972   0.54977020396  41    41 #> 42         1        20    18642.8233007  0.157200906378  42    42 #> 43         1        20    19566.2045371  0.639335640746  43    43 #> 44         2        48    9711.54632884  0.751115282016  44    44 #> 45         2        68    15672.8306471 0.0751878474962  45    45 ssn_get_netgeom(mf04p$edges, \"DistanceUpstream\") #>     DistanceUpstream #> 1      17458.2653443 #> 2      18357.2013474 #> 3      20793.1520844 #> 4      270.643033817 #> 5      3028.37256124 #> 6      4402.46334121 #> 7       5256.1104882 #> 8      6616.51936215 #> 9      7886.30445018 #> 10     8457.24370497 #> 11     9024.57776818 #> 12     9536.51353251 #> 13      11015.393429 #> 14      12325.976268 #> 15     14022.4029322 #> 16     14249.2973301 #> 17     14657.4130884 #> 18     14790.6182827 #> 19     16358.1988978 #> 20     18341.7531704 #> 21      20256.946519 #> 22     8056.91207735 #> 23     9607.96481233 #> 24     13105.9441562 #> 25     14112.2130889 #> 26     16613.9926597 #> 27     5611.09385126 #> 28     7959.14006425 #> 29     1556.81337221 #> 30     1920.27673835 #> 31     2336.21218485 #> 32     2967.11873356 #> 33     3676.08342921 #> 34     6863.15529791 #> 35     7113.95880148 #> 36     7508.49727746 #> 37     9386.94738161 #> 38     9805.94712673 #> 39     10250.1720836 #> 40     10289.8733396 #> 41     13963.1651297 #> 42      5221.6543089 #> 43     5793.50030353 #> 44     6280.45578932 #> 45     7277.66171417 #> 46     7449.52533783 #> 47     8801.09743647 #> 48     9118.56820965 #> 49     9908.03173594 #> 50     11074.7483485 #> 51     12266.6584864 #> 52     14564.9190207 #> 53     16981.1440334 #> 54     17918.0916672 #> 55     21902.5186538 #> 56      25336.584133 #> 57     26164.3010726 #> 58     4091.85088316 #> 59     4586.16095742 #> 60     5508.24341114 #> 61     6616.11354599 #> 62     7197.21170564 #> 63     9920.68301648 #> 64     10066.0179407 #> 65     10803.9630453 #> 66     11769.6727859 #> 67       14189.23821 #> 68     15528.0057611 #> 69     17454.1798817 #> 70     20006.2582619 #> 71     10029.1136375 #> 72     10892.8779616 #> 73     11270.8487206 #> 74     11844.1591731 #> 75     13763.1476372 #> 76     12715.7735595 #> 77     14546.8345236 #> 78     17838.6435898 #> 79     8887.67517889 #> 80     8891.97355539 #> 81     12839.2250328 #> 82     5433.90934003 #> 83     6124.91189817 #> 84       6267.876827 #> 85     6677.35714099 #> 86     8073.26914058 #> 87     11585.4944924 #> 88     12057.2455603 #> 89      13058.816991 #> 90      19026.619806 #> 91     18924.7499077 #> 92      19525.005383 #> 93     19880.9175089 #> 94     21486.7688274 #> 95     13246.0911694 #> 96     14399.5534169 #> 97     10396.7058431 #> 98     12938.8411906 #> 99     17726.9196396 #> 100    15870.5572204 #> 101     20989.122938 #> 102    15125.2684793 #> 103    15389.8725133 #> 104    14793.2194916 #> 105    14808.0000677 #> 106    18347.8931324 #> 107    19583.0855759 #> 108    14729.3812838 #> 109    6772.63827193 #> 110    13344.6086556 #> 111    12092.1446817 #> 112     9641.3496983 #> 113    7320.38318899 #> 114    12444.8239212 #> 115    8982.07724987 #> 116    10769.2396836 #> 117    8257.25110283 #> 118     14762.170523 #> 119     24929.505597 #> 120    13100.4871773 #> 121    19498.8928168 #> 122    7406.49129826 #> 123    17122.0987313 #> 124    6939.68851959 #> 125    7564.94695587 #> 126    10026.1216248 #> 127    7873.39351219 #> 128    13530.4857972 #> 129    20684.2844081 #> 130    3786.82459871 #> 131    4527.88648086 #> 132    18932.7126208 #> 133     8241.4743325 #> 134    9033.46264257 #> 135    5684.90317016 #> 136    9288.41653484 #> 137    14064.8357297 #> 138    13639.3482775 #> 139    9878.97609863 #> 140    6900.36121897 #> 141    6948.21909422 #> 142    9589.88284429 #> 143    14957.8325229 #> 144    8207.03249122 #> 145    12218.7636229 #> 146     14966.125674 #> 147    10353.6917539 #> 148    10436.3448079 #> 149    16900.7317618 #> 150    11569.3278539 #> 151    13332.8551099 #> 152    11680.4722376 #> 153    12568.0861313 #> 154    13081.1159537 #> 155    12724.5995839 #> 156    17091.6681516 #> 157    15598.4603055 #> 158    14400.3070942 #> 159    15886.9084559 #> 160    10139.4205086 #> 161    7406.60614164 #> 162    6346.02608977 #> 163    7466.07885572"},{"path":"/reference/ssn_get_netgeometry.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract netgeometry column — ssn_get_netgeometry","title":"Extract netgeometry column — ssn_get_netgeometry","text":"Extract topological information netgeom column","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract netgeometry column — ssn_get_netgeometry","text":"","code":"ssn_get_netgeom(x, netvars = \"all\", reformat = FALSE)"},{"path":"/reference/ssn_get_netgeometry.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract netgeometry column — ssn_get_netgeometry","text":"x sf data.frame found SSN object netgeom column vector netvars Network coordinate variables return. Default \"\". edges, valid column names include: \"NetworkID\", \"SegmentID\", \"DistanceUpstream\". point datasets, valid column names include \"NetworkID\", \"SegmentID\", \"DistanceUpstream\", \"ratio\", \"pid\", \"locID\". reformat Convert network coordinate variables character numeric.","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract netgeometry column — ssn_get_netgeometry","text":"one column requested using netvars, function returns data.frame (default). one column requested, result vector.","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract netgeometry column — ssn_get_netgeometry","text":"SSN object generated using importSSN function, text column named \"netgeom\" added edges, observed sites, prediction sites (exist) data.frames. netgeom column contains data used describe edge site features relate one another topological space. edges, netgeom values contain \"ENETWORK\" prefix, 3 space delimited values parentheses: \"ENETWORK (NetworkID SegmentID DistanceUpstream)\". point datasets (observed prediction sites), values contain \"SNETWORK\" prefix, followed 6 space delimited values parentheses: \"SNETWORK (NetworkID SegmentID DistanceUpstream ratio pid locID)\". ssn_get_netgeom function extracts converts values text numeric, returning either data.frame (default) vector containing variables requested via netvars.","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract netgeometry column — ssn_get_netgeometry","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_get_netgeom(mf04p$obs) #>    NetworkID SegmentID DistanceUpstream           ratio pid locID #> 1          2        32    3194.82758834  0.321185040918   1     1 #> 2          2        33    3896.10674037 0.0690361937938   2     2 #> 3          2        33    4857.50889816  0.370693074276   3     3 #> 4          2        33    5886.68489225  0.693615191033   4     4 #> 5          2        33    6281.22686216   0.81740969149   5     5 #> 6          2        35    7300.18896771  0.472020290969   6     6 #> 7          2        36    8386.87883527  0.467609736278   7     7 #> 8          2        36    8626.98482356  0.595431065021   8     8 #> 9          2        38    10019.9727397  0.481795562545   9     9 #> 10         2        41    4049.94559903  0.241892607278  10    10 #> 11         2        47    8914.29845837  0.356571475119  11    11 #> 12         2        48     9295.3266467  0.223896901083  12    12 #> 13         2        51    12659.9947331  0.171145194729  13    13 #> 14         2        51    14103.2031154  0.799102017173  14    14 #> 15         2        52    16019.2025746  0.601882501114  15    15 #> 16         2        54    18447.5789988  0.132889204232  16    16 #> 17         2        57    3405.72135314  0.609185232313  17    17 #> 18         2        57    4046.04417336  0.973908806041  18    18 #> 19         2        59    5365.42224132  0.845110196768  19    19 #> 20         2        60    6489.59766227  0.885802604708  20    20 #> 21         2        60    5794.51481162  0.258397975972  21    21 #> 22         2        62    7547.78605505  0.128723349502  22    22 #> 23         2        63    10022.9334715   0.70355047499  23    23 #> 24         2        65     10879.708177  0.078434677104  24    24 #> 25         2        66    11985.9719107 0.0893958570497  25    25 #> 26         2        66    12357.0088971  0.242744463686  26    26 #> 27         2        67    15080.7113189  0.665890884643  27    27 #> 28         2        70    9921.48731634  0.881800162966  28    28 #> 29         2        94     10871.508857  0.196749884783  29    29 #> 30         2       109    11795.0008893 0.0160819902947  30    30 #> 31         1         0    16786.5620802   0.79067935202  31    31 #> 32         1         0    14295.1963452 0.0143033570212  32    32 #> 33         1         0    16258.1596546  0.626015066404  33    33 #> 34         1         4    909.868496941  0.231794110614  34    34 #> 35         1         4    1692.25764135  0.515501826192  35    35 #> 36         1         9    8046.69965981  0.280932180239  36    36 #> 37         1        12    10443.8838685  0.613552417712  37    37 #> 38         1        14    13865.3815622  0.907439930469  38    38 #> 39         1        16    14323.7279441  0.182376231574  39    39 #> 40         1        16     14580.785008  0.812239349337  40    40 #> 41         1        18    15652.4273972   0.54977020396  41    41 #> 42         1        20    18642.8233007  0.157200906378  42    42 #> 43         1        20    19566.2045371  0.639335640746  43    43 #> 44         2        48    9711.54632884  0.751115282016  44    44 #> 45         2        68    15672.8306471 0.0751878474962  45    45 ssn_get_netgeom(mf04p$edges, \"DistanceUpstream\") #>     DistanceUpstream #> 1      17458.2653443 #> 2      18357.2013474 #> 3      20793.1520844 #> 4      270.643033817 #> 5      3028.37256124 #> 6      4402.46334121 #> 7       5256.1104882 #> 8      6616.51936215 #> 9      7886.30445018 #> 10     8457.24370497 #> 11     9024.57776818 #> 12     9536.51353251 #> 13      11015.393429 #> 14      12325.976268 #> 15     14022.4029322 #> 16     14249.2973301 #> 17     14657.4130884 #> 18     14790.6182827 #> 19     16358.1988978 #> 20     18341.7531704 #> 21      20256.946519 #> 22     8056.91207735 #> 23     9607.96481233 #> 24     13105.9441562 #> 25     14112.2130889 #> 26     16613.9926597 #> 27     5611.09385126 #> 28     7959.14006425 #> 29     1556.81337221 #> 30     1920.27673835 #> 31     2336.21218485 #> 32     2967.11873356 #> 33     3676.08342921 #> 34     6863.15529791 #> 35     7113.95880148 #> 36     7508.49727746 #> 37     9386.94738161 #> 38     9805.94712673 #> 39     10250.1720836 #> 40     10289.8733396 #> 41     13963.1651297 #> 42      5221.6543089 #> 43     5793.50030353 #> 44     6280.45578932 #> 45     7277.66171417 #> 46     7449.52533783 #> 47     8801.09743647 #> 48     9118.56820965 #> 49     9908.03173594 #> 50     11074.7483485 #> 51     12266.6584864 #> 52     14564.9190207 #> 53     16981.1440334 #> 54     17918.0916672 #> 55     21902.5186538 #> 56      25336.584133 #> 57     26164.3010726 #> 58     4091.85088316 #> 59     4586.16095742 #> 60     5508.24341114 #> 61     6616.11354599 #> 62     7197.21170564 #> 63     9920.68301648 #> 64     10066.0179407 #> 65     10803.9630453 #> 66     11769.6727859 #> 67       14189.23821 #> 68     15528.0057611 #> 69     17454.1798817 #> 70     20006.2582619 #> 71     10029.1136375 #> 72     10892.8779616 #> 73     11270.8487206 #> 74     11844.1591731 #> 75     13763.1476372 #> 76     12715.7735595 #> 77     14546.8345236 #> 78     17838.6435898 #> 79     8887.67517889 #> 80     8891.97355539 #> 81     12839.2250328 #> 82     5433.90934003 #> 83     6124.91189817 #> 84       6267.876827 #> 85     6677.35714099 #> 86     8073.26914058 #> 87     11585.4944924 #> 88     12057.2455603 #> 89      13058.816991 #> 90      19026.619806 #> 91     18924.7499077 #> 92      19525.005383 #> 93     19880.9175089 #> 94     21486.7688274 #> 95     13246.0911694 #> 96     14399.5534169 #> 97     10396.7058431 #> 98     12938.8411906 #> 99     17726.9196396 #> 100    15870.5572204 #> 101     20989.122938 #> 102    15125.2684793 #> 103    15389.8725133 #> 104    14793.2194916 #> 105    14808.0000677 #> 106    18347.8931324 #> 107    19583.0855759 #> 108    14729.3812838 #> 109    6772.63827193 #> 110    13344.6086556 #> 111    12092.1446817 #> 112     9641.3496983 #> 113    7320.38318899 #> 114    12444.8239212 #> 115    8982.07724987 #> 116    10769.2396836 #> 117    8257.25110283 #> 118     14762.170523 #> 119     24929.505597 #> 120    13100.4871773 #> 121    19498.8928168 #> 122    7406.49129826 #> 123    17122.0987313 #> 124    6939.68851959 #> 125    7564.94695587 #> 126    10026.1216248 #> 127    7873.39351219 #> 128    13530.4857972 #> 129    20684.2844081 #> 130    3786.82459871 #> 131    4527.88648086 #> 132    18932.7126208 #> 133     8241.4743325 #> 134    9033.46264257 #> 135    5684.90317016 #> 136    9288.41653484 #> 137    14064.8357297 #> 138    13639.3482775 #> 139    9878.97609863 #> 140    6900.36121897 #> 141    6948.21909422 #> 142    9589.88284429 #> 143    14957.8325229 #> 144    8207.03249122 #> 145    12218.7636229 #> 146     14966.125674 #> 147    10353.6917539 #> 148    10436.3448079 #> 149    16900.7317618 #> 150    11569.3278539 #> 151    13332.8551099 #> 152    11680.4722376 #> 153    12568.0861313 #> 154    13081.1159537 #> 155    12724.5995839 #> 156    17091.6681516 #> 157    15598.4603055 #> 158    14400.3070942 #> 159    15886.9084559 #> 160    10139.4205086 #> 161    7406.60614164 #> 162    6346.02608977 #> 163    7466.07885572"},{"path":"/reference/ssn_get_stream_distmat.html","id":null,"dir":"Reference","previous_headings":"","what":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"Extracts stream network distance matrices observation prediction data SSN object.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"","code":"ssn_get_stream_distmat(x, name = \"obs\")"},{"path":"/reference/ssn_get_stream_distmat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"x SSN object name Internal name dataset object x. observed values, always \"obs\", default.  get stream network distance matrix prediction data set, name dataset must given, quotes.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"list asymmetric downstream stream distance matrices, network.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"internal name observed data objects class SSN \"obs\" default. another name specified, must represent prediction data set SSN object. SSN objects, names obtained using call names(x$preds). Note traditional symmetric distance matrices. First, distances SSN object represent stream distance, hydrologic distance, distance two locations movement restricted branching stream network. Another important difference distance matrices SSN objects contain downstream stream distance two locations, making asymmetric. asymmetry provides way store two types spatial relationships based stream distance: Flow-connected: Water flows upstream site downstream site. Flow-unconnected: Two sites reside stream network, share flow. example, two sites flow-connected downstream distance upstream site downstream site > 0, downstream distance downstream site upstream site = 0. flow-unconnected sites, downstream distance represents distance site closest downstream junction > 0 directions. Direction preserved, columns representing site rows representing site. Row column names correspond unique point identifier \"pid\" site. matrix, also possible get total stream distance (downstream + upstream) two sites network (see examples additional details). Stream distances calculated within network asymmetric matrices also stored network.  observation data, single square matrix distances returned network, names based netID value (e.g. \"dist.net1\", \"dist.net2\", etc.). However, two distance matrices (\"\" \"b\") required store downstream distance observed prediction sites. label \"\" represents downstream stream distance prediction sites observation sites, label \"b\" represents distance observation sites predictions sites.  Thus, list prediction matrices labeled \"dist.net1.\" downstream distance prediction sites columns, observation sites rows, first network. prediction matrix labeled \"dist.net1.b\" contains downstream distances observation sites columns prediction sites rows, first network. downstream distance matrices observations predictions rectangular, unless number observation prediction locations equal.  argument amongPreds = TRUE used function ssn_create_distmat, distance prediction sites also returned, using labelling convention among observation sites. , matrices network labeled \"dist.net1\", \"dist.net2\", etc., first second network, etc.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks. Journal American Statistical Association, 105(489), 22--24","code":""},{"path":[]},{"path":"/reference/ssn_get_stream_distmat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"","code":"## For this example only, copy MiddleFork04.ssn directory to R's ## temporary directory copy_lsn_to_temp() ## Create an SSN object with prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = \"pred1km\", overwrite = TRUE )  ## Create distance matrices for obs x obs, obs x preds, and preds x ## preds if (FALSE) { ssn_create_distmat(mf04p,   predpts = \"pred1km\", among_predpts = TRUE,   overwrite = TRUE ) }  ## Check names of prediction datasets names(mf04p$preds) #> [1] \"pred1km\"  ## Get list of stream distance matrices for observations dist_obs <- ssn_get_stream_distmat(mf04p) ## Display structure of list and names of the matrices str(dist_obs) #> List of 2 #>  $ dist.net1: num [1:13, 1:13] 0 2491 528 15877 15094 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:13] \"31\" \"32\" \"33\" \"34\" ... #>   .. ..$ : chr [1:13] \"31\" \"32\" \"33\" \"34\" ... #>  $ dist.net2: num [1:32, 1:32] 0 0 0 0 0 0 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:32] \"1\" \"2\" \"3\" \"4\" ... #>   .. ..$ : chr [1:32] \"1\" \"2\" \"3\" \"4\" ... names(dist_obs) #> [1] \"dist.net1\" \"dist.net2\" ## Look at first 5 rows and columns in asymmetric ## downstream only distance matrix for netID == 1 dist_obs$dist.net1[1:5, 1:5] #>            31       32        33 34       35 #> 31     0.0000     0.00     0.000  0   0.0000 #> 32  2491.3657     0.00  1962.963  0   0.0000 #> 33   528.4024     0.00     0.000  0   0.0000 #> 34 15876.6936 13385.33 15348.291  0 782.3891 #> 35 15094.3044 12602.94 14565.902  0   0.0000  ## Create symmetric total stream distance matrix between ## observations strdist_2 <- dist_obs$dist.net2 + t(dist_obs$dist.net2) strdist_2[5:10, 5:10] #>           5        6        7        8        9       10 #> 5     0.000 1018.962 2105.652 2345.758 3738.746 2979.006 #> 6  1018.962    0.000 1086.690 1326.796 2719.784 3997.968 #> 7  2105.652 1086.690    0.000  240.106 1633.094 5084.658 #> 8  2345.758 1326.796  240.106    0.000 1392.988 5324.764 #> 9  3738.746 2719.784 1633.094 1392.988    0.000 6717.751 #> 10 2979.006 3997.968 5084.658 5324.764 6717.751    0.000  ## Get maximum downstream only distance between ## observations on netID == 2 a.mat <- pmax(dist_obs$dist.net2, t(dist_obs$dist.net2)) a.mat[5:10, 5:10] #>           5        6        7        8        9       10 #> 5     0.000 1018.962 2105.652 2345.758 3738.746 2605.143 #> 6  1018.962    0.000 1086.690 1326.796 2719.784 3624.106 #> 7  2105.652 1086.690    0.000  240.106 1633.094 4710.795 #> 8  2345.758 1326.796  240.106    0.000 1392.988 4950.901 #> 9  3738.746 2719.784 1633.094 1392.988    0.000 6343.889 #> 10 2605.143 3624.106 4710.795 4950.901 6343.889    0.000  ## Get minimum downstream only distance between observations. If ## minimum distance == 0, sites are flow-connected b.mat <- pmin(dist_obs$dist.net2, t(dist_obs$dist.net2)) b.mat[5:10, 5:10] #>           5        6        7        8        9       10 #> 5    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 6    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 7    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 8    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 9    0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 10 373.8622 373.8622 373.8622 373.8622 373.8622   0.0000  ## Get distance matrices for pred1km dist_pred1km <- ssn_get_stream_distmat(mf04p, name = \"pred1km\") str(dist_pred1km) #> List of 6 #>  $ dist.net1  : num [1:57, 1:57] 0 1000 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>  $ dist.net1.a: num [1:13, 1:57] 0 0 0 1119 336 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:13] \"31\" \"32\" \"33\" \"34\" ... #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>  $ dist.net1.b: num [1:57, 1:13] 14758 15758 13384 11170 9900 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>   .. ..$ : chr [1:13] \"31\" \"32\" \"33\" \"34\" ... #>  $ dist.net2  : num [1:118, 1:118] 0 0 0 0 0 0 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>  $ dist.net2.a: num [1:32, 1:118] 0 0 0 0 0 0 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:32] \"1\" \"2\" \"3\" \"4\" ... #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>  $ dist.net2.b: num [1:118, 1:32] 2638 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>   .. ..$ : chr [1:32] \"1\" \"2\" \"3\" \"4\" ... names(dist_pred1km) #> [1] \"dist.net1\"   \"dist.net1.a\" \"dist.net1.b\" \"dist.net2\"   \"dist.net2.a\" #> [6] \"dist.net2.b\" ## Look at first 5 rows and columns of downstream only distances ## FROM prediction sites TO observed sites on netID == 1 dist_pred1km$dist.net1.a[1:5, 1:5] #>           46       47       48       49       50 #> 31    0.0000   0.0000    0.000    0.000    0.000 #> 32    0.0000   0.0000    0.000    0.000    0.000 #> 33    0.0000   0.0000    0.000    0.000    0.000 #> 34 1118.5041 118.5041 2492.595 4706.651 5976.436 #> 35  336.1149   0.0000 1710.206 3924.262 5194.047  ## Look at downstream only stream distances among prediction ## sites in pred1km on netID == 1. This is useful for block ## prediction dist_pred1km$dist.net1[1:5, 1:5] #>      46 47       48       49       50 #> 46    0  0 1374.091 3588.147 4857.932 #> 47 1000  0 2374.091 4588.147 5857.932 #> 48    0  0    0.000 2214.056 3483.841 #> 49    0  0    0.000    0.000 1269.785 #> 50    0  0    0.000    0.000    0.000"},{"path":"/reference/ssn_glm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"function works spatial stream network objects fit generalized linear models spatially autocorrelated errors using likelihood methods, allowing non-spatial random effects, anisotropy, partition factors, big data methods, . spatial formulation described Ver Hoef Peterson (2010) Peterson Ver Hoef (2010).","code":""},{"path":"/reference/ssn_glm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"","code":"ssn_glm(   formula,   ssn.object,   family,   tailup_type = \"none\",   taildown_type = \"none\",   euclid_type = \"none\",   nugget_type = \"nugget\",   tailup_initial,   taildown_initial,   euclid_initial,   nugget_initial,   dispersion_initial,   additive,   estmethod = \"reml\",   anisotropy = FALSE,   random,   randcov_initial,   partition_factor,   ... )"},{"path":"/reference/ssn_glm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"formula two-sided linear formula describing fixed effect structure model, response left ~ operator terms right, separated + operators. ssn.object spatial stream network object class SSN. family generalized linear model family use ssn_glm(). Available options include \"Gaussian\", \"poisson\", \"nbinomial\" (negative binomial), \"binomial\", \"beta\", \"Gamma\", \"invgauss\". family \"Gaussian\", arguments passed evaluated ssn_lm(). Can quoted unquoted. Note family argument takes single value, rather list structure used stats::glm. See Details . tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". Parameterizations described Details. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". Parameterizations described Details. tailup_initial object tailup_initial() specifying initial /known values tailup covariance parameters. taildown_initial object taildown_initial() specifying initial /known values taildown covariance parameters. euclid_initial object euclid_initial() specifying initial /known values euclidean covariance parameters. nugget_initial object nugget_initial() specifying initial /known values nugget covariance parameters. dispersion_initial object dispersion_initial() specifying initial /known values tailup covariance parameters. additive name variable ssn.object used define spatial weights. Can quoted unquoted. tailup covariance functions, additive weights used branching. Technical details describe role additive variable tailup covariance function available Ver Hoef Peterson (2010). estmethod estimation method. Available options include \"reml\" restricted maximum likelihood \"ml\" maximum likelihood. default \"reml\". anisotropy logical indicating whether (geometric) anisotropy modeled. required spcov_initial provided 1) rotate assumed unknown assumed known non-zero 2) scale assumed unknown assumed known less one. anisotropy TRUE, computational times can significantly increase. default FALSE. random one-sided linear formula describing random effect structure model. Terms specified right ~ operator. term structure x1 + ... + xn | g1/.../gm, x1 + ... + xn specifies model random effects g1/.../gm grouping structure. Separate terms separated + must generally wrapped parentheses. Random intercepts added model implicitly least  one variable defined. random intercept desired, must explicitly defined (e.g., x1 + ... + xn - 1 | g1/.../gm). random intercept desired grouping structure, random intercept must specified 1 | g1/.../gm. Note g1/.../gm shorthand (1 | g1/.../gm). random intercepts desired shorthand notation used, parentheses can omitted. randcov_initial optional object specifying initial /known values random effect variances. See spmodel::randcov_initial(). partition_factor one-sided linear formula single term specifying partition factor.  partition factor assumes observations different levels partition factor uncorrelated. ... arguments stats::optim().","code":""},{"path":"/reference/ssn_glm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"list many elements store information fitted model object class ssn_glm. Many generic functions summarize model fit available ssn_glm objects, including AIC, AICc, anova, augment, coef, cooks.distance, covmatrix, deviance, fitted, formula, glance, glances, hatvalues, influence, labels, logLik, loocv, model.frame, model.matrix, plot, predict, print, pseudoR2, summary, terms, tidy, update, varcomp, vcov. fitted model list contains following elements: additive: name additive function value column. anisotropy: Whether euclidean anisotropy modeled. call: function call. coefficients: Model coefficients. contrasts: user-supplied contrasts. cooks_distance: Cook's distance values. crs: geographic coordinate reference system. deviance: model deviance. diagtol: tolerance value may added diagonal  ovariance matrices encourage decomposition stability. estmethod: estimation method. euclid_max: maximum euclidean distance. family: generalized linear model family fitted: Fitted values. formula: model formula. hatvalues: hat (leverage) values. is_known: object identifies parameters known. local_index: index identifier used internally sorting. missing_index: rows \"obs\" object missing responses. n: sample size. npar: number estimated covariance parameters. observed_index: rows \"obs\" object observed responses. optim: optimization output. p: number fixed effects. partition_factor: partition factor formula. pseudoR2: pseudo R-squared. random: random effect formula. residuals: residuals. sf_column_name: name geometry columns ssn.object size: size binomial trials relevant. ssn.object: updated ssn.object. tail_max: maximum stream distance. terms: model terms. vcov: Variance-covariance matrices xlevels: levels factors model matrix. y: response. list elements meant used various generic functions (e.g., residuals() operate model object. possible access elements fitted model list directly, strongly advise generic available return element interest. example, strongly recommend using residuals() obtain model residuals instead accessing fitted model list directly via object$residuals.","code":""},{"path":"/reference/ssn_glm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"generalized linear model spatial stream networks can written \\(g(\\mu) = \\eta = X \\beta + zu + zd + ze + n\\), \\(\\mu\\) expectation response given random errors, \\(y\\), \\(g()\\) function links mean \\(\\eta\\) (called link function), X fixed effects design matrix, \\(\\beta\\) fixed effects, \\(zu\\) tailup random error, \\(zd\\) taildown random error, \\(ze\\) Euclidean random error, \\(n\\) nugget random error. six generalized linear model families available: poisson assumes \\(y\\) Poisson random variable nbinomial assumes \\(y\\) negative binomial random variable, binomial assumes \\(y\\) binomial random variable, beta assumes \\(y\\) beta random variable, Gamma assumes \\(y\\) gamma random variable, inverse.gaussian assumes \\(y\\) inverse Gaussian random variable. supports \\(y\\) family given : family: support \\(y\\) Gaussian: \\(-\\infty < y < \\infty\\) poisson: \\(0 \\le y\\); \\(y\\) integer nbinomial: \\(0 \\le y\\); \\(y\\) integer binomial: \\(0 \\le y\\); \\(y\\) integer beta: \\(0 < y < 1\\) Gamma: \\(0 < y\\) inverse.gaussian: \\(0 < y\\) generalized linear model families parameterizations link functions given : family: link function Gaussian: \\(g(\\mu) = \\eta\\) (identity link) poisson: \\(g(\\mu) = log(\\eta)\\) (log link) nbinomial: \\(g(\\mu) = log(\\eta)\\) (log link) binomial: \\(g(\\mu) = log(\\eta / (1 - \\eta))\\) (logit link) beta: \\(g(\\mu) = log(\\eta / (1 - \\eta))\\) (logit link) Gamma: \\(g(\\mu) = log(\\eta)\\) (log link) inverse.gaussian: \\(g(\\mu) = log(\\eta)\\) (log link) variance function individual \\(y\\) (given \\(\\mu\\)) generalized linear model family given : family: \\(Var(y)\\) Gaussian: \\(\\sigma^2\\) poisson: \\(\\mu \\phi\\) nbinomial: \\(\\mu + \\mu^2 / \\phi\\) binomial: \\(n \\mu (1 - \\mu) \\phi\\) beta: \\(\\mu (1 - \\mu) / (1 + \\phi)\\) Gamma: \\(\\mu^2 / \\phi\\) inverse.gaussian: \\(\\mu^2 / \\phi\\) parameter \\(\\phi\\) dispersion parameter influences \\(Var(y)\\). poisson binomial families, \\(\\phi\\) always one. Note inverse Gaussian parameterization different standard inverse Gaussian parameterization, variance \\(\\mu^3 / \\lambda\\). Setting \\(\\phi = \\lambda / \\mu\\) yields parameterization, preferred computational stability. Also note dispersion parameter often defined literature \\(V(\\mu) \\phi\\), \\(V(\\mu)\\) variance function mean. use parameterization, important recognize interpreting dispersion estimates. generalized linear model constructions, see McCullagh Nelder (1989). generalized linear model context, tailup, taildown, Euclidean, nugget covariance affect modeled mean observation (conditional effects). link scale, tailup random errors capture spatial covariance moving downstream (depend downstream distance), taildown random errors capture spatial covariance moving upstream (depend upstream) distance, Euclidean random errors capture spatial covariance depends Euclidean distance, nugget random errors captures variability independent spatial locations. \\(\\eta\\) modeled using spatial covariance function expressed \\(de(zu) * R(zu) + de(zd) * R(zd) + de(ze) * R(ze) + nugget * \\). \\(de(zu)\\), \\(de(zu)\\), \\(de(zd)\\) represent tailup, taildown, Euclidean variances, respectively. \\(R(zu)\\), \\(R(zd)\\), \\(R(ze)\\) represent tailup, taildown, Euclidean correlation matrices, respectively. correlation matrix depends range parameter controls distance-decay behavior correlation. \\(nugget\\) represents nugget variance \\(\\) represents identity matrix. tailup_type Details: Let \\(D\\) matrix hydrologic distances, \\(W\\) diagonal matrix weights additive, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zu)\\) given : linear: \\((1 - r) * (r <= 1) * W\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1) * W\\) exponential: \\(exp(-r) * W\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0) * W\\) epa: \\((D - range)^2 * F * (r <= 1) * W / 16range^5\\) none: \\(\\) * W Details describing F matrix epa covariance given Garreta et al. (2010). Flow-unconnected elements \\(R(zu)\\) assumed uncorrelated. Observations different networks also assumed uncorrelated. taildown_type Details: Let \\(D\\) matrix hydrologic distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zd)\\) given : linear: \\((1 - r) * (r <= 1)\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) exponential: \\(exp(-r)\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0)\\) epa: \\((D - range)^2 * F1 * (r <= 1) / 16range^5\\) none: \\(\\) Now let \\(\\) matrix contains shorter two distances two sites common downstream junction, \\(r1 = / range\\), \\(B\\) matrix contains longer two distances two sites common downstream junction, \\(r2 = B / range\\),  \\(\\) identity matrix. parametric forms flow-unconnected elements \\(R(zd)\\) given : linear: \\((1 - r2) * (r2 <= 1)\\) spherical: \\((1 - 1.5r1 + 0.5r2) * (1 - r2)^2 * (r2 <= 1)\\) exponential: \\(exp(-(r1 + r2))\\) mariah: \\((log(90r1 + 1) - log(90r2 + 1)) / (90r1 - 90r2) * (=/ B) + (1 / (90r1 + 1)) * (= B)\\) epa: \\((B - range)^2 * F2 * (r2 <= 1) / 16range^5\\) none: \\(\\) Details describing F1 F2 matrices epa covariance given Garreta et al. (2010). Observations different networks assumed uncorrelated. euclid_type Details: Let \\(D\\) matrix Euclidean distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms elements \\(R(ze)\\) given : exponential: \\(exp(- r )\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) gaussian: \\(exp(- r^2 )\\) cubic: \\((1 - 7r^2 + 8.75r^3 - 3.5r^5 + 0.75r^7) * (r <= 1)\\) pentaspherical: \\((1 - 1.875r + 1.25r^3 - 0.375r^5) * (r <= 1)\\) cosine: \\(cos(r)\\) wave: \\(sin(r) * (h > 0) / r + (h = 0)\\) jbessel: \\(Bj(h * range)\\), Bj Bessel-J function gravity: \\((1 + r^2)^{-0.5}\\) rquad: \\((1 + r^2)^{-1}\\) magnetic: \\((1 + r^2)^{-1.5}\\) none: \\(\\) nugget_type Details: Let \\(\\) identity matrix \\(0\\) zero matrix. parametric forms elements nugget variance given : nugget: \\(\\) none: \\(0\\) short, nugget effect modeled nugget_type \"nugget\" omitted nugget_type \"none\". estmethod Details: various estimation methods reml: Maximize restricted log-likelihood. ml: Maximize log-likelihood. anisotropy Details: default, Euclidean covariance parameters except rotate scale assumed unknown, requiring estimation. either rotate scale given initial values 0 1 (respectively) assumed unknown euclid_initial(), anisotropy implicitly set TRUE. (Geometric) Anisotropy modeled transforming Euclidean covariance function decays differently different directions one decays equally directions via rotation scaling original Euclidean coordinates. rotation controlled rotate parameter \\([0, \\pi]\\) radians. scaling controlled scale parameter \\([0, 1]\\). anisotropy correction involves first rotation coordinates clockwise rotate scaling coordinates' minor axis reciprocal scale. Euclidean covariance computed using transformed coordinates. random Details: random effects used (estimation method must \"reml\" \"ml\"), model can written \\(g(\\mu) = \\eta = X \\beta + W1\\gamma 1 + ... Wj\\gamma j + zu + zd + ze + n\\), Z random effects design matrix u random effect. partition_factor Details: partition factor can represented matrix form \\(P\\), elements \\(P\\) equal one observations level partition factor zero otherwise. covariance matrix involving spatial random effects components multiplied element-wise (Hadmard product) \\(P\\), yielding final covariance matrix. Details: Observations NA response values removed model fitting, values can predicted afterwards running predict(object).","code":""},{"path":"/reference/ssn_glm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"function perform internal scaling. optimization stable due large extremely large variances, scale relevant variables variance 1 optimization.","code":""},{"path":"/reference/ssn_glm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"Garreta, V., Monestiez, P. Ver Hoef, J.M. (2010) Spatial modelling prediction river networks: model, model, hybrid? Environmetrics 21(5), 439--456. McCullagh P. Nelder, J. . (1989) Generalized Linear Models. London: Chapman Hall. Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644--651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6--18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.","code":""},{"path":"/reference/ssn_glm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_gmod <- ssn_glm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   family = \"Gamma\",   tailup_type = \"exponential\",   additive = \"afvArea\" ) summary(ssn_gmod) #>  #> Call: #> ssn_glm(formula = Summer_mn ~ ELEV_DEM, ssn.object = mf04p, family = \"Gamma\",  #>     tailup_type = \"exponential\", additive = \"afvArea\") #>  #> Deviance Residuals: #>        Min         1Q     Median         3Q        Max  #> -0.0125918 -0.0028720 -0.0006556  0.0027528  0.0157742  #>  #> Coefficients (fixed): #>               Estimate Std. Error z value Pr(>|z|)     #> (Intercept)  7.4233120  0.5207267  14.256   <2e-16 *** #> ELEV_DEM    -0.0024085  0.0002589  -9.301   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.6477 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  3.265e-02 #>   tailup exponential         range  1.006e+06 #>               nugget        nugget  2.110e-05 #>           dispersion    dispersion  8.114e+03 #>"},{"path":"/reference/ssn_import.html","id":null,"dir":"Reference","previous_headings":"","what":"Import SSN object — ssn_import","title":"Import SSN object — ssn_import","text":"function reads spatial data .ssn folder creates SSN object.","code":""},{"path":"/reference/ssn_import.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import SSN object — ssn_import","text":"","code":"ssn_import(   path,   include_obs = TRUE,   predpts,   format_additive = FALSE,   names_additive = NULL,   overwrite = FALSE )"},{"path":"/reference/ssn_import.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import SSN object — ssn_import","text":"path Filepath .ssn directory. See details. include_obs default = TRUE. Logical indicating whether observed sites included SSN object. predpts Vector shapefile basenames prediction sites found within .ssn folder. format_additive Logical indicating whether columns containing addtive function values formated SSN2. Default = FALSE. names_additive Character vector column names observed prediction site datasets containing additive function values. Must defined format_additive = TRUE. Default = NULL. overwrite default = FALSE. TRUE, overwrite existing binaryID.db files.","code":""},{"path":"/reference/ssn_import.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import SSN object — ssn_import","text":"ssn_import returns object class SSN, list four elements containing: edges: sf data.frame containing stream network, additional 'netgeom' column. obs: sf data.frame containing observed site locations, additional 'netgeom' column. NA include_obs =     FALSE. preds: list sf data.frames containing prediction site locations. names preds list correspond basenames prediction site shapefiles (without .shp extension) specified predpts. Empty list predpts provided. path: local file .ssn directory associated SSN object.","code":""},{"path":"/reference/ssn_import.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import SSN object — ssn_import","text":"importSSN function imports spatial data .ssn folder create SSN object. information contained .ssn folder can generated using number proprietary open source software tools: Spatial Tools Analysis River Systems (STARS) tools ArcGIS Desktop versions 9.3x-10.8x (Peterson Ver Hoef 2014). custom ArcGIS toolset designed work existing streams data vector format. openSTARS package (Kattwinkel et al. 2020) extends functionality STARS toolset, makes use R GRASS GIS. open source designed derive streams raster format digital elevation model (DEM). SSNbler package (currently development September 2023) open source version STARS toolset, makes use functionality found sf package process streams data vector format. spatial data processed using one software tools, .ssn directory output contains spatial, topological attribute data needed fit spatial statistical stream network model streams data.  includes: edges shapefile lines represent stream network. sites shapefile points observed data collected stream network. Prediction sites shapefile(s) locations predictions made. netID.dat files distinct network, store topological relationships line segments edges. detailed description .ssn directory contents provided Peterson Ver Hoef (2014). ssn_import imports edges, observed sites, prediction sites sf data.frame objects. new column named 'netgeom' created store important data represents topological relationships spatial stream network model. data stored character format, less likely inadvertantly changed users. See ssn_get_netgeom detailed description format contents 'netgeom'. information contained netID text files imported SQLite database, binaryID.db, stored .ssn directory. information used internally ssn_create_distmat, ssn_lm ssn_glm calculate data necessary fit spatial statistical model stream network data. overwrite = TRUE (overwrite = FALSE default) binaryID.db file already exists within .ssn directory, overwriten SSN object created. minimum, SSN object must always contain streams, referred edges. SSN object also typically contain set observed sites, measurements collected one observed dataset permitted. include_obs=FALSE, SSN object created without observations. option provides flexibility users like simulate data set artifical sites existing stream network. Note observation sites must included SSN object order fit models using ssn_lm ssn_glm. SSN object may contain multiple sets prediction points (none), stored separate shapefiles .ssn directory. ssn_import_predpts function allows users import additional sets prediction sites existing SSN object.","code":""},{"path":"/reference/ssn_import.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Import SSN object — ssn_import","text":"Kattwinkel, M., Szocs, E., Peterson, E., Schafer, R.B. (2020) Preparing GIS data analysis stream monitoring data: R package openSTARS. PLOS One 15(9), e0239237. Peterson, E., Ver Hoef, J.M. (2014) STARS: ArcGIS toolset used calculate spatial information needed fit spatial statistical stream network models stream network data. Journal Statistical Software 56(2), 1--17.","code":""},{"path":[]},{"path":"/reference/ssn_import.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import SSN object — ssn_import","text":"","code":"## Create local temporary copy of MiddleFork04.ssn found in # SSN2/lsndata folder. Only necessary for this example. copy_lsn_to_temp()  ## Import SSN object with no prediction sites mf04 <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   overwrite = TRUE )  ## Import SSN object with 3 sets of prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(     \"pred1km.shp\",     \"CapeHorn.shp\",     \"Knapp.shp\"   ),   overwrite = TRUE )"},{"path":"/reference/ssn_import_predpts.html","id":null,"dir":"Reference","previous_headings":"","what":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"shapefile prediction points found .ssn directory imported existing object class SSN, ssn_lm, ssn_glm.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"","code":"ssn_import_predpts(x, predpts, format_additive = FALSE, names_additive = NULL)"},{"path":"/reference/ssn_import_predpts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"x object classSSN, ssn_lm, ssn_glm. predpts Name prediction point shapefile import character format, without .shp extension. format_additive Logical indicating whether columns containing addtive function values formated SSN2. Default = FALSE. names_additive Character vector column names observed prediction site datasets containing additive function values. Must defined format_additive = TRUE. Default = NULL.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"object class SSN, ssn_lm, ssn_glm contains new prediction dataset. name prediction dataset preds list corresponds basenames prediction site shapefiles (without .shp extension) specified predpts. See ssn_import detailed description prediction dataset format within SSN class object.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"ssn_import_predpts imports shapefile prediction points residing .ssn directory existing SSN, ssn_lm, ssn_glm object. prediction dataset must reside ssn.object$path directory. path SSN object can updated using ssn_update_path() prior importing prediction datasets. Note , prediction dataset must contain spatial, topological attribute information needed make predictions using ssn_lm ssn_glm object.  information can generated using number proprietary open source software tools: Spatial Tools Analysis River Systems (STARS) tools ArcGIS Desktop versions 9.3x-10.8x (Peterson Ver Hoef 2014). custom ArcGIS toolset designed work existing streams data vector format. openSTARS package (Kattwinkel et al. 2020) extends functionality STARS toolset, makes use R GRASS GIS. open source designed derive streams raster format digital elevation model (DEM). SSNbler package (currently development September 2023) open source version STARS toolset, makes use functionality found sf package process streams data vector format.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"Kattwinkel, M., Szocs, E., Peterson, E., Schafer, R.B. (2020) Preparing GIS data analysis stream monitoring data: R package openSTARS. PLOS One 15(9), e0239237. Peterson, E., Ver Hoef, J.M. (2014) STARS: ArcGIS toolset used calculate spatial information needed fit spatial statistical stream network models stream network data. Journal Statistical Software 56(2), 1--17.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"","code":"## Create local temporary copy of MiddleFork04.ssn found in # SSN2/lsndata folder. Only necessary for this example. copy_lsn_to_temp()  ## Import SSN object with no prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   overwrite = TRUE )  ## Import pred1km prediction dataset into SSN object mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\")) #> binaryID.db already exists - no changes were made to binaryID.db table mf04p <- ssn_import_predpts(mf04p, predpts = \"pred1km\") names(mf04p$preds) #> [1] \"pred1km\"  ## Import pred1km prediction dataset into a ssn_glm object ssn_gmod <- ssn_glm(Summer_mn ~ netID, mf04p,   family = \"Gamma\",   tailup_type = \"exponential\", additive = \"afvArea\" ) ssn_gmod <- ssn_import_predpts(ssn_gmod, predpts = \"CapeHorn\") names(ssn_gmod$ssn.object$preds) #> [1] \"pred1km\"  \"CapeHorn\""},{"path":"/reference/ssn_initial.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a covariance parameter initial object — ssn_initial","title":"Create a covariance parameter initial object — ssn_initial","text":"Create covariance parameter initial object specifies initial /known values use estimating specific covariance parameters ssn_lm() ssn_glm().  See spmodel::randcov_initial() documentation regarding random effect covariance parameter initial objects.","code":""},{"path":"/reference/ssn_initial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a covariance parameter initial object — ssn_initial","text":"","code":"tailup_initial(tailup_type, de, range, known)  taildown_initial(taildown_type, de, range, known)  euclid_initial(euclid_type, de, range, rotate, scale, known)  nugget_initial(nugget_type, nugget, known)"},{"path":"/reference/ssn_initial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a covariance parameter initial object — ssn_initial","text":"tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". de spatially dependent (correlated) random error variance. Commonly referred partial sill. range correlation parameter. known character vector indicating covariance parameters assumed known. value \"given\" shorthand assuming covariance parameters given *_initial() assumed known. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". rotate Anisotropy rotation parameter (0 \\(\\pi\\) radians) euclidean portion covariance. value 0 (default) implies rotation. scale Anisotropy scale parameter (0 1) euclidean portion covariance. value 1 (default) implies scaling. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". nugget spatially independent (correlated) random error variance. Commonly referred nugget.","code":""},{"path":"/reference/ssn_initial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a covariance parameter initial object — ssn_initial","text":"list two elements: initial is_known. initial named numeric vector indicating spatial covariance parameters specified initial /known values. is_known named numeric vector indicating whether spatial covariance parameters initial known . class list matches relevant spatial covariance type.","code":""},{"path":"/reference/ssn_initial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a covariance parameter initial object — ssn_initial","text":"Create initial object use ssn_lm() ssn_glm(). NA values can given ie, rotate, scale, lets functions find initial values parameters sometimes otherwise assumed known (e.g., rotate scale ssn_lm() ssn_glm(). Parametric forms spatial covariance type presented . tailup_type Details: Let \\(D\\) matrix hydrologic distances, \\(W\\) diagonal matrix weights additive, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zu)\\) given : linear: \\((1 - r) * (r <= 1) * W\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1) * W\\) exponential: \\(exp(-r) * W\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0) * W\\) epa: \\((D - range)^2 * F * (r <= 1) * W / 16range^5\\) none: \\(\\) * W Details describing F matrix epa covariance given Garreta et al. (2010). Flow-unconnected elements \\(R(zu)\\) assumed uncorrelated. Observations different networks also assumed uncorrelated. taildown_type Details: Let \\(D\\) matrix hydrologic distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zd)\\) given : linear: \\((1 - r) * (r <= 1)\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) exponential: \\(exp(-r)\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0)\\) epa: \\((D - range)^2 * F1 * (r <= 1) / 16range^5\\) none: \\(\\) Now let \\(\\) matrix contains shorter two distances two sites common downstream junction, \\(r1 = / range\\), \\(B\\) matrix contains longer two distances two sites common downstream junction, \\(r2 = B / range\\),  \\(\\) identity matrix. parametric forms flow-unconnected elements \\(R(zd)\\) given : linear: \\((1 - r2) * (r2 <= 1)\\) spherical: \\((1 - 1.5r1 + 0.5r2) * (1 - r2)^2 * (r2 <= 1)\\) exponential: \\(exp(-(r1 + r2))\\) mariah: \\((log(90r1 + 1) - log(90r2 + 1)) / (90r1 - 90r2) * (=/ B) + (1 / (90r1 + 1)) * (= B)\\) epa: \\((B - range)^2 * F2 * (r2 <= 1) / 16range^5\\) none: \\(\\) Details describing F1 F2 matrices epa covariance given Garreta et al. (2010). Observations different networks assumed uncorrelated. euclid_type Details: Let \\(D\\) matrix Euclidean distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms elements \\(R(ze)\\) given : exponential: \\(exp(- r )\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) gaussian: \\(exp(- r^2 )\\) cubic: \\((1 - 7r^2 + 8.75r^3 - 3.5r^5 + 0.75r^7) * (r <= 1)\\) pentaspherical: \\((1 - 1.875r + 1.25r^3 - 0.375r^5) * (r <= 1)\\) cosine: \\(cos(r)\\) wave: \\(sin(r) * (h > 0) / r + (h = 0)\\) jbessel: \\(Bj(h * range)\\), Bj Bessel-J function gravity: \\((1 + r^2)^{-0.5}\\) rquad: \\((1 + r^2)^{-1}\\) magnetic: \\((1 + r^2)^{-1.5}\\) none: \\(\\) nugget_type Details: Let \\(\\) identity matrix \\(0\\) zero matrix. parametric forms elements nugget variance given : nugget: \\(\\) none: \\(0\\) short, nugget effect modeled nugget_type \"nugget\" omitted nugget_type \"none\". Dispersion random effect initial objects specified via spmodel::dispersion_initial() spmodel::randcov_initial(), respectively.","code":""},{"path":"/reference/ssn_initial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create a covariance parameter initial object — ssn_initial","text":"Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644--651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6--18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.","code":""},{"path":"/reference/ssn_initial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a covariance parameter initial object — ssn_initial","text":"","code":"tailup_initial(\"exponential\", de = 1, range = 20, known = \"range\") #> $initial #>    de range  #>     1    20  #>  #> $is_known #>    de range  #> FALSE  TRUE  #>  #> attr(,\"class\") #> [1] \"tailup_exponential\" tailup_initial(\"exponential\", de = 1, range = 20, known = \"given\") #> $initial #>    de range  #>     1    20  #>  #> $is_known #>    de range  #>  TRUE  TRUE  #>  #> attr(,\"class\") #> [1] \"tailup_exponential\" euclid_initial(\"spherical\", de = 2, range = 4, scale = 0.8, known = c(\"range\", \"scale\")) #> $initial #>    de range scale  #>   2.0   4.0   0.8  #>  #> $is_known #>    de range scale  #> FALSE  TRUE  TRUE  #>  #> attr(,\"class\") #> [1] \"euclid_spherical\" dispersion_initial(\"nbinomial\", dispersion = 5) #> $initial #> dispersion  #>          5  #>  #> $is_known #> dispersion  #>      FALSE  #>  #> attr(,\"class\") #> [1] \"nbinomial\""},{"path":"/reference/ssn_lm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"function works spatial stream network objects fit linear models spatially autocorrelated errors using likelihood methods, allowing non-spatial random effects, anisotropy, partition factors, big data methods, . spatial formulation described Ver Hoef Peterson (2010) Peterson Ver Hoef (2010).","code":""},{"path":"/reference/ssn_lm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"","code":"ssn_lm(   formula,   ssn.object,   tailup_type = \"none\",   taildown_type = \"none\",   euclid_type = \"none\",   nugget_type = \"nugget\",   tailup_initial,   taildown_initial,   euclid_initial,   nugget_initial,   additive,   estmethod = \"reml\",   anisotropy = FALSE,   random,   randcov_initial,   partition_factor,   ... )"},{"path":"/reference/ssn_lm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"formula two-sided linear formula describing fixed effect structure model, response left ~ operator terms right, separated + operators. ssn.object spatial stream network object class SSN. tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". Parameterizations described Details. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". Parameterizations described Details. tailup_initial object tailup_initial() specifying initial /known values tailup covariance parameters. taildown_initial object taildown_initial() specifying initial /known values taildown covariance parameters. euclid_initial object euclid_initial() specifying initial /known values euclidean covariance parameters. nugget_initial object nugget_initial() specifying initial /known values nugget covariance parameters. additive name variable ssn.object used define spatial weights. Can quoted unquoted. tailup covariance functions, additive weights used branching. Technical details describe role additive variable tailup covariance function available Ver Hoef Peterson (2010). estmethod estimation method. Available options include \"reml\" restricted maximum likelihood \"ml\" maximum likelihood. default \"reml\". anisotropy logical indicating whether (geometric) anisotropy modeled. required spcov_initial provided 1) rotate assumed unknown assumed known non-zero 2) scale assumed unknown assumed known less one. anisotropy TRUE, computational times can significantly increase. default FALSE. random one-sided linear formula describing random effect structure model. Terms specified right ~ operator. term structure x1 + ... + xn | g1/.../gm, x1 + ... + xn specifies model random effects g1/.../gm grouping structure. Separate terms separated + must generally wrapped parentheses. Random intercepts added model implicitly least  one variable defined. random intercept desired, must explicitly defined (e.g., x1 + ... + xn - 1 | g1/.../gm). random intercept desired grouping structure, random intercept must specified 1 | g1/.../gm. Note g1/.../gm shorthand (1 | g1/.../gm). random intercepts desired shorthand notation used, parentheses can omitted. randcov_initial optional object specifying initial /known values random effect variances. See spmodel::randcov_initial(). partition_factor one-sided linear formula single term specifying partition factor.  partition factor assumes observations different levels partition factor uncorrelated. ... arguments stats::optim().","code":""},{"path":"/reference/ssn_lm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"list many elements store information fitted model object class ssn_lm. Many generic functions summarize model fit available ssn_lm objects, including AIC, AICc, anova, augment, coef, cooks.distance, covmatrix, deviance, fitted, formula, glance, glances, hatvalues, influence, labels, logLik, loocv, model.frame, model.matrix, plot, predict, print, pseudoR2, summary, terms, tidy, update, varcomp, vcov. fitted model list contains following elements: additive: name additive function value column. anisotropy: Whether euclidean anisotropy modeled. call: function call. coefficients: Model coefficients. contrasts: user-supplied contrasts. cooks_distance: Cook's distance values. crs: geographic coordinate reference system. deviance: model deviance. diagtol: tolerance value may added diagonal  ovariance matrices encourage decomposition stability. estmethod: estimation method. euclid_max: maximum euclidean distance. fitted: Fitted values. formula: model formula. hatvalues: hat (leverage) values. is_known: object identifies parameters known. local_index: index identifier used internally sorting. missing_index: rows \"obs\" object missing responses. n: sample size. npar: number estimated covariance parameters. observed_index: rows \"obs\" object observed responses. optim: optimization output. p: number fixed effects. partition_factor: partition factor formula. pseudoR2: pseudo R-squared. random: random effect formula. residuals: residuals. sf_column_name: name geometry columns ssn.object ssn.object: updated ssn.object. tail_max: maximum stream distance. terms: model terms. vcov: Variance-covariance matrices xlevels: levels factors model matrix. list elements meant used various generic functions (e.g., residuals() operate model object. possible access elements fitted model list directly, strongly advise generic available return element interest. example, strongly recommend using residuals() obtain model residuals instead accessing fitted model list directly via object$residuals.","code":""},{"path":"/reference/ssn_lm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"linear model spatial stream networks can written \\(y = X \\beta + zu + zd + ze + n\\), \\(X\\) fixed effects design matrix, \\(\\beta\\) fixed effects, \\(zu\\) tailup random error, \\(zd\\) taildown random error, \\(ze\\) Euclidean random error, \\(n\\) nugget random error. tailup random errors capture spatial covariance moving downstream (depend downstream distance), taildown random errors capture spatial covariance moving upstream (depend upstream) distance, Euclidean random errors capture spatial covariance depends Euclidean distance, nugget random errors captures variability independent spatial locations. response \\(y\\) modeled using spatial covariance function expressed \\(de(zu) * R(zu) + de(zd) * R(zd) + de(ze) * R(ze) + nugget * \\). \\(de(zu)\\), \\(de(zu)\\), \\(de(zd)\\) represent tailup, taildown, Euclidean variances, respectively. \\(R(zu)\\), \\(R(zd)\\), \\(R(ze)\\) represent tailup, taildown, Euclidean correlation matrices, respectively. correlation matrix depends range parameter controls distance-decay behavior correlation. \\(nugget\\) represents nugget variance \\(\\) represents identity matrix. tailup_type Details: Let \\(D\\) matrix hydrologic distances, \\(W\\) diagonal matrix weights additive, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zu)\\) given : linear: \\((1 - r) * (r <= 1) * W\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1) * W\\) exponential: \\(exp(-r) * W\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0) * W\\) epa: \\((D - range)^2 * F * (r <= 1) * W / 16range^5\\) none: \\(\\) * W Details describing F matrix epa covariance given Garreta et al. (2010). Flow-unconnected elements \\(R(zu)\\) assumed uncorrelated. Observations different networks also assumed uncorrelated. taildown_type Details: Let \\(D\\) matrix hydrologic distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zd)\\) given : linear: \\((1 - r) * (r <= 1)\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) exponential: \\(exp(-r)\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0)\\) epa: \\((D - range)^2 * F1 * (r <= 1) / 16range^5\\) none: \\(\\) Now let \\(\\) matrix contains shorter two distances two sites common downstream junction, \\(r1 = / range\\), \\(B\\) matrix contains longer two distances two sites common downstream junction, \\(r2 = B / range\\),  \\(\\) identity matrix. parametric forms flow-unconnected elements \\(R(zd)\\) given : linear: \\((1 - r2) * (r2 <= 1)\\) spherical: \\((1 - 1.5r1 + 0.5r2) * (1 - r2)^2 * (r2 <= 1)\\) exponential: \\(exp(-(r1 + r2))\\) mariah: \\((log(90r1 + 1) - log(90r2 + 1)) / (90r1 - 90r2) * (=/ B) + (1 / (90r1 + 1)) * (= B)\\) epa: \\((B - range)^2 * F2 * (r2 <= 1) / 16range^5\\) none: \\(\\) Details describing F1 F2 matrices epa covariance given Garreta et al. (2010). Observations different networks assumed uncorrelated. euclid_type Details: Let \\(D\\) matrix Euclidean distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms elements \\(R(ze)\\) given : exponential: \\(exp(- r )\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) gaussian: \\(exp(- r^2 )\\) cubic: \\((1 - 7r^2 + 8.75r^3 - 3.5r^5 + 0.75r^7) * (r <= 1)\\) pentaspherical: \\((1 - 1.875r + 1.25r^3 - 0.375r^5) * (r <= 1)\\) cosine: \\(cos(r)\\) wave: \\(sin(r) * (h > 0) / r + (h = 0)\\) jbessel: \\(Bj(h * range)\\), Bj Bessel-J function gravity: \\((1 + r^2)^{-0.5}\\) rquad: \\((1 + r^2)^{-1}\\) magnetic: \\((1 + r^2)^{-1.5}\\) none: \\(\\) nugget_type Details: Let \\(\\) identity matrix \\(0\\) zero matrix. parametric forms elements nugget variance given : nugget: \\(\\) none: \\(0\\) short, nugget effect modeled nugget_type \"nugget\" omitted nugget_type \"none\". estmethod Details: various estimation methods reml: Maximize restricted log-likelihood. ml: Maximize log-likelihood. anisotropy Details: default, Euclidean covariance parameters except rotate scale assumed unknown, requiring estimation. either rotate scale given initial values 0 1 (respectively) assumed unknown euclid_initial(), anisotropy implicitly set TRUE. (Geometric) Anisotropy modeled transforming Euclidean covariance function decays differently different directions one decays equally directions via rotation scaling original Euclidean coordinates. rotation controlled rotate parameter \\([0, \\pi]\\) radians. scaling controlled scale parameter \\([0, 1]\\). anisotropy correction involves first rotation coordinates clockwise rotate scaling coordinates' minor axis reciprocal scale. Euclidean covariance computed using transformed coordinates. random Details: random effects used, model can written \\(y = X \\beta + W1\\gamma 1 + ... Wj\\gamma j + zu + zd + ze + n\\), Z random effects design matrix u random effect. partition_factor Details: partition factor can represented matrix form \\(P\\), elements \\(P\\) equal one observations level partition factor zero otherwise. covariance matrix involving spatial random effects components multiplied element-wise (Hadmard product) \\(P\\), yielding final covariance matrix. Details: Observations NA response values removed model fitting, values can predicted afterwards running predict(object).","code":""},{"path":"/reference/ssn_lm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"function perform internal scaling. optimization stable due large extremely large variances, scale relevant variables variance 1 optimization.","code":""},{"path":"/reference/ssn_lm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"Garreta, V., Monestiez, P. Ver Hoef, J.M. (2010) Spatial modelling prediction river networks: model, model, hybrid? Environmetrics 21(5), 439--456. Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644--651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6--18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.","code":""},{"path":"/reference/ssn_lm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) summary(ssn_mod) #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM, ssn.object = mf04p, tailup_type = \"exponential\",  #>     additive = \"afvArea\") #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -3.9487 -2.3775 -0.8840 -0.2776  0.9001  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 71.323162   6.193050  11.517   <2e-16 *** #> ELEV_DEM    -0.028822   0.003075  -9.372   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.6688 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  4.415e+00 #>   tailup exponential         range  1.175e+06 #>               nugget        nugget  2.562e-02 #>"},{"path":"/reference/ssn_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create covariance parameter objects. — ssn_params","title":"Create covariance parameter objects. — ssn_params","text":"Create covariance parameter object us functions. See spmodel::randcov_params() documentation regarding random effect covariance parameter objects.","code":""},{"path":"/reference/ssn_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create covariance parameter objects. — ssn_params","text":"","code":"tailup_params(tailup_type, de, range)  taildown_params(taildown_type, de, range)  euclid_params(euclid_type, de, range, rotate, scale)  nugget_params(nugget_type, nugget)"},{"path":"/reference/ssn_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create covariance parameter objects. — ssn_params","text":"tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". de spatially dependent (correlated) random error variance. Commonly referred partial sill. range correlation parameter. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". rotate Anisotropy rotation parameter (0 \\(\\pi\\) radians) euclidean portion covariance. value 0 (default) implies rotation. scale Anisotropy scale parameter (0 1) euclidean portion covariance. value 1 (default) implies scaling. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". nugget spatially independent (correlated) random error variance. Commonly referred nugget.","code":""},{"path":"/reference/ssn_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create covariance parameter objects. — ssn_params","text":"parameter object class matches relevant type argument.","code":""},{"path":"/reference/ssn_params.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create covariance parameter objects. — ssn_params","text":"Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644--651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6--18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.","code":""},{"path":"/reference/ssn_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create covariance parameter objects. — ssn_params","text":"","code":"tailup_params(\"exponential\", de = 1, range = 20) #>    de range  #>     1    20  #> attr(,\"class\") #> [1] \"tailup_exponential\" taildown_params(\"exponential\", de = 1, range = 20) #>    de range  #>     1    20  #> attr(,\"class\") #> [1] \"taildown_exponential\" euclid_params(\"exponential\", de = 1, range = 20, rotate = 0, scale = 1) #>     de  range rotate  scale  #>      1     20      0      1  #> attr(,\"class\") #> [1] \"euclid_exponential\" nugget_params(\"nugget\", nugget = 1) #> nugget  #>      1  #> attr(,\"class\") #> [1] \"nugget_nugget\""},{"path":"/reference/ssn_put_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Put an sf data.frame in an SSN object — ssn_put_data","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"ssn_put_data function puts sf data.frame representing observation prediction data SSN, ssn_lm, ssn_glm object.","code":""},{"path":"/reference/ssn_put_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"","code":"ssn_put_data(data, x, name = \"obs\", resize_data = FALSE)"},{"path":"/reference/ssn_put_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"data sf data.frame point geometry. x object class SSN, ssn_lm, ssn_glm. name internal name data set object x. observed data, always \"obs\", default. resize_data Logical. Indicates whether sf_df can different number features current data.frame object. Default FALSE.","code":""},{"path":"/reference/ssn_put_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"Returns object class x, contains sf data.frame sf_data.","code":""},{"path":"/reference/ssn_put_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"internal name observed data objects class SSN, ssn_lm, ssn_glm \"obs\" default. another name specified, must represent prediction dataset object. SSN objects, names obtained using call names(x$preds). object classes, names obtained using call names(x$ssn.object$preds). resize_sf_data argument specifies whether sf_data can different number features (.e., rows) sf data.frame replacing. Care taken resize_df set TRUE, especially new sf_data features existing sf data.frame. cases, user responsible ensuring additional features correct spatial, topological, attribute data accurately represent spatial relationships SSN object.","code":""},{"path":[]},{"path":"/reference/ssn_put_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"","code":"data(mf04p) ## Extract observation data.frame from SSN object obs.df <- ssn_get_data(mf04p) ## Create a new column for summer mean temperature and set Value in obs.df$Value <- obs.df$Summer_mn obs.df$Value[1] <- NA  ## Put the modified sf data.frame into the SSN object mf04p <- ssn_put_data(obs.df, mf04p) head(ssn_get_data(mf04p)[, c(\"Summer_mn\", \"Value\")]) #> Simple feature collection with 6 features and 2 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1515032 ymin: 2529461 xmax: -1512690 ymax: 2531883 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version #>   Summer_mn Value                 geometry #> 1     11.38    NA POINT (-1512690 2531883) #> 2     10.72 10.72 POINT (-1512852 2531295) #> 3     10.43 10.43 POINT (-1513400 2530706) #> 4     10.13 10.13 POINT (-1514027 2530147) #> 5     10.12 10.12 POINT (-1514309 2529902) #> 6      9.81  9.81 POINT (-1515032 2529461)"},{"path":"/reference/ssn_simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate random variables on a stream network — ssn_simulate","title":"Simulate random variables on a stream network — ssn_simulate","text":"Simulate random variables stream network specific mean covariance structure. Designed use ssn_simulate(), individual simulation functions resposne distribution also exist.","code":""},{"path":"/reference/ssn_simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate random variables on a stream network — ssn_simulate","text":"","code":"ssn_simulate(   family = \"Gaussian\",   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   additive,   mean = 0,   samples = 1,   dispersion = 1,   size = 1,   randcov_params,   partition_factor,   ... )  ssn_rbeta(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rbinom(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   mean = 0,   size = 1,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rgamma(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rinvgauss(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rnbinom(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rnorm(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rpois(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )"},{"path":"/reference/ssn_simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate random variables on a stream network — ssn_simulate","text":"family response distribution family. default \"Gaussian\". ssn.object spatial stream network object class SSN. Random variables simulated row ssn.object$obs. network spatial stream network simulate . Currently allowed \"obs\" ssn.object$obs object. tailup_params object tailup_params() specifying tailup covariance parameters. taildown_params object taildown_params() specifying taildown covariance parameters. euclid_params object euclid_params() specifying Euclidean covariance parameters. nugget_params object nugget_params() specifying nugget covariance parameters. additive name variable ssn.object used define spatial weights. Can quoted unquoted. tailup covariance functions, additive weights used branching. Technical details describe role additive variable tailup covariance function available Ver Hoef Peterson (2010). mean numeric vector representing mean. mean must length 1 (case recycled) length equal number rows data. default 0. samples number independent samples generate. default 1. dispersion dispersion value (relevant). size numeric vector representing sample size binomial trial. default 1, corresponds Bernoulli trial observation. randcov_params spmodel::randcov_params() object. partition_factor formula indicating partition factor. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/ssn_simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate random variables on a stream network — ssn_simulate","text":"samples 1, vector random variables row ssn.object$obs returned. samples greater one, matrix random variables returned, rows correspond row ssn.object$obs columns correspond independent samples.","code":""},{"path":"/reference/ssn_simulate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate random variables on a stream network — ssn_simulate","text":"Random variables simulated via product covariance matrix's square (Cholesky) root independent standard normal random variables link scale, used simulate relevant variable response scale according family. Computing square root significant computational burden likely unfeasible sample sizes much past 10,000. square root needs computed , however, nearly sample computational cost call ssn_rnorm() value samples. using ssn_simulate(), individual simulation functions response distribution exist: ssn_rnorm(): Simulate Gaussian distribution ssn_rpois(): Simulate Poisson distribution ssn_rnbinom(): Simulate negative binomial distribution ssn_rbinom(): Simulate binomial distribution ssn_rbeta(): Simulate beta distribution ssn_rgamma(): Simulate gamma distribution ssn_rinvgauss(): Simulate inverse Gaussian distribution","code":""},{"path":"/reference/ssn_simulate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate random variables on a stream network — ssn_simulate","text":"Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6--18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.","code":""},{"path":"/reference/ssn_simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate random variables on a stream network — ssn_simulate","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  tailup <- tailup_params(\"exponential\", de = 0.1, range = 200) taildown <- taildown_params(\"exponential\", de = 0.4, range = 300) euclid <- euclid_params(\"spherical\", de = 0.2, range = 1000, rotate = 0, scale = 1) nugget <- nugget_params(\"nugget\", nugget = 0.1) ssn_simulate(\"gaussian\", mf04p, \"obs\", tailup, taildown, euclid, nugget, additive = \"afvArea\") #>  [1]  0.23243313  0.45329177  0.18570140 -2.34240654 -1.64613982 -1.87065835 #>  [7]  0.29619710  0.64904228 -0.03683179  0.94769933  0.60677198  1.18204714 #> [13]  0.68230622 -0.19460793 -1.16475226  0.82461078  0.34565867  0.91866220 #> [19] -1.72120539 -0.68537520  0.09534920 -0.84413067  0.94423558  0.01030223 #> [25]  0.88562756  1.92679655  0.70020757  0.17573536  0.07202305 -0.17097118 #> [31] -0.73808153 -0.89988571  0.78511665 -0.03124410 -0.67371771  0.39741993 #> [37]  0.14390780 -0.07988414  0.65623517  0.82860617 -0.13304709  0.01619557 #> [43]  1.31099525 -0.31899800  1.08881410"},{"path":"/reference/ssn_split_predpts.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a prediction dataset in an SSN object — ssn_split_predpts","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"splitPrediction function used split prediction sets SSN object smaller prediction sets. returns SSN object additional prediction sets based equal interval splits, factor, integer, character logical column stored within prediction set, logical expression.","code":""},{"path":"/reference/ssn_split_predpts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"","code":"ssn_split_predpts(   ssn,   predpts,   size_predpts,   by,   subset,   id_predpts,   keep = TRUE,   drop_levels = FALSE,   overwrite = FALSE )"},{"path":"/reference/ssn_split_predpts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"ssn SSN object. predpts character string representing name prediction dataset size_predpts numeric value representing size new prediction sets. existing prediction set split equally produce multiple prediction sets size character string representing column name type factor, integer, character logical split based subset logical expression indicating elements rows keep; missing values taken FALSE id_predpts character string representing new prediction dataset name. value specified subset method used keep logical value indicating whether original prediction dataset retained SSN object. Default TRUE drop_levels logical value indicating whether empty factor levels dropped column new prediction dataset(s) created. Default FALSE overwrite logical indicating whether new prediction dataset shapefile deleted .ssn directory already exists. Default = FALSE","code":""},{"path":"/reference/ssn_split_predpts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"returns SSN specified ssn, one new prediction sets. Shapefiles new prediction sets written .ssn directory designated ssn$path.","code":""},{"path":"/reference/ssn_split_predpts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"Three methods provided split prediction sets: size_predpts, , subset. size_predpts method used split existing prediction set multiple equally-sized prediction sets. Note final prediction set may smaller size others total number predictions evenly divisible size_predpts. method used prediction set split multiple new prediction sets based existing column type factor, integer, character, logical. subset method used create one new prediction set based logical expression. one prediction dataset created prediction dataset names appended hyphen prediction dataset number one prediction dataset created. example, \"preds\" split using size_predpts, new names \"preds-1\", \"preds-2\", forth. keep=FALSE, prediction dataset removed SSN object stored memory, deleted .ssn directory specified ssn$path. Note , one method may specified ssn_split_predpts function called. distance matrices new prediction datasets must created using ssn_create_distmat predictions can made.","code":""},{"path":"/reference/ssn_split_predpts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"","code":"## Import SSN object copy_lsn_to_temp() ## Only needed for this example ssn <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(\"pred1km.shp\", \"Knapp\", \"CapeHorn\"),   overwrite = TRUE )  ## Split predictions into size_predpts 200 ssn1 <- ssn_split_predpts(ssn, \"CapeHorn\",   size_predpts = 200,   keep = FALSE, overwrite = TRUE ) names(ssn1$preds) #> [1] \"pred1km\"    \"Knapp\"      \"CapeHorn-1\" \"CapeHorn-2\" \"CapeHorn-3\" #> [6] \"CapeHorn-4\" nrow(ssn1$preds[[\"CapeHorn-1\"]]) #> [1] 200  ## Split predictions using by method ssn$preds$pred1km$net.fac <- as.factor(ssn$preds$pred1km$netID) ssn2 <- ssn_split_predpts(ssn, \"pred1km\",   by = \"net.fac\",   overwrite = TRUE ) names(ssn2$preds) #> [1] \"pred1km\"           \"Knapp\"             \"CapeHorn\"          #> [4] \"pred1km-net.fac-1\" \"pred1km-net.fac-2\"  ## Split predictions using subset method ssn3 <- ssn_split_predpts(ssn, \"pred1km\",   subset = ratio > 0.5,   id_predpts = \"RATIO_05\", overwrite = TRUE ) names(ssn3$preds) #> [1] \"pred1km\"  \"Knapp\"    \"CapeHorn\" \"RATIO_05\""},{"path":"/reference/ssn_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset an SSN object — ssn_subset","title":"Subset an SSN object — ssn_subset","text":"Returns SSN object subset based logical expression.","code":""},{"path":"/reference/ssn_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset an SSN object — ssn_subset","text":"","code":"ssn_subset(ssn, path, subset, clip = FALSE, overwrite = FALSE)"},{"path":"/reference/ssn_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset an SSN object — ssn_subset","text":"ssn SSN object. path filepath .ssn folder, string format, subset SSN saved. subset logical expression indicating features keep. clip TRUE, create subset edges prediction sites, based logical expression used subset observed sites.  Default = FALSE. overwrite TRUE, overwrite folder specified path exists. Default = FALSE.","code":""},{"path":"/reference/ssn_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset an SSN object — ssn_subset","text":"object class SSN, stored locally .ssn directory specified path. also creates stores SQLite database, binaryID.db, within .ssn directory.","code":""},{"path":"/reference/ssn_subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subset an SSN object — ssn_subset","text":"function creates subset original SSN object based logical expression defined subset argument. subset argument treated expression within ssn_subset() full argument string; although values factor character format still require quotes (see examples). clip = TRUE, columns referred subset must present edges prediction datasets (present SSN object). Note features missing values subset expression treated false included subset SSN object. subset SSN object written local directory, re-imported using ssn_import. process, binaryID.db recreated. distance matrices exist original SSN object, copied recalculated new SSN object. Users need run ssn_create_distmat create distance matrices fitting models data subset SSN.","code":""},{"path":"/reference/ssn_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset an SSN object — ssn_subset","text":"","code":"## Import SSN object copy_lsn_to_temp() ## Only needed for this example mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(\"pred1km.shp\", \"Knapp\"),   overwrite = TRUE )  ## Subset SSN observations, edges, and prediction sites on network 1 ssn.sub1 <- ssn_subset(mf04p,   path = paste0(tempdir(), \"/subset1.ssn\"),   subset = netID == 1, clip = TRUE,   overwrite = TRUE )  ## Subset SSN observations, removing two sites ssn.sub2 <- ssn_subset(mf04p,   path = paste0(tempdir(), \"/subset2.ssn\"),   subset = !COMID %in% c(\"23519461\", \"23519365\"),   overwrite = TRUE )"},{"path":"/reference/SSN_to_SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"Convert S4 SpatialStreamNetwork object created SSN package S3 SSN object used SSN2 package.","code":""},{"path":"/reference/SSN_to_SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"","code":"SSN_to_SSN2(object, edge_additive = NULL, site_additive = NULL)"},{"path":"/reference/SSN_to_SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"object SpatialStreamNetwork object edge_additive character vector additive function value column names found edges. Default NULL. See Details information. site_additive character vector additive function value column names found observed sites prediction sites. See Details information. Default NULL.","code":""},{"path":"/reference/SSN_to_SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"S3 SSN class object, additive function value columns converted text format.","code":""},{"path":"/reference/SSN_to_SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"SSN_to_SSN2() made available help users migrate SSN package updated SSN2 package. used convert existing S4 SpatialStreamNetwork objects stored saved workspaces S3 SSN class object used SSN2 package. Note ssn_import used create S3 SSN object data stored locally .ssn directory. Additive function values used generate spatial weights tail-covariance function used ssn_glm. range additive function values restricted \\(0 \\le AFV \\le   1\\). SSN2 package, columns containing additive function values stored text, rather numeric format. prevents values less 1 10 digits truncated writing/reading shapefiles (.dbf tables). columns containing additive function values specified using edge_additive site_additive arguments converted character format SSN class object returned. arguments edge_additive site_additive accept single column name character format, vector containing multiple column names. Note , column names additive function values edges, sites, prediction sites may differ. column specified edge_additive site_additive present, function return warning, rather error. Columns containing additive function values can also converted text manually using formatC function, provides flexibility needed store values full precision.","code":""},{"path":"/reference/ssn_update_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Update path in an SSN object — ssn_update_path","title":"Update path in an SSN object — ssn_update_path","text":"Update local path existing SSN object based user defined file.","code":""},{"path":"/reference/ssn_update_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update path in an SSN object — ssn_update_path","text":"","code":"ssn_update_path(x, path, verbose = FALSE)"},{"path":"/reference/ssn_update_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update path in an SSN object — ssn_update_path","text":"x SSN, ssn_lm ssn_glm object. path Filepath .ssn folder associated SSN object. verbose logical indicates new path printed console.","code":""},{"path":"/reference/ssn_update_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update path in an SSN object — ssn_update_path","text":"SSN object new path list element.","code":""},{"path":"/reference/ssn_update_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Update path in an SSN object — ssn_update_path","text":"times, may necessary move .ssn directory, linked SSN object R workspace. .ssn directory moved, path must updated using ssn_glmssn function functions read/write .ssn directory. ssn_update_path helper function serves purpose.","code":""},{"path":"/reference/ssn_update_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update path in an SSN object — ssn_update_path","text":"","code":"## Use mf04p SSN object provided in SSN2 data(mf04p)  ## For examples only, make sure mf04p has the correct path ## If you use ssn_import(), the path will be correct newpath <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_update_path(mf04p, newpath)"},{"path":"/reference/ssn_write.html","id":null,"dir":"Reference","previous_headings":"","what":"write an SSN object — ssn_write","title":"write an SSN object — ssn_write","text":"function writes SSN object local .ssn directory","code":""},{"path":"/reference/ssn_write.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"write an SSN object — ssn_write","text":"","code":"ssn_write(ssn, path, overwrite = FALSE, copy_dist = FALSE, import = FALSE)"},{"path":"/reference/ssn_write.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"write an SSN object — ssn_write","text":"ssn SSN object. path filepath local .ssn directory write . overwrite TRUE, overwrite existing files file (exists). Defaults FALSE. copy_dist TRUE, copy distance matrices file (exist). Defaults FALSE. import TRUE, import return SSN object writing file. Defaults FALSE.","code":""},{"path":"/reference/ssn_write.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"write an SSN object — ssn_write","text":"ssn_write creates .ssn directory contains spatial, topological, attribute information stored original SSN object. import = TRUE, SSN object imported returned.","code":""},{"path":"/reference/ssn_write.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"write an SSN object — ssn_write","text":"","code":"## For examples only, copy MiddleFork04.ssn directory to R's # temporary directory copy_lsn_to_temp() ## Import SSN object with prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(\"pred1km.shp\"),   overwrite = TRUE )  ## Write SSN to new .ssn directory ssn_write(mf04p, path = paste0(tempdir(), \"/tempSSN.ssn\"))  ## Write SSN to .ssn directory and return SSN object tempSSN <- ssn_write(mf04p, path = paste0(   tempdir(),   \"/tempSSN.ssn\" ), overwrite = TRUE, import = TRUE) #> binaryID.db already exists - no changes were made to binaryID.db table"},{"path":"/reference/summary.SSN.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize an SSN object — summary.SSN","title":"Summarize an SSN object — summary.SSN","text":"Summarize data found SSN object.","code":""},{"path":"/reference/summary.SSN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize an SSN object — summary.SSN","text":"","code":"# S3 method for SSN summary(object, ...)"},{"path":"/reference/summary.SSN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize an SSN object — summary.SSN","text":"object SSN object. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/summary.SSN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize an SSN object — summary.SSN","text":"list several fitted model quantities used create informative summaries printing.","code":""},{"path":"/reference/summary.SSN.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summarize an SSN object — summary.SSN","text":"summary.SSN() creates summary SSN object intended printed using print(). summary contains information number observed prediction locations, well column names found respective sf data.frames.","code":""},{"path":"/reference/summary.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted model object — summary.SSN2","title":"Summarize a fitted model object — summary.SSN2","text":"Summarize fitted model object.","code":""},{"path":"/reference/summary.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted model object — summary.SSN2","text":"","code":"# S3 method for ssn_lm summary(object, ...)  # S3 method for ssn_glm summary(object, ...)"},{"path":"/reference/summary.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted model object — summary.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/summary.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted model object — summary.SSN2","text":"list several fitted model quantities used create informative summaries printing.","code":""},{"path":"/reference/summary.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summarize a fitted model object — summary.SSN2","text":"summary.ssn() creates summary fitted model object intended printed using print(). summary contains useful information like original function call, residuals, coefficients table, pseudo r-squared, estimated covariance parameters.","code":""},{"path":[]},{"path":"/reference/summary.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize a fitted model object — summary.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) summary(ssn_mod) #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM, ssn.object = mf04p, tailup_type = \"exponential\",  #>     additive = \"afvArea\") #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -3.9487 -2.3775 -0.8840 -0.2776  0.9001  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 71.323162   6.193050  11.517   <2e-16 *** #> ELEV_DEM    -0.028822   0.003075  -9.372   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.6688 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  4.415e+00 #>   tailup exponential         range  1.175e+06 #>               nugget        nugget  2.562e-02 #>"},{"path":"/reference/tidy.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy a fitted model object — tidy.SSN2","title":"Tidy a fitted model object — tidy.SSN2","text":"Tidy fitted model object summarized tibble.","code":""},{"path":"/reference/tidy.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy a fitted model object — tidy.SSN2","text":"","code":"# S3 method for ssn_lm tidy(x, conf.int = FALSE, conf.level = 0.95, effects = \"fixed\", ...)  # S3 method for ssn_glm tidy(x, conf.int = FALSE, conf.level = 0.95, effects = \"fixed\", ...)"},{"path":"/reference/tidy.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy a fitted model object — tidy.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). conf.int Logical indicating whether include confidence interval tidied output. default FALSE. conf.level confidence level use confidence interval conf.int TRUE. Must strictly greater 0 less 1. default 0.95, corresponds 95 percent confidence interval. effects type effects tidy. Available options \"fixed\" (fixed effects), \"tailup\" (tailup covariance parameters), \"taildown\" (taildown covariance parameters), \"euclid\" (Euclidean covariance parameters), \"nugget\" (nugget covariance parameter), \"dispersion\" (dispersion parameter relevant), \"ssn\" \"tailup\", \"taildown\", \"euclid\", \"nugget\", \"dispersion\", \"randcov\" (random effect variances). default \"fixed\". ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/tidy.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy a fitted model object — tidy.SSN2","text":"tidy tibble summary information effects.","code":""},{"path":[]},{"path":"/reference/tidy.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy a fitted model object — tidy.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) tidy(ssn_mod) #> # A tibble: 2 × 5 #>   term        estimate std.error statistic p.value #>   <chr>          <dbl>     <dbl>     <dbl>   <dbl> #> 1 (Intercept)  71.3      6.19        11.5        0 #> 2 ELEV_DEM     -0.0288   0.00308     -9.37       0"},{"path":"/reference/Torgegram.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the empirical semivariogram — Torgegram","title":"Compute the empirical semivariogram — Torgegram","text":"Compute empirical semivariogram varying bin sizes cutoff values.","code":""},{"path":"/reference/Torgegram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the empirical semivariogram — Torgegram","text":"","code":"Torgegram(   formula,   ssn.object,   type = c(\"flowcon\", \"flowuncon\"),   bins = 15,   cutoff,   partition_factor )"},{"path":"/reference/Torgegram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the empirical semivariogram — Torgegram","text":"formula formula describing fixed effect structure. ssn.object spatial stream network object class SSN. type Torgegram type. vector possible values \"flowcon\" flow-connected distances, \"flowuncon\" flow-unconnected distances, \"euclid\" Euclidean distances. default show flow-connected flow-unconnected distances. bins number equally spaced bins. default 15. cutoff maximum distance considered. default half diagonal bounding box coordinates. partition_factor optional formula specifying partition factor. specified, semivariances computed observations sharing level partition factor.","code":""},{"path":"/reference/Torgegram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the empirical semivariogram — Torgegram","text":"list elements correspond type. element data frame distance bins (bins),  average distance (dist), semivariance (gamma), number (unique) pairs (np) respective type.","code":""},{"path":"/reference/Torgegram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute the empirical semivariogram — Torgegram","text":"Torgegram empirical semivariogram tool used visualize model spatial dependence estimating semivariance process varying distances separately flow-connected, flow-unconnected, Euclidean distances. constant-mean process, semivariance distance \\(h\\) denoted \\(\\gamma(h)\\) defined \\(0.5 * Var(z1  - z2)\\). second-order stationarity, \\(\\gamma(h) = Cov(0) - Cov(h)\\), \\(Cov(h)\\) covariance function distance h. Typically residuals ordinary least squares fit defined formula second-order stationary mean zero. residuals used compute empirical semivariogram. distance h, empirical semivariance \\(1/N(h) \\sum (r1 - r2)^2\\), \\(N(h)\\) number (unique) pairs set observations whose distance separation h r1 r2 residuals corresponding observations whose distance separation h. spmodel, distance bins actually contain observations whose distance separation h +- c, c constant determined implicitly bins. Typically, observations whose distance separation cutoff used compute empirical semivariogram (cutoff determined cutoff).","code":""},{"path":"/reference/Torgegram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute the empirical semivariogram — Torgegram","text":"Zimmerman, D. L., & Ver Hoef, J. M. (2017). Torgegram fluvial variography: characterizing spatial dependence stream networks. Journal Computational Graphical Statistics, 26(2), 253--264.","code":""},{"path":[]},{"path":"/reference/Torgegram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the empirical semivariogram — Torgegram","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  tg <- Torgegram(Summer_mn ~ 1, mf04p) plot(tg)"},{"path":"/reference/varcomp.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Variability component comparison — varcomp.SSN2","title":"Variability component comparison — varcomp.SSN2","text":"Compare proportion total variability explained fixed effects variance parameter.","code":""},{"path":"/reference/varcomp.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variability component comparison — varcomp.SSN2","text":"","code":"# S3 method for ssn_lm varcomp(object, ...)  # S3 method for ssn_glm varcomp(object, ...)"},{"path":"/reference/varcomp.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variability component comparison — varcomp.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/varcomp.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variability component comparison — varcomp.SSN2","text":"tibble partitions total variability fixed effects variance parameter. proportion variability explained fixed effects pseudo R-squared obtained psuedoR2(). remaining proportion spread accordingly among variance parameter: \"tailup_de\", \"taildown_de\", \"euclid_de\", \"nugget\", random effects used, named random effect. ssn_glm(), models, variances link scale considered (.e., variance function response omitted).","code":""},{"path":"/reference/varcomp.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variability component comparison — varcomp.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) varcomp(ssn_mod) #> # A tibble: 5 × 2 #>   varcomp            proportion #>   <chr>                   <dbl> #> 1 Covariates (PR-sq)    0.669   #> 2 tailup_de             0.329   #> 3 taildown_de           0       #> 4 euclid_de             0       #> 5 nugget                0.00191"},{"path":"/reference/vcov.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"Calculate variance-covariance matrix fitted model object.","code":""},{"path":"/reference/vcov.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"","code":"# S3 method for ssn_lm vcov(object, ...)  # S3 method for ssn_glm vcov(object, var_correct = TRUE, ...)"},{"path":"/reference/vcov.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency). var_correct logical indicating whether return corrected variance-covariance matrix models fit using ssn_glm() (family different \"Gaussian\"). default TRUE.","code":""},{"path":"/reference/vcov.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"variance-covariance matrix coefficients obtained via coef(). Currently, variance-covariance matrix fixed effects supported.","code":""},{"path":"/reference/vcov.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) vcov(ssn_mod) #>             (Intercept)      ELEV_DEM #> (Intercept) 38.35386871 -1.902743e-02 #> ELEV_DEM    -0.01902743  9.458396e-06"},{"path":"/news/index.html","id":"ssn2-011","dir":"Changelog","previous_headings":"","what":"SSN2 0.1.1","title":"SSN2 0.1.1","text":"CRAN release: 2024-01-15","code":""},{"path":"/news/index.html","id":"minor-updates-0-1-1","dir":"Changelog","previous_headings":"","what":"Minor Updates","title":"SSN2 0.1.1","text":"Changed network geometry name netgeometry netgeom avoid exceeding 10 character limit column/field names writing shapefiles (#2). Added error message family missing ssn_glm() (#8). Added deprecation warning SSN_to_SSN2(). Minor stability updates. Minor error message updates. Minor documentation updates.","code":""},{"path":"/news/index.html","id":"bug-fixes-0-1-1","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"SSN2 0.1.1","text":"Fixed bug Torgegram() prevented intended computation cutoff specified. Fixed bug plot.Torgegram() occasionally prevented proper spacing legend. Fixed bug prevented proper printing dispersion parameter ssn_glm() model objects (summaries) covariance parameters known. Fixed bug prevented simulation euclid_type \"none\". Fixed bug cause improper prediction behavior taildown_type \"spherical\". Fixed bug printed response residuals instead deviance residuals ssn_glm() objects.","code":""},{"path":"/news/index.html","id":"ssn2-010","dir":"Changelog","previous_headings":"","what":"SSN2 0.1.0","title":"SSN2 0.1.0","text":"CRAN release: 2023-11-08 Initial CRAN submission.","code":""}]
+[{"path":"/articles/introduction.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"Data streams frequently exhibit unique patterns spatial autocorrelation resulting branching network structure, longitudinal (.e., upstream/downstream) connectivity, directional water flow, differences flow volume throughout network (Erin E. Peterson et al. 2013). addition, stream networks embedded within spatial environment, can also influence observations stream network. Traditional spatial statistical models, based solely Euclidean distance, often fail adequately describe unique complex spatial dependencies. Spatial stream network models based moving-average construction (J. M. Ver Hoef Peterson 2010) specifically designed describe two unique spatial relationships found streams data. pair sites considered flow-connected water flows upstream site downstream site. Sites flow-unconnected reside stream network (.e., share common junction downstream) share flow. Spatial stream network models typically rely two families covariance functions represent relationships: tail-tail-models. tail-model, moving-average function points upstream direction. Covariance function stream distance weighting structure used proportionally allocate, split, function upstream junctions account differences flow volume influential variables (Erin E. Peterson Ver Hoef 2010). result, non-zero covariances restricted flow-connected sites tail-model. tail-model, moving average function points downstream direction. contrast tail-model, tail-models allow flow-connected flow-unconnected autocorrelation, autocorrelation always equal stronger flow-unconnected sites flow-connected sites separated equal stream distances (J. M. Ver Hoef Peterson 2010). tail-model, covariance function stream distance weights required. However, also possible often preferable build spatial stream network models based mixture four components: tail-component, tail-component, Euclidean component, nugget component. Euclidean component useful captures covariance influential processes independent stream network intermediate broad scales (e.g., air temperature, soil type, geology). nugget component captures covariance processes highly localized, thus independent across sites. details regarding construction spatial stream network models covariance components, see Cressie et al. (2006), J. M. Ver Hoef, Peterson, Theobald (2006), J. M. Ver Hoef Peterson (2010), Erin E. Peterson Ver Hoef (2010), Isaak et al. (2014). SSN2 R package used fit summarize spatial stream network models make predictions unobserved locations (Kriging). SSN2 updated version SSN R package (J. Ver Hoef et al. 2014). create SSN2 replace SSN R package? two main reasons: SSN R package depends rgdal (Bivand, Keitt, Rowlingson 2021), rgeos (Bivand Rundel 2020), maptools (Bivand Lewin-Koh 2021) R packages, retired October, 2023. functionality replaced modernized sf package (Pebesma 2018). SSN2 depends sf instead rgdal, rgeos, maptools, reflecting broader change regarding handling spatial data R. See Nowosad (2023) information regarding retirement rgdal, rgeos, maptools, available link. features added SSN2 difficult implement SSN R package without massive restructuring foundation, new package created. example, SSN objects SSN2 S3 objects SSN objects SSN R package S4 objects. Additionally, many functions rewritten /repurposed SSN2 use generic functions (e.g., block prediction SSN2 performed using predict() SSN R package performed using BlockPredict()). Moreover, SSN2 leverages many tools spmodel R package spatial statistical modeling (Dumelle, Higham, Ver Hoef 2023). vignette provides overview basic features SSN2. load SSN2 running use SSN2 formal publication report, please cite . Citing SSN2 lets us devote resources future. view SSN2 citation running","code":"library(SSN2) citation(package = \"SSN2\") #> To cite SSN2 in publications use: #>  #>   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: #>   Spatial Modeling on Stream Networks in R. R package version 0.2.0 #>  #> A BibTeX entry for LaTeX users is #>  #>   @Manual{, #>     title = {{SSN2}: Spatial Modeling on Stream Networks in {R}}, #>     author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak}, #>     year = {2023}, #>     note = {{R} package version 0.2.0}, #>   }"},{"path":"/articles/introduction.html","id":"input-data","dir":"Articles","previous_headings":"","what":"Input Data","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"streams, observation, prediction datasets must pre-processed prior fitting SSN models making predictions unobserved locations using SSN2. Previously, STARS toolset ArcGIS Desktop versions 9.3x - 10.8x (E. Peterson Ver Hoef 2014) openSTARS R package (kattwinkel2020preparing?) used generate spatial information required model fitting prediction. However, software packages recently retired replaced SSNbler R package (Erin E. Peterson et al. 2024), new, R-based version STARS tools. SSNbler currently available GitHub (link ), soon available CRAN, contains several useful resources guide users pre-processing steps. Pre-processing using either SSNbler, STARS, openSTARS ends creatino .ssn folder, non-proprietary. Files residing .ssn folder read R using ssn_import() SSN2 palced list structure called SSN object, contains spatial, topological, attribute information needed leverage modeling tools SSN2. includes: edges: geopackage lines representing linear geometry stream network(s). sites: geopackage site locations observed data collected stream network. prediction sites: one geopackages locations predictions made. Optional. netIDx.dat stream network: text file containing topological relationships line segments edges, network.","code":""},{"path":"/articles/introduction.html","id":"ssn-objects-in-ssn2","dir":"Articles","previous_headings":"","what":"SSN Objects in SSN2","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"data contained .ssn object read R stored SSN object, special list structure four elements: edges: sf object contains edges LINESTRING geometry (representing linear geometry stream network/s). endpoints edges called nodes. obs: sf object contains observed data POINT geometry. preds list sf objects POINT geometry, containing set locations predictions made. path: character string represents path relevant .ssn directory stored computer. netgeom (short “network geometry”) column also added sf objects stored within SSN object. netgeom column contains character string describing position line (edges) point (obs preds) feature relation one another. format netgeom column differs depending whether describing feature LINESTRING POINT geometry. edges, format netgeom \"ENETWORK (netID rid upDist)\", sites \"SNETWORK (netID rid upDist ratio pid locID)\", data used define netgeom column found edges, observed sites, prediction sites geopackages, created using SSNbler (recommended), STARS, openSTARS software. edges, includes unique network identifier (netID) reach (.e., edge) identifier (rid), well distance downstream location stream network (.e., stream outlet) upstream node edge segment, movement restricted stream network (upDist). netgeom column sites also contains netID rid edge site resides. point identifier (pid) unique measurement, location identifier (locID) unique spatial location. Note locID may multiple pids associated repeated measurements observed data multiple predictions made location. upDist value site represents stream distance stream outlet site location. Finally, ratio used describe relative position site associated edge segment. proportional distance downstream node edge segment site location. example, ratio site close zero site close downstream node edge segment, ratio site close one site far downstream node edge segment. Together key pieces data used describe network edge site resides , well exactly site line segment. may first seem redundant combine store multiple numeric columns text netgeom column. However, data dictate observed prediction sites relate one another topological space, impacts parameter estimates predicted values generated fitted models. Storing data text netgeom column significantly reduces chance values accidentally (unknowingly) altered user.","code":""},{"path":"/articles/introduction.html","id":"the-middle-fork-data","dir":"Articles","previous_headings":"SSN Objects in SSN2","what":"The Middle Fork Data","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"vignette, use Middle Fork 2004 stream temperature data SSN2. raw input data stored lsndata/MiddleFork04.ssn directory installed alongside SSN2. may store file path example data: Several functions SSN2 reading writing data (use shortly) directly manipulate .ssn folder. desirable directly manipulate MiddleFork04.ssn data installed alongside SSN2, MiddleFork04.ssn may copied temporary directory relevant path alternative location can stored: specifying path (using system.file() copy_lsn_to_temp()), import stream reaches, observed sites, prediction sites: summarise mf04p data running see mf04p contains 45 observation sites total 829 prediction sites stored two different prediction datasets. explore several variables throughout rest vignette: AREAWTMAP: Precipitation (area-weighted mm) ELEV_DEM: Elevation (based 30m DEM) Summer_mn: Summer mean stream temperature (Celsius) C16: Number times daily stream temperature exceeded 16 Celsius (summer) detailed description variables mf04p available documentation can seen running ?MiddleFork04.ssn help(MiddleFork04.ssn, package = \"SSN2\"). SSN2 currently generic plotting function SSN objects. Instead, rely plotting functionality ggplot2 (Wickham 2016) sf (Pebesma 2018). vignette focuses use ggplot2, load running ggplot2 installed alongside SSN2 dependencies = TRUE install.packages(), check installed reproducing visualizations vignette. Prediction sites can easily accessed SSN object using list element number names attribute. example, print names prediction datasets console view Middle Fork stream network, overlay observed sites data collected using brown circles, overlay pred1km prediction locations using smaller, blue triangles running  Later fit models stream network data. , however, supplement .ssn object hydrologic distance matrices preserve directionality, required statistical modeling: Stream distance matrices saved local files .ssn directory associated SSN object, mf04p$path, folder called distance created ssn_create_distmat(). matrices stored .Rdata files separate sub-folders observed sites (obs) set prediction sites. file path .ssn directory incorrect, ssn_update_path() can used update distance matrices generated.","code":"path <- system.file(\"lsndata/MiddleFork04.ssn\", package = \"SSN2\") copy_lsn_to_temp() path <- file.path(tempdir(), \"MiddleFork04.ssn\") mf04p <- ssn_import(   path = path,   predpts = c(\"pred1km\", \"CapeHorn\"),   overwrite = TRUE ) summary(mf04p) #> Object of class SSN #>  #> Object includes observations on 26 variables across 45 sites within the bounding box #>       xmin       ymin       xmax       ymax  #> -1531384.6   914394.3 -1498447.7   933487.5  #>  #> Object also includes 2 sets of prediction points with a total of 829 locations #>  #> Variable names are (found using ssn_names(object)): #> $obs #>  [1] \"rid\"        \"pid\"        \"STREAMNAME\" \"COMID\"      \"AREAWTMAP\"  #>  [6] \"SLOPE\"      \"ELEV_DEM\"   \"Source\"     \"Summer_mn\"  \"MaxOver20\"  #> [11] \"C16\"        \"C20\"        \"C24\"        \"FlowCMS\"    \"AirMEANc\"   #> [16] \"AirMWMTc\"   \"rcaAreaKm2\" \"h2oAreaKm2\" \"ratio\"      \"snapdist\"   #> [21] \"upDist\"     \"afvArea\"    \"locID\"      \"netID\"      \"netgeom\"    #> [26] \"geometry\"   #>  #> $pred1km #>  [1] \"rid\"        \"pid\"        \"COMID\"      \"AREAWTMAP\"  \"SLOPE\"      #>  [6] \"ELEV_DEM\"   \"FlowCMS\"    \"AirMEANc\"   \"AirMWMTc\"   \"rcaAreaKm2\" #> [11] \"h2oAreaKm2\" \"ratio\"      \"snapdist\"   \"upDist\"     \"afvArea\"    #> [16] \"locID\"      \"netID\"      \"netgeom\"    \"geom\"       #>  #> $CapeHorn #>  [1] \"rid\"        \"pid\"        \"COMID\"      \"AREAWTMAP\"  \"SLOPE\"      #>  [6] \"ELEV_DEM\"   \"FlowCMS\"    \"AirMEANc\"   \"AirMWMTc\"   \"rcaAreaKm2\" #> [11] \"h2oAreaKm2\" \"ratio\"      \"snapdist\"   \"upDist\"     \"afvArea\"    #> [16] \"locID\"      \"netID\"      \"netgeom\"    \"geom\" library(ggplot2) names(mf04p$preds) #> [1] \"pred1km\"  \"CapeHorn\" ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$preds$pred1km, pch = 17, color = \"blue\") +   geom_sf(data = mf04p$obs, color = \"brown\", size = 2) +   theme_bw() ssn_create_distmat(   ssn.object = mf04p,   predpts = c(\"pred1km\", \"CapeHorn\"),   among_predpts = TRUE,   overwrite = TRUE )"},{"path":[]},{"path":"/articles/introduction.html","id":"sec:lm","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models","what":"Linear SSN Models","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"begin fitting linear models stream network data using ssn_lm() function. Later fit generalized linear models stream network data using ssn_glm() function. Typically, linear models used response variable (.e., dependent variable) continuous highly skewed, generalized linear models often used response variable binary, count, highly skewed. Linear spatial stream network models quantitative response vector 𝐲\\mathbf{y} spatially dependent random errors often parameterized 𝐗\\mathbf{X} matrix explanatory variables (usually including column 1’s intercept), 𝛃\\boldsymbol{\\beta} vector fixed effects describe average impact 𝐗\\mathbf{X} 𝐲\\mathbf{y}, 𝛕tu\\boldsymbol{\\tau}_{tu} vector spatially dependent (autocorrelated) tail-random errors, 𝛕td\\boldsymbol{\\tau}_{td} vector spatially dependent (autocorrelated) tail-random errors, 𝛕eu\\boldsymbol{\\tau}_{eu} vector spatially dependent (autocorrelated) Euclidean random errors, 𝛜\\boldsymbol{\\epsilon} vector spatially independent (uncorrelated) random errors. spatial dependence 𝛕\\boldsymbol{\\tau} term explicitly specified using spatial covariance function incorporates variance respective 𝛕\\boldsymbol{\\tau} term, often called partial sill, range parameter controls behavior respective spatial covariance. variance 𝛜\\boldsymbol{\\epsilon} often called nugget (nugget effect). Sometimes several unconnected networks modeled simultaneously. two observations separate networks, tail-tail-random errors uncorrelated Euclidean random errors can autocorrelated. Suppose interested studying summer mean temperature (Summer_mn) stream network. can visualize distribution summer mean temperature (overlain onto stream network) running  ssn_lm() function used fit linear spatial stream network models bears many similarities base-R’s lm() function non-spatial linear models. provide commonly used arguments ssn_lm(): formula uses syntax formula argument lm(). ssn.object: .ssn object. tailup_type: tail-covariance, can \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\" (default) taildown_type: tail-covariance, can \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\" (default) euclid_type: Euclidean covariance, can \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\" (default) nugget_type: \"nugget\" (default) \"none\". important note default tailup_type, taildown_type, euclid_type \"none\", means must specified relevant covariances desired. default nugget_type \"nugget\", specifies nugget effect, useful many ecological processes localized variability important capture. Full parameterizations covariance function given ssn_lm()’s documentation, can viewed running help(\"ssn_lm\", \"SSN2\"). different approaches choosing covariance functions. One approach fit several models compare fits using statistics like AIC cross-validation error. Another approach visualize Torgegram() choose functions appropriately. Torgegram() SSN2 essentially semivariogram describes variability streams data based flow-connected, flow-unconnected, Euclidean spatial relationships. Like semivariograms, Torgegram describes semivariance (.e. halved average squared difference) observations changes hydrologic Euclidean distances. strong dependence sites based flow-connected flow-unconnected relationships, semivariance increase respective distance. , however, strong dependence, semivariance relatively flat. Torgegram() output can combined plot() better understand covariance components may suitable model. example, semivariance flow-connected sites increases hydrologic distance semivariance flow-unconnected sites flat, tail-component may sufficient model (.e., tail-component needed). However, model likely benefit tail-component combination tail-tail-models semivariance flow-connected flow-unconnected sites increases distance. Alternatively, semivariance flat, model unlikely benefit tail-tail-components. SSN2 also allows users visualize changes semivariance based Euclidean distance, may provide additional insights whether Euclidean component mixture tail-, tail-/Euclidean models improve model. Please see Zimmerman Ver Hoef (2017) -depth review Toregegrams, along strategies interpreting using inform model fitting. formal comparison models, use statistics like AIC cross-validation error, discuss later. Suppose want model summer mean stream temperature function elevation precipitation. can aid understanding covariance components may informative visualizing Torgegram: first argument Torgegram() formula. Residuals non-spatial linear model specified formula used Toregram visualize remaining spatial dependence. type argument specifies Torgegram types default value c(\"flowcon\", \"flowuncon\") flow-connected flow-unconnected semivariances. also desire visualize Euclidean semivariance. visualize three components running  flow-connected semivariances seem generally increase distance, suggests model benefit least tail-component. takeaway flow-unconnected Euclidean semivariances less clear – seem generally increase distance short distances high semivariances. empirically investigate impact dependence structure next fit model three components: tail-, tail-, Euclidean. fit spatial stream network model regressing summer mean stream temperature elevation watershed-averaged precipitation using exponential tail-covariance function additive weights created using watershed area (afvArea), spherical tail-covariance function, Gaussian Euclidean covariance function, nugget effect running estimation method specified via estmethod argument, default value \"reml\" restricted maximum likelihood (REML). estimation method \"ml\" maximum likelihood (ML). REML chosen default tends yield accurate covariance parameter estimates ML, especially small sample sizes. One nuance REML, however, comparisons likelihood-based statistics like AIC valid models fixed effects structure (.e., formula). compare fixed effects covariance structures simultaneously, use ML model comparison tool likelihood-based, cross validation via loocv(), discuss later.","code":"ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = Summer_mn), size = 2) +   scale_color_viridis_c(limits = c(0, 17), option = \"H\") +   theme_bw() tg <- Torgegram(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   type = c(\"flowcon\", \"flowuncon\", \"euclid\") ) plot(tg) ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\" )"},{"path":"/articles/introduction.html","id":"model-summaries","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models > Linear SSN Models","what":"Model Summaries","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"summarize fitted model running Similar summaries lm() objects, summaries ssn_lm() objects include original function call, residuals, coefficients table fixed effects. (Intercept) represents average summer mean stream temperature sea level (elevation zero) precipitation, ELEV_DEM represents decrease average summer mean stream temperature one unit (meter) increase elevation, AREAWTMAP represents decrease average summer mean stream temperature one unit (mm) increase precipitation. strong evidence average summer mean stream temperature decreases elevation (pp-value <0.001< 0.001), moderate evidence average summer mean stream temperature decreases precipitation (pp-value ≈\\approx 0.05). pseudo r-squared also returned, quantifies proportion variability explained fixed effects. coefficients table covariance parameters describes model’s dependence. larger de parameter, variability process attributed relevant effect. , model’s random variability comes tail-tail-portions model. larger range parameter, autocorrelated nearby observations respect relevant effect. directly compare sources variability model using varcomp function: variability summer mean stream temperature explained fixed effects elevation precipitation (Covariates (PR-sq)), tail-component, tail-component. Note values proportion column sum one. remainder subsection, describe broom (Robinson, Hayes, Couch 2021) functions tidy(), glance() augment(). tidy() tidies coefficient output convenient tibble, glance() glances model-fit statistics, augment() augments data fitted model diagnostics. tidy fixed effects (add confidence intervals) running glance model-fit statistics running columns tibble represent: n: sample size. p: number fixed effects (linearly independent columns 𝐗\\mathbf{X}). npar: number estimated covariance parameters. value: value minimized objective function used fitting model. AIC: Akaike Information Criterion (AIC). AICc: AIC small sample size correction. logLik: log-likelihood. deviance: deviance. pseudo.r.squared: pseudo r-squared. glances() function can used glance multiple models . Suppose wanted compare current model new model omits tail-Euclidean components. using glances() running Often AIC AICc used model selection, balance model fit model simplicity. lower AIC AICc original model (ssn_mod) indicates better fit data (ssn_mod2). Outside glance() glances(), functions AIC(), AICc(), logLik(), deviance(), pseudoR2() available compute relevant statistics. Note additive required tail-covariance specified. able compare AIC AICc models fit using REML changing covariance structure, fixed effects structure. compare AIC AICc models varying fixed effect covariance structures, use ML. example, compare model without elevation assess importance: Elevation seems important model fit, evidenced lower AIC. Erin E. Peterson Ver Hoef (2010) describe two-step model procedure model selection based AIC comparing models varying covariance fixed structures. First, covariance components included (tail-, tail-, Euclidean, nugget) fixed effects compared using ML. using model lowest AIC, refit using REML compare models varying combinations covariance components. Finally, proceed model lowest AIC. Another approach compare suite models (varying fixed effect covariance components) using ML refit best model using REML. Henceforth, proceed REML models, ssn_mod ssn_mod2. Another way compare model fits leave-one-cross validation available via loocv() function. loocv() returns many model-fit statistics. One root-mean-squared-prediction error, captures typical absolute error associated prediction. can compare mean-squared-prediction error ssn_mod, ssn_mod2: ssn_mod better model respect AIC, AICc, RMSPE shortly use return model diagnostics make predictions. Note model comparison using loocv() depend estimation method (ML vs REML). augment data model diagnostics running columns tibble represent: Summer_mn: Summer mean stream temperature. ELEV_DEM: Elevation. AREAWTMAP: Precipitation. .fitted: fitted values (estimated mean given explanatory variable values). .resid: residuals (response minus fitted values). .hat: leverage (hat) values. .cooksd: Cook’s distance. .std.residuals: Standardized residuals. pid: pid value. geometry: spatial information sf object. default, augment() returns variables data used model. variables original data returned setting drop = FALSE. can write augmented data geopackage loading sf (comes installed alongside SSN2) running Many model diagnostics returned augment() can visualized running using plot(). example, plot fitted values standardized residuals running  6 total diagnostic plots (specified via argument) return information returned running plot() lm() object.","code":"summary(ssn_mod) #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_type = \"gaussian\", additive = \"afvArea\") #>  #> Residuals: #>      Min       1Q   Median       3Q      Max  #> -2.73430 -1.43161 -0.04368  0.83251  1.39377  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 78.214857  12.189379   6.417 1.39e-10 *** #> ELEV_DEM    -0.028758   0.005808  -4.952 7.35e-07 *** #> AREAWTMAP   -0.008067   0.004125  -1.955   0.0505 .   #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.4157 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  1.348e+00 #>   tailup exponential         range  8.987e+05 #>   taildown spherical  de (parsill)  2.647e+00 #>   taildown spherical         range  1.960e+05 #>      euclid gaussian  de (parsill)  1.092e-04 #>      euclid gaussian         range  1.805e+05 #>               nugget        nugget  1.660e-02 varcomp(ssn_mod) #> # A tibble: 5 × 2 #>   varcomp            proportion #>   <chr>                   <dbl> #> 1 Covariates (PR-sq)  0.416     #> 2 tailup_de           0.196     #> 3 taildown_de         0.386     #> 4 euclid_de           0.0000159 #> 5 nugget              0.00242 tidy(ssn_mod, conf.int = TRUE) #> # A tibble: 3 × 7 #>   term        estimate std.error statistic  p.value conf.low   conf.high #>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>       <dbl> #> 1 (Intercept) 78.2      12.2          6.42 1.39e-10  54.3    102.        #> 2 AREAWTMAP   -0.00807   0.00413     -1.96 5.05e- 2  -0.0162   0.0000187 #> 3 ELEV_DEM    -0.0288    0.00581     -4.95 7.35e- 7  -0.0401  -0.0174 glance(ssn_mod) #> # A tibble: 1 × 9 #>       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1    45     3     7  76.6  90.6  93.7  -38.3     41.8            0.416 ssn_mod2 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   taildown_type = \"spherical\" ) glances(ssn_mod, ssn_mod2) #> # A tibble: 2 × 10 #>   model        n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <chr>    <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1 ssn_mod     45     3     7  76.6  90.6  93.7  -38.3     41.8            0.416 #> 2 ssn_mod2    45     3     3 130.  136.  137.   -65.1     41.9            0.180 ml_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) ml_mod2 <- ssn_lm(   formula = Summer_mn ~ AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) glances(ml_mod, ml_mod2) #> # A tibble: 2 × 10 #>   model       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <chr>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1 ml_mod     45     3     7  60.2  80.2  86.7  -30.1     45.4           0.461  #> 2 ml_mod2    45     2     7  72.9  90.9  96.0  -36.4     44.9           0.0237 loocv_mod <- loocv(ssn_mod) loocv_mod$RMSPE #> [1] 0.4800386 loocv_mod2 <- loocv(ssn_mod2) loocv_mod2$RMSPE #> [1] 0.8150308 aug_ssn_mod <- augment(ssn_mod) aug_ssn_mod #> Simple feature collection with 45 features and 9 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1530805 ymin: 920324.3 xmax: -1503079 ymax: 931036.6 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic #> # A tibble: 45 × 10 #>    Summer_mn ELEV_DEM AREAWTMAP .fitted  .resid    .hat .cooksd .std.resid pid   #>  *     <dbl>    <int>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>      <dbl> <chr> #>  1      14.9     1947     1001.    14.1  0.770  0.0724  2.74e-3     0.337  1     #>  2      14.7     1952     1001.    14.0  0.714  0.0569  4.49e-5    -0.0486 2     #>  3      14.6     1958     1001.    13.8  0.776  0.0629  2.59e-3     0.352  3     #>  4      15.2     1923     1007.    14.8  0.427  0.125   4.71e-2     1.06   4     #>  5      14.5     1932     1007.    14.5 -0.0437 0.0359  3.43e-2    -1.69   5     #>  6      15.3     1940     1009.    14.3  1.01   0.0220  3.29e-3     0.670  6     #>  7      15.1     1940     1010.    14.3  0.797  0.0178  1.05e-4    -0.133  7     #>  8      14.9     1945     1013.    14.1  0.833  0.00213 8.13e-5    -0.338  8     #>  9      15.0     1948     1025.    13.9  1.06   0.0560  1.82e-4     0.0988 9     #> 10      15.0     1950     1025.    13.9  1.15   0.0471  6.84e-3     0.660  10    #> # ℹ 35 more rows #> # ℹ 1 more variable: geometry <POINT [m]> library(sf) st_write(aug_ssn_mod, paste0(tempdir(), \"/aug_ssn_mod.gpkg\")) plot(ssn_mod, which = 1)"},{"path":"/articles/introduction.html","id":"prediction-kriging","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models > Linear SSN Models","what":"Prediction (Kriging)","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"Commonly goal data analysis make predictions unobserved locations. spatial contexts, prediction often called Kriging. Next make summer mean stream temperature predictions location pred1km data mf04p running augment() previously used augment original data model diagnostics, can also used augment newdata predictions: .fitted represents predictions. Confidence intervals mean response prediction intervals predicted response can obtained specifying interval argument predict() augment(). default, predict() augment() compute 95% intervals, though can changed using level argument. arguments predict() augment() ssn_lm() objects slightly different arguments lm() object – learn run help(\"predict.SSN2\", \"SSN2\") help(\"augment.SSN2\", \"SSN2\"). visualize predictions (overlain onto stream network) running  Previously wrote model diagnostics geopackage. Now write predictions geopackage (recall sf must loaded) running performing prediction SSN2, name newdata must name prediction data set contained ssn.object$preds. newdata omitted value \"\", prediction performed prediction data sets ssn.object. example, makes predictions pred1km CapeHorn (names mf04p$preds). Lastly, observations (obs object) whose response missing (NA), observations removed model fitting moved prediction data set named .missing. predictions can obtained locations. can also predict average value region using block Prediction (instead making point predictions). predict average summer mean temperature throughout Middle Fork stream network running","code":"predict(ssn_mod, newdata = \"pred1km\") aug_preds <- augment(ssn_mod, newdata = \"pred1km\") aug_preds[, \".fitted\"] #> Simple feature collection with 175 features and 1 field #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1530631 ymin: 914920.7 xmax: -1500020 ymax: 933466.4 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic #> # A tibble: 175 × 2 #>    .fitted            geometry #>      <dbl>         <POINT [m]> #>  1    14.7 (-1528406 928161.4) #>  2    14.7 (-1528202 928821.1) #>  3    14.9 (-1528173 929414.9) #>  4    14.4 (-1530218 926538.7) #>  5    14.5 (-1529466 926808.1) #>  6    14.5 (-1520657 929871.1) #>  7    15.0 (-1519866 930025.5) #>  8    14.7 (-1521823 930124.7) #>  9    14.9 (-1523183 930469.7) #> 10    15.2 (-1523860 930665.8) #> # ℹ 165 more rows ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = aug_preds, aes(color = .fitted), size = 2) +   scale_color_viridis_c(limits = c(0, 17), option = \"H\") +   theme_bw() st_write(aug_preds, paste0(tempdir(), \"/aug_preds.gpkg\")) predict(ssn_mod) predict(ssn_mod, newdata = \"all\") predict(ssn_mod, newdata = \"pred1km\", block = TRUE, interval = \"prediction\") #>        fit      lwr      upr #> 1 10.21747 9.317693 11.11725"},{"path":"/articles/introduction.html","id":"sec:advanced","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models > Linear SSN Models","what":"Advanced Features","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"several additional modeling tools available SSN2 discuss next: Fixing parameter values; non-spatial random effects; partition factors. Perhaps want assume particular covariance parameter known. may reasonable information known process desire perform model selection nested models create profile likelihood confidence intervals. Fixing covariance parameters SSN2 accomplished via tailup_initial, taildown_initial, euclid_initial, nugget_initial arguments ssn_lm(). arguments passed appropriate initial value object created using tailup_initial(), taildown_initial(), euclid_initial(), nugget_initial() function, respectively. example, suppose want fix Euclidean dependent error variance parameter (.e., Euclidean partial sill, parsill) 1, forcing component moderate effect covariance. First, specify appropriate object running euclid_init output shows de parameter initial value 1 assumed known. range parameter still estimated. Next model fit: Random effects can added SSN model incorporate additional sources variability separate stream network. Common additional sources variability modeled include repeated observations sites network-specific effects. random effects modeled using similar syntax random effects nlme (Pinheiro Bates 2006) lme4 (Bates et al. 2015) R packages, specified via formula passed random argument. model random intercepts two networks data running random = ~ .factor(netID) short-hand random = ~ (1 | .factor(netID)), familiar lme4 nlme syntax. partition factor variable allows observations uncorrelated share value. example, one may want partition model two networks (despite adjacency) significant land mass (similar obstruction). case, may relevant assume observations upstream land mass correlated observations downstream land mass. Incorporating effect partition factor different incorporating effect fixed (.e., right-hand side formula argument). Partition factors affect covariance portion model fixed effects affect mean portion. Partition factors modeled using formula contains single variable passed partition_factor argument: , partition factor ensures observations separate stream networks completely uncorrelated.","code":"euclid_init <- euclid_initial(\"gaussian\", de = 1, known = \"de\") euclid_init #> $initial #> de  #>  1  #>  #> $is_known #>   de  #> TRUE  #>  #> attr(,\"class\") #> [1] \"euclid_gaussian\" ssn_init <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_initial = euclid_init,   additive = \"afvArea\" ) ssn_init #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_initial = euclid_init, additive = \"afvArea\") #>  #>  #> Coefficients (fixed): #> (Intercept)     ELEV_DEM    AREAWTMAP   #>   71.247739    -0.026707    -0.005522   #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  1.250e+00 #>   tailup exponential         range  4.191e+05 #>   taildown spherical  de (parsill)  1.924e-02 #>   taildown spherical         range  3.125e+04 #>      euclid gaussian  de (parsill)  1.000e+00 #>      euclid gaussian         range  9.998e+03 #>               nugget        nugget  3.578e-02 ssn_rand <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   random = ~ as.factor(netID) ) ssn_rand #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_type = \"gaussian\", additive = \"afvArea\", random = ~as.factor(netID)) #>  #>  #> Coefficients (fixed): #> (Intercept)     ELEV_DEM    AREAWTMAP   #>   59.579720    -0.020328    -0.006484   #>  #> Coefficients (covariance): #>               Effect             Parameter   Estimate #>   tailup exponential          de (parsill)  1.128e+00 #>   tailup exponential                 range  2.015e+05 #>   taildown spherical          de (parsill)  2.948e-03 #>   taildown spherical                 range  8.357e+04 #>      euclid gaussian          de (parsill)  1.397e+00 #>      euclid gaussian                 range  1.096e+04 #>               nugget                nugget  2.977e-02 #>               random  1 | as.factor(netID)  4.370e+00 ssn_part <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   partition_factor = ~ as.factor(netID) ) ssn_part #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     tailup_type = \"exponential\", taildown_type = \"spherical\",  #>     euclid_type = \"gaussian\", additive = \"afvArea\", partition_factor = ~as.factor(netID)) #>  #>  #> Coefficients (fixed): #> (Intercept)     ELEV_DEM    AREAWTMAP   #>   77.421289    -0.028431    -0.007938   #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  1.331e+00 #>   tailup exponential         range  9.689e+05 #>   taildown spherical  de (parsill)  2.923e+00 #>   taildown spherical         range  1.880e+05 #>      euclid gaussian  de (parsill)  1.654e-04 #>      euclid gaussian         range  2.458e+01 #>               nugget        nugget  1.487e-02"},{"path":"/articles/introduction.html","id":"sec:glm","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models","what":"Generalized Linear SSN Models","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"Generalized linear spatial stream network models response vector 𝐲\\mathbf{y} spatially dependent random errors often parameterized 𝛍\\boldsymbol{\\mu} mean 𝐲\\mathbf{y}, g(⋅)g(\\cdot) link function “links” 𝛍\\boldsymbol{\\mu} linear function predictor variables random errors, terms defined linear spatial stream network models. Rather assuming yy normally (Gaussian) distributed often case linear spatial stream network models, generalized linear spatial stream network models assume 𝐲\\mathbf{y} follows one many distributions corresponding link function. summarize families generalized linear spatial stream network models supported SSN2 link functions, type data typically associated families. generalized linear models generally, see McCullagh Nelder (1989), Myers et al. (2012), Faraway (2016). ssn_glm() function used fit generalized linear spatial stream network models bears many similarities base-R’s glm() function non-spatial generalized linear models. family (.e., response distribution) controlled family argument. family Gaussian(), model fit equivalent one fit using ssn_lm(). Note parameters estimated relevant link scale interpreted accordingly. C16 variable mf04p represents number times daily summer stream temperature exceeded 16 Celsius:  Suppose want model C16 function elevation precipitation. Often count data modeled using Poisson regression. Using tail-, tail-, nugget components, fit Poisson model running previous SSN2 functions used explore linear spatial stream network models also available generalized linear spatial stream network models. example, can summarize model using summary(): Similar summaries glm() objects, summaries ssn_glm() objects include original function call, deviance residuals, coefficients table fixed effects. (Intercept) represents log average C16 sea level (elevation zero) zero precipitation, ELEV_DEM represents decrease log average summer mean temperature one unit (meter) increase elevation, AREAWTMAP represents decrease log average summer mean temperature one unit (mm) increase precipitation. strong evidence log average summer mean temperature decreases elevation (pp-value <0.001< 0.001), moderate evidence log average summer mean temperature decreases precipitation (pp-value ≈\\approx 0.03). Recall covariance parameter estimates link (, log) scale. Poisson model assumes observations mean variance equal. Often ecological environmental data, variance larger mean – called overdispersion. negative binomial model accommodates overdispersion count data. fit negative binomial model running can compare fit models using leave-one-cross validation running negative binomial model lower RMSPE, suggests evidence overdispersion. ways assess impact overdispersion include 1) comparing models using likelihood-based fit statistic like AIC; AIC values similar little evidence overdispersion – 2) inspecting dispersion parameter estimates negative binomial model; dispersion parameter large, little evidence overdispersion. advanced modeling features discussed linear spatial stream network models (e.g., fixing covariance parameter values, random effects, partition factors) also available generalized linear spatial stream network models.","code":"ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = C16), size = 2) +   scale_color_viridis_c(option = \"H\") +   theme_bw() ssn_pois <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"poisson\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) summary(ssn_pois) #>  #> Call: #> ssn_glm(formula = C16 ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,  #>     family = \"poisson\", tailup_type = \"epa\", taildown_type = \"mariah\",  #>     additive = \"afvArea\") #>  #> Deviance Residuals: #>      Min       1Q   Median       3Q      Max  #> -3.10387 -0.36874 -0.05467  0.27401  1.37106  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 45.889749   9.803696   4.681 2.86e-06 *** #> ELEV_DEM    -0.018206   0.004547  -4.004 6.24e-05 *** #> AREAWTMAP   -0.006715   0.002958  -2.270   0.0232 *   #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.2527 #>  #> Coefficients (covariance): #>            Effect     Parameter   Estimate #>        tailup epa  de (parsill)  6.358e-01 #>        tailup epa         range  3.090e+04 #>   taildown mariah  de (parsill)  7.500e-02 #>   taildown mariah         range  2.878e+06 #>            nugget        nugget  1.289e-03 #>        dispersion    dispersion  1.000e+00 ssn_nb <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"nbinomial\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) loocv_pois <- loocv(ssn_pois) loocv_pois$RMSPE #> [1] 6.015313 loocv_nb <- loocv(ssn_nb) loocv_nb$RMSPE #> [1] 5.875028"},{"path":"/articles/introduction.html","id":"simulating-spatial-stream-network-data","dir":"Articles","previous_headings":"Spatial Stream Network (SSN) Models","what":"Simulating Spatial Stream Network Data","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"ssn_simulate() function used simulate data stream network. First, covariance parameter values specified seed set: call ssn_simulate(), specifying family argument depending type simulated data desired (, Gaussian), ssn.object network (, observed network): simulate binomial (presence/absence) data running Currently, ssn_simulate() works observed network (network = \"obs\"). However, simulation SSN2 focus future updates, plan add support simulating prediction networks well observed prediction networks simultaneously.","code":"tu_params <- tailup_params(\"exponential\", de = 0.4, range = 1e5) td_params <- taildown_params(\"spherical\", de = 0.1, range = 1e6) euc_params <- euclid_params(\"gaussian\", de = 0.2, range = 1e3) nug_params <- nugget_params(\"nugget\", nugget = 0.1) set.seed(2) sims <- ssn_simulate(   family = \"gaussian\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 1 ) head(sims) #> [1] -0.8022248 -0.4047795  0.3819883 -1.0798647 -0.8943244 -0.4111767 sims <- ssn_simulate(   family = \"binomial\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 2 ) head(sims) #>      1 2 #> [1,] 0 1 #> [2,] 0 1 #> [3,] 0 1 #> [4,] 1 1 #> [5,] 1 0 #> [6,] 0 0"},{"path":"/articles/introduction.html","id":"function-glossary","dir":"Articles","previous_headings":"","what":"Function Glossary","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"list two SSN2 functions used fit models: ssn_glm(): Fit spatial stream network generalized linear model. ssn_lm(): Fit spatial stream network linear model. list commonly used SSN2 functions operate model fits: AIC(): Compute AIC. AICc(): Compute AICc. anova(): Perform analysis variance. augment(): Augment data diagnostics new data predictions. coef(): Return coefficients. confint(): Compute confidence intervals. cooks.distance(): Compute Cook’s distance. covmatrix(): Return covariance matrices. deviance(): Compute deviance. fitted(): Compute fitted values. glance(): Glance fitted model. glances(): Glance multiple fitted models. hatvalues(): Compute leverage (hat) values. logLik(): Compute log-likelihood. loocv(): Perform leave-one-cross validation compute relevant statistics. model.matrix(): Return model matrix (𝐗\\mathbf{X}). plot(): Create fitted model plots. predict(): Compute predictions prediction intervals. pseudoR2(): Compute pseudo r-squared. residuals(): Compute residuals. summary(): Summarize fitted models. tidy(): Tidy fitted models. varcomp(): Compare variance components. vcov(): Compute variance-covariance matrices estimated parameters. Documentation functions can found running ?function_name.SSN2 help(\"function_name.SSN2\", \"SSN2\"). example, ?predict.SSN2 help(\"predict.SSN2\", \"SSN2\"). list commonly used SSN2 functions manipulating SSN objects: ssn_create_distmat(): Create distance matrices .ssn directory use modeling functions. ssn_get_data(): Extract sf data.frame observed prediction locations SSN object. ssn_get_netgeom(): Extract topological information netgeom column. ssn_get_stream_distmat(): Extract stream distance matrices observed prediction locations SSN object. ssn_import(): Import SSN object .ssn directory. ssn_import_predpts(): Import prediction data store within existing SSN object. ssn_put_data(): Replace sf data.frame observed prediction locations SSN object. ssn_split_predpts(): Split prediction data stored within SSN object multiple prediction data sets. ssn_subset(): Subset existing SSN object based logical expression. SSN_to_SSN2(): Convert S4 SpatialStreamNetwork object created SSN S3 SSN object used SSN2. ssn_update_path(): Update path element SSN object. ssn_write(): Write SSN project new local .ssn directory. functions manipulate SSN objects ssn_ prefix, makes easily accessible via tab completion RStudio. list commonly used miscellaneous SSN2 functions: ssn_simulate(): Simulate spatially autocorrelated random variables stream network. full list SSN2 functions alongside documentation, see documentation manual.","code":""},{"path":"/articles/introduction.html","id":"from-ssn-to-ssn2","dir":"Articles","previous_headings":"","what":"From SSN to SSN2","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"present table SSN functions provide relevant successors SSN2: addition function name changes , function argument names also changed. Please read documentation function interest see relevant argument name changes.","code":""},{"path":"/articles/introduction.html","id":"the-future-of-ssn2","dir":"Articles","previous_headings":"","what":"The Future of SSN2","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"several features planned future versions SSN2 make initial release due October timeline regarding rgdal, rgeos, maptools retirements. , plan regularly update add features SSN2 coming years – check back often! features include additional tools large data sets (model fitting prediction), manipulating .ssn object, simulating data, . best make future versions SSN2 backward compatible version, minor changes may occur ready release version 1.0.0.","code":""},{"path":"/articles/introduction.html","id":"r-code-appendix","dir":"Articles","previous_headings":"","what":"R Code Appendix","title":"An Introduction to Spatial Stream Network Modeling in R Using SSN2","text":"","code":"library(SSN2) citation(package = \"SSN2\") path <- system.file(\"lsndata/MiddleFork04.ssn\", package = \"SSN2\") copy_lsn_to_temp() path <- file.path(tempdir(), \"MiddleFork04.ssn\") mf04p <- ssn_import(   path = path,   predpts = c(\"pred1km\", \"CapeHorn\"),   overwrite = TRUE ) summary(mf04p) library(ggplot2) names(mf04p$preds) ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$preds$pred1km, pch = 17, color = \"blue\") +   geom_sf(data = mf04p$obs, color = \"brown\", size = 2) +   theme_bw() ssn_create_distmat(   ssn.object = mf04p,   predpts = c(\"pred1km\", \"CapeHorn\"),   among_predpts = TRUE,   overwrite = TRUE ) ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = Summer_mn), size = 2) +   scale_color_viridis_c(limits = c(0, 17), option = \"H\") +   theme_bw() tg <- Torgegram(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   type = c(\"flowcon\", \"flowuncon\", \"euclid\") ) plot(tg) ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\" ) summary(ssn_mod) varcomp(ssn_mod) tidy(ssn_mod, conf.int = TRUE) glance(ssn_mod) ssn_mod2 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   taildown_type = \"spherical\" ) glances(ssn_mod, ssn_mod2) ml_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) ml_mod2 <- ssn_lm(   formula = Summer_mn ~ AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   estmethod = \"ml\" ) glances(ml_mod, ml_mod2) loocv_mod <- loocv(ssn_mod) loocv_mod$RMSPE loocv_mod2 <- loocv(ssn_mod2) loocv_mod2$RMSPE aug_ssn_mod <- augment(ssn_mod) aug_ssn_mod library(sf) st_write(aug_ssn_mod, paste0(tempdir(), \"/aug_ssn_mod.gpkg\")) plot(ssn_mod, which = 1) predict(ssn_mod, newdata = \"pred1km\") aug_preds <- augment(ssn_mod, newdata = \"pred1km\") aug_preds[, \".fitted\"] ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = aug_preds, aes(color = .fitted), size = 2) +   scale_color_viridis_c(limits = c(0, 17), option = \"H\") +   theme_bw() st_write(aug_preds, paste0(tempdir(), \"/aug_preds.gpkg\")) predict(ssn_mod) predict(ssn_mod, newdata = \"all\") predict(ssn_mod, newdata = \"pred1km\", block = TRUE, interval = \"prediction\") euclid_init <- euclid_initial(\"gaussian\", de = 1, known = \"de\") euclid_init ssn_init <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_initial = euclid_init,   additive = \"afvArea\" ) ssn_init ssn_rand <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   random = ~ as.factor(netID) ) ssn_rand ssn_part <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\",   partition_factor = ~ as.factor(netID) ) ssn_part ggplot() +   geom_sf(data = mf04p$edges) +   geom_sf(data = mf04p$obs, aes(color = C16), size = 2) +   scale_color_viridis_c(option = \"H\") +   theme_bw() ssn_pois <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"poisson\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) summary(ssn_pois) ssn_nb <- ssn_glm(   formula = C16 ~ ELEV_DEM + AREAWTMAP,   family = \"nbinomial\",   ssn.object = mf04p,   tailup_type = \"epa\",   taildown_type = \"mariah\",   additive = \"afvArea\" ) loocv_pois <- loocv(ssn_pois) loocv_pois$RMSPE loocv_nb <- loocv(ssn_nb) loocv_nb$RMSPE tu_params <- tailup_params(\"exponential\", de = 0.4, range = 1e5) td_params <- taildown_params(\"spherical\", de = 0.1, range = 1e6) euc_params <- euclid_params(\"gaussian\", de = 0.2, range = 1e3) nug_params <- nugget_params(\"nugget\", nugget = 0.1) set.seed(2) sims <- ssn_simulate(   family = \"gaussian\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 1 ) head(sims) sims <- ssn_simulate(   family = \"binomial\",   ssn.object = mf04p,   network = \"obs\",   additive = \"afvArea\",   tailup_params = tu_params,   taildown_params = td_params,   euclid_params = euc_params,   nugget_params = nug_params,   mean = 0,   samples = 2 ) head(sims)"},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michael Dumelle. Author, maintainer. Jay M. Ver Hoef. Author. Erin Peterson. Author. Alan Pearse. Contributor. Dan Isaak. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Dumelle M, Peterson, E, Ver Hoef JM, Pearse , Isaak D (2023). SSN2: Spatial Modeling Stream Networks R. R package version 0.2.0","code":"@Manual{,   title = {{SSN2}: Spatial Modeling on Stream Networks in {R}},   author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak},   year = {2023},   note = {{R} package version 0.2.0}, }"},{"path":"/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to SSN2 development","title":"Contributing to SSN2 development","text":"outlines propose change SSN2. two main approaches: Create GitHub issue identifying bug suggesting feature. Contribute code change via GitHub pull request. prefer use GitHub, please send email Dumelle.Michael@epa.gov.","code":""},{"path":"/CONTRIBUTING.html","id":"github-issue","dir":"","previous_headings":"","what":"GitHub Issue","title":"Contributing to SSN2 development","text":"may create GitHub issue identifying bug suggesting feature. may access SSN2’s GitHub issues webpage visiting https://github.com/USEPA/SSN2/issues. Please illustrate bug feature using minimal reproducible example, reprex, linked . See tidyverse guide create great issue linked advice.","code":""},{"path":"/CONTRIBUTING.html","id":"github-pull-request","dir":"","previous_headings":"","what":"GitHub Pull Request","title":"Contributing to SSN2 development","text":"contribute change SSN2, please use pull request. learn creating GitHub pull request, please see GitHub guide linked .","code":""},{"path":"/index.html","id":"ssn2-spatial-modeling-on-stream-networks","dir":"","previous_headings":"","what":"Spatial Modeling on Stream Networks","title":"Spatial Modeling on Stream Networks","text":"SSN2 R package spatial statistical modeling prediction stream networks, including models based -stream distance. Models created using moving average constructions. Spatial linear models, including explanatory variables, can fit (restricted) maximum likelihood. Mapping graphical functions included. successor SSN R package. See SSN2 website : https://usepa.github.io/SSN2/.","code":""},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Spatial Modeling on Stream Networks","text":"use SSN2 formal publication report, please cite . Citing SSN2 lets us devote resources future. View SSN2 citation running","code":"citation(package = \"SSN2\") #> #> To cite SSN2 in publications use: #> #>   Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023). SSN2: Spatial Modeling on Stream Networks in R. R package version 0.2.0 #> #> A BibTeX entry for LaTeX users is #> #>   @Manual{, #>     title = {{SSN2}: Spatial Modeling on Stream Networks in {R}}, #>     author = {Michael Dumelle and Erin Peterson and Jay M. {Ver Hoef} and Alan Pearse and Dan Isaak}, #>     year = {2023}, #>     note = {{R} package version 0.2.0}, #>   }"},{"path":"/index.html","id":"statement-of-need","dir":"","previous_headings":"","what":"Statement of Need","title":"Spatial Modeling on Stream Networks","text":"Streams provide vital aquatic services sustain wildlife, provide drinking irrigation water, support recreational cultural activities. Data often collected various locations stream network used characterize scientific phenomenon stream. Spatial stream network (SSN) models use spatial statistical modeling framework describe unique complex dependencies stream network resulting branching network structure, directional water flow, differences flow volume. SSN models relate response variable one explanatory variables, spatially independent error term (.e., nugget), three spatially dependent error terms: tail-errors, tail-errors, Euclidean errors. Tail-errors restrict spatial dependence flow-connected sites (.e., water flows upstream downstream site) incorporate spatial weights (.e., additive function) describe branching network . Tail-errors describe spatial dependence flow-connected flow-unconnected (.e., sites share common downstream junction flow) sites, spatial weights required. Euclidean errors describe spatial dependence sites based Euclidean distance governed factors confined stream network like regional geology. SSN2 R package designed help users fit SSN models stream network data.","code":""},{"path":"/index.html","id":"installation-instructions","dir":"","previous_headings":"","what":"Installation Instructions","title":"Spatial Modeling on Stream Networks","text":"Install load recent approved version CRAN running Install load recent version ofSSN2 GitHub running Install load recent development version ofSSN2 GitHub running","code":"# install the most recent approved version from CRAN install.packages(\"SSN2\") # load the most recent approved version from CRAN library(SSN2) # Installing from GitHub requires you first install the remotes package install.packages(\"remotes\")  # install the most recent version from GitHub remotes::install_github(\"USEPA/SSN2\", ref = \"main\") # load the most recent development version from GitHub library(SSN2) # Installing from GitHub requires you first install the remotes package install.packages(\"remotes\")  # install the most recent version from GitHub remotes::install_github(\"USEPA/SSN2\", ref = \"develop\") # load the most recent development version from GitHub library(SSN2)"},{"path":"/index.html","id":"contributing-to-ssn2","dir":"","previous_headings":"","what":"Contributing to SSN2","title":"Spatial Modeling on Stream Networks","text":"encourage users report bugs /contribute SSN2. detail , please see contributing guide (CONTRIBUTING.md).","code":""},{"path":"/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"Spatial Modeling on Stream Networks","text":"several ways get help SSN2: Open GitHub issue link . Email SSN support team (support@spatialstreamnetworks.com Dumelle.Michael@epa.gov) Post support website like Stack Overflow Cross Validated.","code":""},{"path":"/index.html","id":"example-usage","dir":"","previous_headings":"","what":"Example Usage","title":"Spatial Modeling on Stream Networks","text":"provide brief example showing use SSN2. thorough introduction software, see introductory vignette linked . list functions available SSN2, see function reference linked . load SSN2, copy .ssn object comes SSN2 temporary directory, create stream distance matrices used modeling running fit summarize SSN model explaining summer water temperatue (Summer_mn) function elevation (ELEV_DEM) precipitation (AREAWTMAP) exponential, spherical, Gaussian structures tail-, tail-, Euclidean errors, respectively, running tidy, glance , augment (diagnostics) fitted model running make predictions prediction sites (pred1km) running predict() (augment():","code":"library(SSN2) copy_lsn_to_temp() path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(path, predpts = \"pred1km\") ssn_create_distmat(mf04p, predpts = \"pred1km\", overwrite = TRUE) ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,   ssn.object = mf04p,   tailup_type = \"exponential\",   taildown_type = \"spherical\",   euclid_type = \"gaussian\",   additive = \"afvArea\" ) summary(ssn_mod) #> #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p, #>     tailup_type = \"exponential\", taildown_type = \"spherical\", #>     euclid_type = \"gaussian\", additive = \"afvArea\") #> #> Residuals: #>     Min      1Q  Median      3Q     Max #> -3.6393 -2.0646 -0.5952  0.2143  0.7497 #> #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|) #> (Intercept) 76.195041   7.871574   9.680  < 2e-16 *** #> ELEV_DEM    -0.026905   0.003646  -7.379  1.6e-13 *** #> AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 * #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Pseudo R-squared: 0.6124 #> #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  3.800e+00 #>   tailup exponential         range  4.194e+06 #>   taildown spherical  de (parsill)  4.480e-01 #>   taildown spherical         range  1.647e+05 #>      euclid gaussian  de (parsill)  1.509e-02 #>      euclid gaussian         range  4.496e+03 #> tidy(ssn_mod, conf.int = TRUE) #> # A tibble: 3 × 7 #>   term        estimate std.error statistic  p.value conf.low conf.high #>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl> #> 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6 #> 2 AREAWTMAP   -0.00910   0.00446     -2.04 4.14e- 2  -0.0178 -0.000356 #> 3 ELEV_DEM    -0.0269    0.00365     -7.38 1.60e-13  -0.0341 -0.0198 glance(ssn_mod) #> # A tibble: 1 × 9 #>       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1    45     3     7  59.3  73.3  76.3  -29.6     41.9            0.612 head(augment(ssn_mod)) #> Simple feature collection with 6 features and 9 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1515032 ymin: 2529461 xmax: -1512690 ymax: 2531883 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version #> # A tibble: 6 × 10 #>   Summer_mn ELEV_DEM AREAWTMAP .fitted .resid   .hat  .cooksd .std.resid pid #>       <dbl>    <int>     <dbl>   <dbl>  <dbl>  <dbl>    <dbl>      <dbl> <chr> #> 1     11.4      1977      940.    14.4  -3.07 0.0915 0.0962       -1.78  1 #> 2     10.7      1984     1087.    12.9  -2.20 0.114  0.00471      -0.352 2 #> 3     10.4      1993     1087.    12.7  -2.25 0.0372 0.00724      -0.764 3 #> 4     10.1      2007     1087.    12.3  -2.18 0.0251 0.00153      -0.427 4 #> 5     10.1      2009     1087.    12.3  -2.13 0.0374 0.000583     -0.216 5 #> 6      9.81     2012     1109.    12.0  -2.16 0.0602 0.0150       -0.863 6 #> # ℹ 1 more variable: geometry <POINT [m]> preds <- predict(ssn_mod, newdata = \"pred1km\", interval = \"prediction\") head(preds) #>        fit      lwr      upr #> 1 14.64383 14.27138 15.01627 #> 2 15.00608 14.65017 15.36198 #> 3 14.79235 14.27414 15.31057 #> 4 14.96884 14.45492 15.48276 #> 5 15.15182 14.73770 15.56595 #> 6 15.12783 14.76358 15.49208"},{"path":"/index.html","id":"imported-packages","dir":"","previous_headings":"","what":"Imported Packages","title":"Spatial Modeling on Stream Networks","text":"SSN2 imports following R packages: generics: exporting generic functions. graphics: visualizations (e.g., plot()). Matrix: efficient matrix manipulations. RSQlite: various functions read write (e.g., ssn_create_distmat()). sf: handling spatial data. spmodel: various modeling functions (e.g., randcov_initial()) generic functions (e.g., loocv()). stats: various modeling functions (e.g., confint()). tibble: creating tibbles output various functions (e.g., tidy()). utils: various utility functions. withr: path handling reading writing.","code":""},{"path":"/index.html","id":"suggested-packages","dir":"","previous_headings":"","what":"Suggested Packages","title":"Spatial Modeling on Stream Networks","text":"SSN2 suggests following R packages: ggplot2: vignette visualizations. knitr: vignette building. rmarkdown: vignette building. sp: making SSN objects SSN R package compatible SSN2. statmod: modeling simulation inverse Gaussian data. testthat: unit testing.","code":""},{"path":"/index.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Spatial Modeling on Stream Networks","text":"project licensed GNU General Public License, GPL-3.","code":""},{"path":"/index.html","id":"epa-disclaimer","dir":"","previous_headings":"","what":"EPA Disclaimer","title":"Spatial Modeling on Stream Networks","text":"United States Environmental Protection Agency (EPA) GitHub project code provided “” basis user assumes responsibility use. 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GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.>"},{"path":"/reference/AIC.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute AIC and AICc of fitted model objects — AIC.SSN2","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"Compute AIC AICc one several fitted model objects log-likelihood value can obtained.","code":""},{"path":"/reference/AIC.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"","code":"# S3 method for class 'ssn_lm' AIC(object, ..., k = 2)  # S3 method for class 'ssn_glm' AIC(object, ..., k = 2)  # S3 method for class 'ssn_lm' AICc(object, ..., k = 2)  # S3 method for class 'ssn_glm' AICc(object, ..., k = 2)"},{"path":"/reference/AIC.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... Optionally fitted model objects. k penalty parameter, taken 2. Currently allowed differ 2 (needed generic consistency).","code":""},{"path":"/reference/AIC.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"just one object provided, numeric value corresponding AIC AICc. multiple objects provided, data.frame rows corresponding objects columns representing number parameters estimated (df) AIC AICc.","code":""},{"path":"/reference/AIC.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"comparing models fit maximum restricted maximum likelihood, smaller AIC AICc, better fit. AICc contains correction AIC small sample sizes. AIC AICc comparisons \"ml\" \"reml\" models meaningless – comparisons made within set models estimated using \"ml\" set models estimated using \"reml\". AIC AICc comparisons \"reml\" must use fixed effects. vary covariance parameters fixed effects simultaneously, use \"ml\". AIC defined \\(-2loglik + 2(npar)\\) AICc defined \\(-2loglik + 2n(npar) / (n - npar - 1)\\), \\(n\\) sample size \\(npar\\) number estimated parameters. \"ml\", \\(npar\\) number estimated covariance parameters plus number estimated fixed effects. \"reml\", \\(npar\\) number estimated covariance parameters.","code":""},{"path":"/reference/AIC.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute AIC and AICc of fitted model objects — AIC.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) AIC(ssn_mod) #> [1] 82.8932 AICc(ssn_mod) #> [1] 83.47857"},{"path":"/reference/anova.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"Compute analysis variance tables fitted model object likelihood ratio test two fitted model objects.","code":""},{"path":"/reference/anova.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"","code":"# S3 method for class 'ssn_lm' anova(object, ..., test = TRUE, Terms, L)  # S3 method for class 'ssn_glm' anova(object, ..., test = TRUE, Terms, L)  # S3 method for class 'anova.ssn_lm' tidy(x, ...)  # S3 method for class 'anova.ssn_glm' tidy(x, ...)"},{"path":"/reference/anova.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... additional fitted model object ssn_lm() ssn_glm() (anova()). test logical value indicating whether p-values asymptotic Chi-squared hypothesis tests returned. Defaults TRUE. Terms optional character integer vector specifies terms model used jointly compute test statistics p-values (test = TRUE) null hypothesis zero. Terms used single fitted model object passed function. Terms character vector, contain names fixed effect terms. Terms integer vector, correspond order (starting one) names fixed effect terms. easiest way obtain names possible terms run tidy(anova(object))$effects (integer representation matches positions vector). L optional numeric matrix list specifying linear combinations coefficients model used compute test statistics p-values (test = TRUE) coefficient constraints corresponding null hypothesis zero. L used single fitted model object passed function. L numeric matrix, rows indicate coefficient constraints columns represent coefficients. single hypothesis test conducted null hypothesis zero. L list, list element numeric matrix specified . separate hypothesis tests conducted. easiest way obtain possible coefficients run tidy(object)$term. x object anova(object).","code":""},{"path":"/reference/anova.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"one fitted model object present, anova() returns data frame degrees freedom (Df), test statistics (Chi2), p-values (Pr(>Chi2) test = TRUE) corresponding asymptotic Chi-squared hypothesis tests model term. two fitted model objects present, anova() returns data frame difference degrees freedom full reduced model (Df), test statistic (Chi2), p-value corresponding likelihood ratio test (Pr(>Chi2) test = TRUE). Whether one two fitted model objects provided, tidy() can used obtain tidy tibbles anova(object) output.","code":""},{"path":"/reference/anova.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"one fitted model object present, anova() performs general linear hypothesis test corresponding hypothesis specified matrix constraints. Terms L specified, model term tested zero (correspond type III marginal hypothesis tests classical ANOVA). Terms specified L specified, terms tested jointly zero. L specified, linear combinations terms specified L jointly tested zero. two fitted model objects present, one must \"reduced\" model nested \"full\" model. anova() performs likelihood ratio test.","code":""},{"path":"/reference/anova.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) anova(ssn_mod) #> Analysis of Variance Table #>  #> Response: Summer_mn #>             Df   Chi2 Pr(>Chi2)     #> (Intercept)  1 84.156 < 2.2e-16 *** #> ELEV_DEM     1 60.799 6.322e-15 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 tidy(anova(ssn_mod)) #> # A tibble: 2 × 4 #>   effects        df statistic  p.value #>   <chr>       <int>     <dbl>    <dbl> #> 1 (Intercept)     1      84.2 4.57e-20 #> 2 ELEV_DEM        1      60.8 6.32e-15"},{"path":"/reference/augment.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment data with information from fitted model objects — augment.SSN2","title":"Augment data with information from fitted model objects — augment.SSN2","text":"Augment accepts fitted model object data set adds information observation data set. New columns always begin . prefix avoid overwriting columns original data set. Augment behaves differently depending whether original data new data requires augmenting. Typically, augmenting original data, fitted model object specified, augmenting new data, fitted model object newdata specified. augmenting original data, diagnostic statistics augmented row data set. augmenting new data, predictions optional intervals (confidence prediction) standard errors augmented row new data set.","code":""},{"path":"/reference/augment.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment data with information from fitted model objects — augment.SSN2","text":"","code":"# S3 method for class 'ssn_lm' augment(   x,   drop = TRUE,   newdata = NULL,   se_fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   level = 0.95,   ... )  # S3 method for class 'ssn_glm' augment(   x,   drop = TRUE,   newdata = NULL,   type = c(\"link\", \"response\"),   se_fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   newdata_size,   level = 0.95,   var_correct = TRUE,   ... )"},{"path":"/reference/augment.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment data with information from fitted model objects — augment.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). drop logical indicating whether drop extra variables fitted model object x augmenting. default drop TRUE. drop ignored augmenting newdata. newdata vector contains names prediction sf objects original ssn.object requiring prediction. original explanatory variables used create fitted model object x must present prediction sf object represented newdata. Defaults NULL, indicates nothing passed newdata augmenting occurs original data. value \"ssn\" shorthand specifying prediction sf objects. se_fit Logical indicating whether .se.fit column added augmented output. Passed predict() defaults FALSE. interval Character indicating type confidence interval columns add augmented newdata output. Passed predict() defaults \"none\". level Tolerance/confidence level. default 0.95. ... Additional arguments predict() augmenting newdata. type scale (response link) predictions obtained using ssn_glm objects. newdata_size size value observation newdata used predicting binomial family. var_correct logical indicating whether return corrected prediction variances predicting via models fit using ssn_glm. default TRUE.","code":""},{"path":"/reference/augment.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Augment data with information from fitted model objects — augment.SSN2","text":"augmenting original data set, tibble additional columns .fitted: Fitted value .resid: Response residual (difference observed fitted values) .hat: Leverage (diagonal hat matrix) .cooksd: Cook's distance .std.resid: Standardized residuals .se.fit: Standard error fitted value. augmenting new data set, tibble additional columns .fitted: Predicted (fitted) value .lower: Lower bound interval .upper: Upper bound interval .se.fit: Standard error predicted (fitted) value predictions prediction objects desired, output list element name matches prediction objects values predictions.","code":""},{"path":"/reference/augment.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augment data with information from fitted model objects — augment.SSN2","text":"augment() returns tibble sf object. Missing response values original data can augmented newdata object providing \".missing\" newdata argument.","code":""},{"path":[]},{"path":"/reference/augment.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augment data with information from fitted model objects — augment.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"CapeHorn\", overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) augment(ssn_mod) #> Simple feature collection with 45 features and 8 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1530805 ymin: 920324.3 xmax: -1503079 ymax: 931036.6 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic #> # A tibble: 45 × 9 #>    Summer_mn ELEV_DEM .fitted  .resid    .hat   .cooksd .std.resid pid   #>  *     <dbl>    <int>   <dbl>   <dbl>   <dbl>     <dbl>      <dbl> <chr> #>  1      14.9     1947    14.4  0.503  0.111   0.0165         0.545 1     #>  2      14.7     1952    14.2  0.473  0.0557  0.00173        0.249 2     #>  3      14.6     1958    14.0  0.568  0.0337  0.00658        0.625 3     #>  4      15.2     1923    15.2 -0.0164 0.0744  0.00893        0.490 4     #>  5      14.5     1932    14.9 -0.439  0.0202  0.0158        -1.25  5     #>  6      15.3     1940    14.7  0.634  0.00569 0.000970       0.584 6     #>  7      15.1     1940    14.7  0.414  0.00162 0.0000507     -0.250 7     #>  8      14.9     1945    14.5  0.454  0.0574  0.0143        -0.706 8     #>  9      15.0     1948    14.4  0.607  0.0739  0.00666        0.425 9     #> 10      15.0     1950    14.3  0.705  0.0581  0.0196         0.821 10    #> # ℹ 35 more rows #> # ℹ 1 more variable: geometry <POINT [m]> augment(ssn_mod, newdata = \"CapeHorn\") #> Simple feature collection with 654 features and 19 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1516634 ymin: 921030.2 xmax: -1512722 ymax: 924632.2 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic #> # A tibble: 654 × 20 #>      rid   pid    COMID AREAWTMAP   SLOPE ELEV_DEM FlowCMS AirMEANc AirMWMTc #>  * <int> <int>    <int>     <dbl>   <dbl>    <int>   <dbl>    <dbl>    <dbl> #>  1    34  1494 23519461     1087. 0.00843     2011    34.8     21.5     35.5 #>  2    34  1495 23519461     1087. 0.00843     2011    34.8     21.5     35.5 #>  3    34  1496 23519461     1087. 0.00843     2011    34.8     21.5     35.5 #>  4    34  1497 23519461     1087. 0.00843     2011    34.8     21.5     35.5 #>  5    34  1498 23519461     1087. 0.00843     2011    34.8     21.5     35.5 #>  6    34  1499 23519461     1087. 0.00843     2011    34.8     21.5     35.5 #>  7    34  1500 23519461     1087. 0.00843     2013    34.8     21.5     35.5 #>  8    34  1501 23519461     1087. 0.00843     2013    34.8     21.5     35.5 #>  9    34  1502 23519461     1087. 0.00843     2013    34.8     21.5     35.5 #> 10    34  1503 23519461     1087. 0.00843     2011    34.8     21.5     35.5 #> # ℹ 644 more rows #> # ℹ 11 more variables: rcaAreaKm2 <dbl>, h2oAreaKm2 <dbl>, ratio <dbl>, #> #   snapdist <dbl>, upDist <dbl>, afvArea <dbl>, locID <int>, netID <dbl>, #> #   netgeom <chr>, .fitted <dbl>, geometry <POINT [m]>"},{"path":"/reference/coef.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted model coefficients — coef.SSN2","title":"Extract fitted model coefficients — coef.SSN2","text":"coef extracts fitted model coefficients fitted model objects. coefficients alias .","code":""},{"path":"/reference/coef.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted model coefficients — coef.SSN2","text":"","code":"# S3 method for class 'ssn_lm' coef(object, type = \"fixed\", ...)  # S3 method for class 'ssn_lm' coefficients(object, type = \"fixed\", ...)  # S3 method for class 'ssn_glm' coef(object, type = \"fixed\", ...)  # S3 method for class 'ssn_glm' coefficients(object, type = \"fixed\", ...)"},{"path":"/reference/coef.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted model coefficients — coef.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). type \"fixed\" fixed effect coefficients, \"tailup\" tailup covariance parameter coefficients, \"taildown\" taildown covariance parameter coefficients, \"euclid\" Euclidean covariance parameter coefficients, \"nugget\" nugget covariance parameter coefficients, \"dispersion\" dispersion parameter coefficient (ssn_glm() objects), \"randcov\" random effect variance coefficients, \"ssn\" tailup, taildown, Euclidean, nugget, dispersion (ssn_glm() objects) parameter coefficients. Defaults \"fixed\". ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/coef.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract fitted model coefficients — coef.SSN2","text":"named vector coefficients.","code":""},{"path":"/reference/coef.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted model coefficients — coef.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) coef(ssn_mod) #> (Intercept)    ELEV_DEM  #>  80.8578372  -0.0341245  coef(ssn_mod, type = \"tailup\") #>           de        range  #> 1.390296e+00 1.306032e+05  #> attr(,\"class\") #> [1] \"tailup_exponential\" coefficients(ssn_mod) #> (Intercept)    ELEV_DEM  #>  80.8578372  -0.0341245"},{"path":"/reference/confint.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for fitted model parameters — confint.SSN2","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"Computes confidence intervals one parameters fitted model object.","code":""},{"path":"/reference/confint.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"","code":"# S3 method for class 'ssn_lm' confint(object, parm, level = 0.95, ...)  # S3 method for class 'ssn_glm' confint(object, parm, level = 0.95, ...)"},{"path":"/reference/confint.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). parm specification parameters given confidence intervals (character vector names). missing, parameters considered. level confidence level required. default 0.95. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/confint.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"Gaussian-based confidence intervals (two-sided equal-tailed) fixed effect coefficients based confidence level specified level. ssn_glm() objects, confidence intervals link scale.","code":""},{"path":"/reference/confint.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for fitted model parameters — confint.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) confint(ssn_mod) #>                   2.5 %      97.5 % #> (Intercept) 63.58249155 98.13318289 #> ELEV_DEM    -0.04270212 -0.02554688 confint(ssn_mod, level = 0.9) #>                     5 %        95 % #> (Intercept) 66.35991011 95.35576433 #> ELEV_DEM    -0.04132307 -0.02692593"},{"path":"/reference/cooks.distance.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cook's distance — cooks.distance.SSN2","title":"Compute Cook's distance — cooks.distance.SSN2","text":"Compute Cook's distance observation fitted model object.","code":""},{"path":"/reference/cooks.distance.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cook's distance — cooks.distance.SSN2","text":"","code":"# S3 method for class 'ssn_lm' cooks.distance(model, ...)  # S3 method for class 'ssn_glm' cooks.distance(model, ...)"},{"path":"/reference/cooks.distance.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cook's distance — cooks.distance.SSN2","text":"model fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/cooks.distance.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Cook's distance — cooks.distance.SSN2","text":"vector Cook's distance values observation fitted model object.","code":""},{"path":"/reference/cooks.distance.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Cook's distance — cooks.distance.SSN2","text":"Cook's distance measures influence observation fitted model object. observation influential, omission data noticeably impacts parameter estimates. larger Cook's distance, larger influence.","code":""},{"path":[]},{"path":"/reference/cooks.distance.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Cook's distance — cooks.distance.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) cooks.distance(ssn_mod) #>            1            2            3            4            5            6  #> 1.647503e-02 1.728298e-03 6.579438e-03 8.927099e-03 1.578560e-02 9.701374e-04  #>            7            8            9           10           11           12  #> 5.068794e-05 1.432315e-02 6.663274e-03 1.956699e-02 5.615854e-03 3.310957e-04  #>           13           14           15           16           17           18  #> 3.666827e-04 1.131630e-01 3.727255e-02 1.286650e-02 6.826981e-04 6.797500e-05  #>           19           20           21           22           23           24  #> 3.876893e-03 1.462109e-04 8.518871e-05 1.666836e-03 2.434997e-03 3.522678e-02  #>           25           26           27           28           29           30  #> 9.185776e-02 1.757621e-04 4.823722e-03 9.509376e-02 1.457562e-02 2.230686e-02  #>           31           32           33           34           35           36  #> 2.639139e-02 1.328369e-02 1.305442e-03 1.840481e-03 6.386774e-04 3.336002e-04  #>           37           38           39           40           41           42  #> 1.436665e-04 1.288808e-01 4.569277e-04 1.439454e-03 4.960449e-03 4.691920e-03  #>           43           44           45  #> 1.208746e-02 4.362963e-02 5.795615e-01"},{"path":"/reference/copy_lsn_to_temp.html","id":null,"dir":"Reference","previous_headings":"","what":"Copy LSN to temporary directory — copy_lsn_to_temp","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"Copies LSN directory MiddleFork04.ssn R's temporary directory examples SSN2 write local library places.","code":""},{"path":"/reference/copy_lsn_to_temp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"","code":"copy_lsn_to_temp()"},{"path":"/reference/copy_lsn_to_temp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"copy MiddleFork04.ssn residing R's temporary directory","code":""},{"path":"/reference/copy_lsn_to_temp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"Copies LSN directory MiddleFork04.ssn R's temporary directory","code":""},{"path":"/reference/copy_lsn_to_temp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Copy LSN to temporary directory — copy_lsn_to_temp","text":"","code":"copy_lsn_to_temp() # getwd() # setwd(tempdir()) # getwd() # if unix-alike, list temporary directory contents using: system('ls') # if windows, list temporary directory contents using: shell('dir')"},{"path":"/reference/covmatrix.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a covariance matrix — covmatrix.SSN2","title":"Create a covariance matrix — covmatrix.SSN2","text":"Create covariance matrix fitted model object.","code":""},{"path":"/reference/covmatrix.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a covariance matrix — covmatrix.SSN2","text":"","code":"# S3 method for class 'ssn_lm' covmatrix(object, newdata, cov_type, ...)  # S3 method for class 'ssn_glm' covmatrix(object, newdata, cov_type, ...)"},{"path":"/reference/covmatrix.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a covariance matrix — covmatrix.SSN2","text":"object fitted model object (e.g., ssn_lm() ssn_glm()). newdata omitted, covariance matrix observed data returned. provided, newdata data frame sf object contains coordinate information required construct covariance newdata observed data. data frame, newdata must contain variables represent coordinates name coordinates observed data used fit object. sf object, coordinates obtained geometry newdata. cov_type type covariance matrix returned. newdata omitted, \\(n \\times n\\) covariance matrix observed data returned, \\(n\\) sample size used fit object. newdata provided cov_type \"pred.obs\" (default), \\(m \\times n\\) covariance matrix predicted observed data returned, \\(m\\) number observations prediction data. newdata provided cov_type \"obs.pred\", \\(n \\times m\\) covariance matrix observed prediction data returned. newdata provided cov_type \"pred.pred\", \\(m \\times m\\) covariance matrix prediction data returned. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/covmatrix.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a covariance matrix — covmatrix.SSN2","text":"covariance matrix (see cov_type).","code":""},{"path":"/reference/covmatrix.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a covariance matrix — covmatrix.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"CapeHorn\", overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) covmatrix(ssn_mod) #>            [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7] #>  [1,] 1.4444501 1.3695561 1.3639389 0.5187511 0.5218678 0.7639806 0.8291424 #>  [2,] 1.3695561 1.4444501 1.3845940 0.5110125 0.5140827 0.7525837 0.8167735 #>  [3,] 1.3639389 1.3845940 1.4444501 0.5089166 0.5119742 0.7494970 0.8134235 #>  [4,] 0.5187511 0.5110125 0.5089166 1.4444501 1.3819932 0.9440263 0.8698358 #>  [5,] 0.5218678 0.5140827 0.5119742 1.3819932 1.4444501 0.9496981 0.8750618 #>  [6,] 0.7639806 0.7525837 0.7494970 0.9440263 0.9496981 1.4444501 1.2810337 #>  [7,] 0.8291424 0.8167735 0.8134235 0.8698358 0.8750618 1.2810337 1.4444501 #>  [8,] 0.9046265 0.8911314 0.8874765 0.7972548 0.8020447 1.1741414 1.2742869 #>  [9,] 0.0000000 0.0000000 0.0000000 0.6018175 0.6054333 0.8863150 0.9619110 #> [10,] 0.0000000 0.0000000 0.0000000 0.6006342 0.6042428 0.8845722 0.9600196 #> [11,] 0.0000000 0.0000000 0.0000000 0.5122583 0.5153359 0.7544183 0.8187646 #> [12,] 0.0000000 0.0000000 0.0000000 0.4112291 0.4136998 0.6056296 0.6572853 #> [13,] 0.0000000 0.0000000 0.0000000 0.4083319 0.4107852 0.6013628 0.6526546 #> [14,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>            [,8]      [,9]     [,10]     [,11]     [,12]     [,13]     [,14] #>  [1,] 0.9046265 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [2,] 0.8911314 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [3,] 0.8874765 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [4,] 0.7972548 0.6018175 0.6006342 0.5122583 0.4112291 0.4083319 0.0000000 #>  [5,] 0.8020447 0.6054333 0.6042428 0.5153359 0.4136998 0.4107852 0.0000000 #>  [6,] 1.1741414 0.8863150 0.8845722 0.7544183 0.6056296 0.6013628 0.0000000 #>  [7,] 1.2742869 0.9619110 0.9600196 0.8187646 0.6572853 0.6526546 0.0000000 #>  [8,] 1.4444501 1.0494821 1.0474185 0.8933038 0.7171237 0.7120714 0.0000000 #>  [9,] 1.0494821 1.4444501 1.3875626 1.1833999 0.9500061 0.9433131 0.0000000 #> [10,] 1.0474185 1.3875626 1.4444501 1.1857314 0.9518777 0.9451716 0.0000000 #> [11,] 0.8933038 1.1833999 1.1857314 1.4444501 1.1160977 1.1082346 0.0000000 #> [12,] 0.7171237 0.9500061 0.9518777 1.1160977 1.4444501 1.3805014 0.0000000 #> [13,] 0.7120714 0.9433131 0.9451716 1.1082346 1.3805014 1.4444501 0.0000000 #> [14,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.4444501 #> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7600376 #> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7544633 #> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7485414 #> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7462835 #> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7123108 #> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6854196 #> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6841607 #> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5799626 #> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.1538975 #> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.8382513 #> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6655399 #> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6634222 #> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6486203 #> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6414849 #> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.6321428 #> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5157278 #> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5031842 #> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4399735 #> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>           [,15]     [,16]     [,17]     [,18]     [,19]     [,20]     [,21] #>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [14,] 0.7600376 0.7544633 0.7485414 0.7462835 0.7123108 0.6854196 0.6841607 #> [15,] 1.4444501 1.3800996 1.3692669 1.3651367 1.3029922 1.2538016 1.2514986 #> [16,] 1.3800996 1.4444501 1.3793836 1.3752228 1.3126192 1.2630651 1.2607452 #> [17,] 1.3692669 1.3793836 1.4444501 1.3861026 1.3230038 1.2730576 1.2707193 #> [18,] 1.3651367 1.3752228 1.3861026 1.4444501 1.3270065 1.2769093 1.2745639 #> [19,] 1.3029922 1.3126192 1.3230038 1.3270065 1.4444501 1.3378097 1.3353525 #> [20,] 1.2538016 1.2630651 1.2730576 1.2769093 1.3378097 1.4444501 1.3877427 #> [21,] 1.2514986 1.2607452 1.2707193 1.2745639 1.3353525 1.3877427 1.4444501 #> [22,] 1.0608946 1.0687329 1.0771880 1.0804470 1.1319775 1.1763886 1.1785533 #> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [44,] 0.8048201 0.8107665 0.8171807 0.8196531 0.8587453 0.8924366 0.8940788 #> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>           [,22]     [,23]     [,24]     [,25]     [,26]     [,27]     [,28] #>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [14,] 0.5799626 1.1538975 0.8382513 0.6655399 0.6634222 0.6486203 0.6414849 #> [15,] 1.0608946 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [16,] 1.0687329 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [17,] 1.0771880 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [18,] 1.0804470 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [19,] 1.1319775 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [20,] 1.1763886 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [21,] 1.1785533 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [22,] 1.4444501 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [23,] 0.0000000 1.4444501 1.0099837 0.8018889 0.7993374 0.7815031 0.7729059 #> [24,] 0.0000000 1.0099837 1.4444501 1.1038428 1.1003305 1.0757806 1.0639461 #> [25,] 0.0000000 0.8018889 1.1038428 1.4444501 1.3858726 1.3549518 1.3400461 #> [26,] 0.0000000 0.7993374 1.1003305 1.3858726 1.4444501 1.3592768 1.3443235 #> [27,] 0.0000000 0.7815031 1.0757806 1.3549518 1.3592768 1.4444501 1.3750018 #> [28,] 0.0000000 0.7729059 1.0639461 1.3400461 1.3443235 1.3750018 1.4444501 #> [29,] 0.0000000 0.7616498 1.0484515 1.3205306 1.3247457 1.3549772 1.3700490 #> [30,] 0.0000000 0.6213848 0.8553693 1.0773425 1.0807814 1.1054455 1.1177416 #> [31,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [32,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [33,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [34,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [35,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [36,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [37,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [38,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [39,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [40,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [41,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [42,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [43,] 0.0000000 0.6062715 0.8345649 0.0000000 0.0000000 0.0000000 0.0000000 #> [44,] 1.0547121 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [45,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>           [,29]     [,30]     [,31]     [,32]     [,33]     [,34]     [,35] #>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [14,] 0.6321428 0.5157278 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [23,] 0.7616498 0.6213848 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [24,] 1.0484515 0.8553693 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [25,] 1.3205306 1.0773425 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [26,] 1.3247457 1.0807814 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [27,] 1.3549772 1.1054455 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [28,] 1.3700490 1.1177416 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [29,] 1.4444501 1.1342602 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [30,] 1.1342602 1.4444501 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [31,] 0.0000000 0.0000000 1.4444501 1.3834966 1.3055425 1.2591504 1.2658696 #> [32,] 0.0000000 0.0000000 1.3834966 1.4444501 1.3119591 1.2653390 1.2720912 #> [33,] 0.0000000 0.0000000 1.3055425 1.3119591 1.4444501 1.3408925 1.3480479 #> [34,] 0.0000000 0.0000000 1.2591504 1.2653390 1.3408925 1.4444501 1.3829167 #> [35,] 0.0000000 0.0000000 1.2658696 1.2720912 1.3480479 1.3829167 1.4444501 #> [36,] 0.0000000 0.0000000 1.1822100 1.1880204 1.2589572 1.3053422 1.2984135 #> [37,] 0.0000000 0.0000000 1.0540673 1.0592479 1.1224957 1.1638529 1.1576752 #> [38,] 0.0000000 0.0000000 0.9019153 0.9063481 0.9604662 0.9958536 0.9905677 #> [39,] 0.0000000 0.0000000 0.7412357 0.7448788 0.7893555 0.8184385 0.8140943 #> [40,] 0.0000000 0.0000000 0.7391329 0.7427656 0.7871162 0.8161167 0.8117848 #> [41,] 0.0000000 0.0000000 0.6543039 0.6575197 0.6967803 0.7224524 0.7186177 #> [42,] 0.0000000 0.0000000 0.5398135 0.5424666 0.5748574 0.5960374 0.5928737 #> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [45,] 0.0000000 0.0000000 0.5010885 0.5035513 0.5336184 0.5532790 0.5503422 #>           [,36]     [,37]     [,38]     [,39]     [,40]     [,41]     [,42] #>  [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [3,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #>  [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [13,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [14,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [15,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [16,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [17,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [18,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [19,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [20,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [21,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [22,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [23,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [24,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [25,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [26,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [27,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [28,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [29,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [30,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [31,] 1.1822100 1.0540673 0.9019153 0.7412357 0.7391329 0.6543039 0.5398135 #> [32,] 1.1880204 1.0592479 0.9063481 0.7448788 0.7427656 0.6575197 0.5424666 #> [33,] 1.2589572 1.1224957 0.9604662 0.7893555 0.7871162 0.6967803 0.5748574 #> [34,] 1.3053422 1.1638529 0.9958536 0.8184385 0.8161167 0.7224524 0.5960374 #> [35,] 1.2984135 1.1576752 0.9905677 0.8140943 0.8117848 0.7186177 0.5928737 #> [36,] 1.4444501 1.2395986 1.0606656 0.8717040 0.8692311 0.7694710 0.6348286 #> [37,] 1.2395986 1.4444501 1.1896105 0.9776769 0.9749033 0.8630153 0.7120046 #> [38,] 1.0606656 1.1896105 1.4444501 1.1426097 1.1393682 1.0086049 0.8321188 #> [39,] 0.8717040 0.9776769 1.1426097 1.4444501 1.3863521 1.2272429 1.0124995 #> [40,] 0.8692311 0.9749033 1.1393682 1.3863521 1.4444501 1.2307344 1.0153800 #> [41,] 0.7694710 0.8630153 1.0086049 1.2272429 1.2307344 1.4444501 1.1470217 #> [42,] 0.6348286 0.7120046 0.8321188 1.0124995 1.0153800 1.1470217 1.4444501 #> [43,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [44,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 #> [45,] 0.5892874 0.6609269 0.7724245 0.0000000 0.0000000 0.0000000 0.0000000 #>           [,43]     [,44]     [,45] #>  [1,] 0.0000000 0.0000000 0.0000000 #>  [2,] 0.0000000 0.0000000 0.0000000 #>  [3,] 0.0000000 0.0000000 0.0000000 #>  [4,] 0.0000000 0.0000000 0.0000000 #>  [5,] 0.0000000 0.0000000 0.0000000 #>  [6,] 0.0000000 0.0000000 0.0000000 #>  [7,] 0.0000000 0.0000000 0.0000000 #>  [8,] 0.0000000 0.0000000 0.0000000 #>  [9,] 0.0000000 0.0000000 0.0000000 #> [10,] 0.0000000 0.0000000 0.0000000 #> [11,] 0.0000000 0.0000000 0.0000000 #> [12,] 0.0000000 0.0000000 0.0000000 #> [13,] 0.0000000 0.0000000 0.0000000 #> [14,] 0.5031842 0.4399735 0.0000000 #> [15,] 0.0000000 0.8048201 0.0000000 #> [16,] 0.0000000 0.8107665 0.0000000 #> [17,] 0.0000000 0.8171807 0.0000000 #> [18,] 0.0000000 0.8196531 0.0000000 #> [19,] 0.0000000 0.8587453 0.0000000 #> [20,] 0.0000000 0.8924366 0.0000000 #> [21,] 0.0000000 0.8940788 0.0000000 #> [22,] 0.0000000 1.0547121 0.0000000 #> [23,] 0.6062715 0.0000000 0.0000000 #> [24,] 0.8345649 0.0000000 0.0000000 #> [25,] 0.0000000 0.0000000 0.0000000 #> [26,] 0.0000000 0.0000000 0.0000000 #> [27,] 0.0000000 0.0000000 0.0000000 #> [28,] 0.0000000 0.0000000 0.0000000 #> [29,] 0.0000000 0.0000000 0.0000000 #> [30,] 0.0000000 0.0000000 0.0000000 #> [31,] 0.0000000 0.0000000 0.5010885 #> [32,] 0.0000000 0.0000000 0.5035513 #> [33,] 0.0000000 0.0000000 0.5336184 #> [34,] 0.0000000 0.0000000 0.5532790 #> [35,] 0.0000000 0.0000000 0.5503422 #> [36,] 0.0000000 0.0000000 0.5892874 #> [37,] 0.0000000 0.0000000 0.6609269 #> [38,] 0.0000000 0.0000000 0.7724245 #> [39,] 0.0000000 0.0000000 0.0000000 #> [40,] 0.0000000 0.0000000 0.0000000 #> [41,] 0.0000000 0.0000000 0.0000000 #> [42,] 0.0000000 0.0000000 0.0000000 #> [43,] 1.4444501 0.0000000 0.0000000 #> [44,] 0.0000000 1.4444501 0.0000000 #> [45,] 0.0000000 0.0000000 1.4444501 covmatrix(ssn_mod, \"CapeHorn\") #>        [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] #>   [1,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [2,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [3,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [4,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [5,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [6,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [7,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [8,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>   [9,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [10,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [11,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [12,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [13,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [14,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [15,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [16,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [17,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [18,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [19,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [20,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [21,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [22,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [23,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [24,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [25,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [26,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [27,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [28,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [29,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [30,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [31,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [32,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [33,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [34,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [35,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [36,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [37,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [38,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [39,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [40,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [41,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [42,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [43,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [44,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [45,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [46,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [47,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [48,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [49,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [50,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [51,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [52,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [53,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [54,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [55,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [56,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [57,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [58,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [59,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [60,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [61,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [62,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [63,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [64,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [65,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [66,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [67,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [68,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [69,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [70,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [71,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [72,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [73,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [74,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [75,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [76,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [77,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [78,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [79,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [80,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [81,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [82,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [83,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [84,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [85,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [86,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [87,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [88,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [89,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [90,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [91,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [92,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [93,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [94,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [95,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [96,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [97,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [98,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>  [99,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [100,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [101,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [102,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [103,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [104,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [105,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [106,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [107,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [108,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [109,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [110,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [111,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [112,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [113,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [114,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [115,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [116,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [117,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [118,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [119,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [120,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [121,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [122,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [123,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [124,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [125,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [126,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [127,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [128,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [129,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [130,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [131,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [132,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [133,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [134,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [135,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [136,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [137,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [138,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [139,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [140,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [141,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [142,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [143,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [144,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [145,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [146,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [147,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [148,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [149,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [150,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [151,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [152,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [153,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [154,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [155,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [156,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [157,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [158,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [159,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [160,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [161,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [162,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [163,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [164,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [165,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [166,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [167,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [168,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [169,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [170,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [171,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [172,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [173,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [174,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [175,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [176,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [177,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [178,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [179,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [180,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [181,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [182,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [183,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [184,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [185,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [186,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [187,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [188,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [189,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [190,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [191,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [192,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [193,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [194,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [195,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [196,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [197,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [198,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [199,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [200,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [201,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [202,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [203,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [204,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [205,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [206,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [207,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [208,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [209,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [210,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [211,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [212,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [213,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [214,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [215,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [216,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [217,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [218,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [219,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [220,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [221,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [222,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [223,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [224,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [225,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [226,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [227,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [228,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [229,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [230,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [231,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [232,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [233,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [234,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [235,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [236,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [237,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [238,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [239,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [240,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [241,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [242,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [243,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [244,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [245,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [246,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [247,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [248,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [249,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [250,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [251,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [252,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [253,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [254,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [255,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [256,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [257,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [258,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [259,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [260,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [261,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [262,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [263,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [264,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [265,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [266,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [267,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [268,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [269,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [270,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [271,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [272,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [273,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [274,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [275,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [276,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [277,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [278,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [279,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [280,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [281,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [282,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [283,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [284,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [285,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [286,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [287,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [288,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [289,]    0    0    0 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0 #> [302,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [303,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [304,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [305,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [306,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [307,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [308,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [309,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [310,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [311,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [312,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [313,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [314,]    0    0    0    0    0    0    0    0  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0 #> [340,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [341,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [342,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [343,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [344,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [345,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [346,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [347,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [348,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [349,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [350,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [351,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [352,]    0    0    0    0    0    0    0    0  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   0    0    0    0    0    0     0     0     0     0 #> [366,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [367,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [368,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [369,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [370,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [371,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [372,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [373,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [374,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [375,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [376,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [377,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [378,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [379,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [380,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [381,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [382,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [383,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [384,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [385,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [386,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [387,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [388,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [389,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [390,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [391,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [392,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [393,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [394,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [395,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [396,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [397,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [398,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [399,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [400,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [401,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [402,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [403,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [404,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [405,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [406,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [407,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [408,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [409,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [410,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [411,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [412,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [413,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [414,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [415,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [416,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [417,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [418,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [419,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [420,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [421,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [422,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [423,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [424,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [425,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [426,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [427,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [428,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [429,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [430,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [431,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [432,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [433,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [434,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [435,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [436,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [437,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [438,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [439,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [440,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [441,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [442,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [443,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [444,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [445,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [446,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [447,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [448,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [449,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [450,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [451,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [452,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [453,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [454,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [455,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [456,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [457,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [458,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [459,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [460,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [461,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [462,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [463,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [464,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [465,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [466,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [467,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [468,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [469,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [470,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [471,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [472,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [473,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [474,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [475,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [476,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [477,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [478,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [479,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [480,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [481,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [482,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [483,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [484,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [485,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [486,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [487,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [488,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [489,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [490,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [491,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [492,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [493,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [494,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [495,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [496,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [497,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [498,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [499,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [500,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [501,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [502,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [503,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [504,]    0    0    0    0    0    0    0    0  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0 #> [530,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [531,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [532,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [533,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [534,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [535,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [536,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [537,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [538,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [539,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [540,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [541,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [542,]    0    0    0    0    0    0    0    0  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0 #> [568,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [569,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [570,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [571,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [572,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [573,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [574,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [575,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [576,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [577,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [578,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [579,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [580,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [581,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [582,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [583,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [584,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [585,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [586,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [587,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [588,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [589,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [590,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [591,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [592,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [593,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [594,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [595,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [596,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [597,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [598,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [599,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [600,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [601,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [602,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [603,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [604,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [605,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [606,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [607,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [608,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [609,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [610,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [611,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [612,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [613,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [614,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [615,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [616,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [617,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [618,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [619,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [620,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [621,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [622,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [623,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [624,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [625,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [626,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [627,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [628,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [629,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [630,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [631,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [632,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [633,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [634,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [635,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [636,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [637,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [638,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [639,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [640,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [641,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [642,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [643,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [644,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [645,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [646,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [647,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [648,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [649,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [650,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [651,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [652,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [653,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #> [654,]    0    0    0    0    0    0    0    0    0     0     0     0     0 #>            [,14]    [,15]    [,16]    [,17]    [,18]    [,19]    [,20]    [,21] #>   [1,] 0.7430226 1.359172 1.369214 1.380046 1.384221 1.332830 1.282513 1.280158 #>   [2,] 0.7430795 1.359276 1.369319 1.380152 1.384327 1.332728 1.282415 1.280060 #>   [3,] 0.7431364 1.359380 1.369423 1.380257 1.384433 1.332626 1.282317 1.279962 #>   [4,] 0.7431933 1.359484 1.369528 1.380363 1.384539 1.332524 1.282219 1.279864 #>   [5,] 0.7432502 1.359588 1.369633 1.380469 1.384645 1.332422 1.282121 1.279766 #>   [6,] 0.7433071 1.359692 1.369738 1.380574 1.384751 1.332320 1.282022 1.279668 #>   [7,] 0.7433640 1.359796 1.369843 1.380680 1.384857 1.332218 1.281924 1.279570 #>   [8,] 0.7434209 1.359900 1.369948 1.380786 1.384963 1.332116 1.281826 1.279472 #>   [9,] 0.7434779 1.360004 1.370053 1.380892 1.385069 1.332014 1.281728 1.279374 #>  [10,] 0.7435348 1.360109 1.370158 1.380997 1.385176 1.331912 1.281630 1.279276 #>  [11,] 0.7435917 1.360213 1.370263 1.381103 1.385282 1.331810 1.281532 1.279178 #>  [12,] 0.7436487 1.360317 1.370367 1.381209 1.385388 1.331708 1.281434 1.279080 #>  [13,] 0.7437056 1.360421 1.370472 1.381315 1.385494 1.331606 1.281335 1.278982 #>  [14,] 0.7437626 1.360525 1.370577 1.381420 1.385600 1.331504 1.281237 1.278884 #>  [15,] 0.7438195 1.360629 1.370682 1.381526 1.385706 1.331402 1.281139 1.278786 #>  [16,] 0.7438765 1.360734 1.370787 1.381632 1.385812 1.331300 1.281041 1.278688 #>  [17,] 0.7439334 1.360838 1.370892 1.381738 1.385918 1.331199 1.280943 1.278590 #>  [18,] 0.7439904 1.360942 1.370997 1.381844 1.386024 1.331097 1.280845 1.278492 #>  [19,] 0.7440473 1.361046 1.371102 1.381949 1.386130 1.330995 1.280747 1.278394 #>  [20,] 0.7441043 1.361150 1.371207 1.382055 1.386237 1.330893 1.280649 1.278297 #>  [21,] 0.7441613 1.361255 1.371312 1.382161 1.386343 1.330791 1.280551 1.278199 #>  [22,] 0.7442183 1.361359 1.371417 1.382267 1.386449 1.330689 1.280453 1.278101 #>  [23,] 0.7442753 1.361463 1.371522 1.382373 1.386555 1.330587 1.280355 1.278003 #>  [24,] 0.7443323 1.361567 1.371627 1.382478 1.386661 1.330485 1.280257 1.277905 #>  [25,] 0.7443892 1.361672 1.371732 1.382584 1.386767 1.330383 1.280159 1.277807 #>  [26,] 0.7444462 1.361776 1.371837 1.382690 1.386874 1.330282 1.280061 1.277709 #>  [27,] 0.7445032 1.361880 1.371942 1.382796 1.386980 1.330180 1.279963 1.277612 #>  [28,] 0.7445603 1.361984 1.372047 1.382902 1.387086 1.330078 1.279865 1.277514 #>  [29,] 0.7446173 1.362089 1.372152 1.383008 1.387192 1.329976 1.279767 1.277416 #>  [30,] 0.7446743 1.362193 1.372257 1.383114 1.387298 1.329874 1.279669 1.277318 #>  [31,] 0.7447313 1.362297 1.372362 1.383220 1.387405 1.329772 1.279571 1.277220 #>  [32,] 0.7447883 1.362402 1.372468 1.383326 1.387511 1.329671 1.279473 1.277123 #>  [33,] 0.7448454 1.362506 1.372573 1.383431 1.387617 1.329569 1.279375 1.277025 #>  [34,] 0.7449024 1.362610 1.372678 1.383537 1.387723 1.329467 1.279277 1.276927 #>  [35,] 0.7449594 1.362715 1.372783 1.383643 1.387830 1.329365 1.279179 1.276829 #>  [36,] 0.7450165 1.362819 1.372888 1.383749 1.387936 1.329263 1.279081 1.276732 #>  [37,] 0.7450735 1.362923 1.372993 1.383855 1.388042 1.329162 1.278983 1.276634 #>  [38,] 0.7451306 1.363028 1.373098 1.383961 1.388148 1.329060 1.278885 1.276536 #>  [39,] 0.7451876 1.363132 1.373203 1.384067 1.388255 1.328958 1.278787 1.276438 #>  [40,] 0.7452447 1.363236 1.373309 1.384173 1.388361 1.328856 1.278689 1.276341 #>  [41,] 0.7453017 1.363341 1.373414 1.384279 1.388467 1.328755 1.278591 1.276243 #>  [42,] 0.7453588 1.363445 1.373519 1.384385 1.388574 1.328653 1.278493 1.276145 #>  [43,] 0.7454159 1.363550 1.373624 1.384491 1.388680 1.328551 1.278396 1.276047 #>  [44,] 0.7454730 1.363654 1.373729 1.384597 1.388786 1.328449 1.278298 1.275950 #>  [45,] 0.7455300 1.363758 1.373834 1.384703 1.388893 1.328348 1.278200 1.275852 #>  [46,] 0.7455871 1.363863 1.373940 1.384809 1.388999 1.328246 1.278102 1.275754 #>  [47,] 0.7456442 1.363967 1.374045 1.384915 1.389105 1.328144 1.278004 1.275657 #>  [48,] 0.7457013 1.364072 1.374150 1.385021 1.389212 1.328043 1.277906 1.275559 #>  [49,] 0.7457584 1.364176 1.374255 1.385127 1.389318 1.327941 1.277808 1.275461 #>  [50,] 0.7458155 1.364281 1.374360 1.385233 1.389424 1.327839 1.277711 1.275364 #>  [51,] 0.7458726 1.364385 1.374466 1.385339 1.389531 1.327738 1.277613 1.275266 #>  [52,] 0.7459297 1.364490 1.374571 1.385446 1.389637 1.327636 1.277515 1.275168 #>  [53,] 0.7459869 1.364594 1.374676 1.385552 1.389744 1.327534 1.277417 1.275071 #>  [54,] 0.7460440 1.364698 1.374781 1.385658 1.389850 1.327433 1.277319 1.274973 #>  [55,] 0.7461011 1.364803 1.374887 1.385764 1.389956 1.327331 1.277221 1.274876 #>  [56,] 0.7461582 1.364907 1.374992 1.385870 1.390063 1.327229 1.277124 1.274778 #>  [57,] 0.7462154 1.365012 1.375097 1.385976 1.390169 1.327128 1.277026 1.274680 #>  [58,] 0.7462725 1.365117 1.375203 1.386082 1.390276 1.327026 1.276928 1.274583 #>  [59,] 0.7463296 1.365221 1.375308 1.386188 1.390210 1.326924 1.276830 1.274485 #>  [60,] 0.7463868 1.365326 1.375413 1.386294 1.390104 1.326823 1.276733 1.274388 #>  [61,] 0.7464439 1.365430 1.375518 1.386401 1.389997 1.326721 1.276635 1.274290 #>  [62,] 0.7465011 1.365535 1.375624 1.386507 1.389891 1.326620 1.276537 1.274192 #>  [63,] 0.7465583 1.365639 1.375729 1.386613 1.389785 1.326518 1.276439 1.274095 #>  [64,] 0.7466154 1.365744 1.375834 1.386719 1.389678 1.326417 1.276342 1.273997 #>  [65,] 0.7466726 1.365848 1.375940 1.386825 1.389572 1.326315 1.276244 1.273900 #>  [66,] 0.7467298 1.365953 1.376045 1.386931 1.389465 1.326213 1.276146 1.273802 #>  [67,] 0.7467869 1.366058 1.376151 1.387038 1.389359 1.326112 1.276048 1.273705 #>  [68,] 0.7468441 1.366162 1.376256 1.387144 1.389253 1.326010 1.275951 1.273607 #>  [69,] 0.7469013 1.366267 1.376361 1.387250 1.389146 1.325909 1.275853 1.273510 #>  [70,] 0.7469585 1.366371 1.376467 1.387356 1.389040 1.325807 1.275755 1.273412 #>  [71,] 0.7470157 1.366476 1.376572 1.387463 1.388934 1.325706 1.275658 1.273315 #>  [72,] 0.7470729 1.366581 1.376677 1.387569 1.388827 1.325604 1.275560 1.273217 #>  [73,] 0.7471301 1.366685 1.376783 1.387675 1.388721 1.325503 1.275462 1.273120 #>  [74,] 0.7471873 1.366790 1.376888 1.387781 1.388615 1.325401 1.275365 1.273022 #>  [75,] 0.7472445 1.366895 1.376994 1.387888 1.388508 1.325300 1.275267 1.272925 #>  [76,] 0.7473017 1.366999 1.377099 1.387994 1.388402 1.325198 1.275169 1.272827 #>  [77,] 0.7473590 1.367104 1.377205 1.388100 1.388296 1.325097 1.275072 1.272730 #>  [78,] 0.7474162 1.367209 1.377310 1.388206 1.388189 1.324995 1.274974 1.272632 #>  [79,] 0.7474734 1.367313 1.377416 1.388313 1.388083 1.324894 1.274877 1.272535 #>  [80,] 0.7475306 1.367418 1.377521 1.388419 1.387977 1.324793 1.274779 1.272437 #>  [81,] 0.7475879 1.367523 1.377626 1.388525 1.387871 1.324691 1.274681 1.272340 #>  [82,] 0.7476451 1.367627 1.377732 1.388632 1.387764 1.324590 1.274584 1.272243 #>  [83,] 0.7477024 1.367732 1.377837 1.388738 1.387658 1.324488 1.274486 1.272145 #>  [84,] 0.7477596 1.367837 1.377943 1.388844 1.387552 1.324387 1.274389 1.272048 #>  [85,] 0.7478169 1.367942 1.378048 1.388951 1.387446 1.324286 1.274291 1.271950 #>  [86,] 0.7478741 1.368046 1.378154 1.389057 1.387339 1.324184 1.274193 1.271853 #>  [87,] 0.7479314 1.368151 1.378260 1.389163 1.387233 1.324083 1.274096 1.271756 #>  [88,] 0.7479887 1.368256 1.378365 1.389270 1.387127 1.323981 1.273998 1.271658 #>  [89,] 0.7480460 1.368361 1.378471 1.389376 1.387021 1.323880 1.273901 1.271561 #>  [90,] 0.7481032 1.368465 1.378576 1.389482 1.386914 1.323779 1.273803 1.271464 #>  [91,] 0.7481605 1.368570 1.378682 1.389589 1.386808 1.323677 1.273706 1.271366 #>  [92,] 0.7482178 1.368675 1.378787 1.389695 1.386702 1.323576 1.273608 1.271269 #>  [93,] 0.7482751 1.368780 1.378893 1.389802 1.386596 1.323475 1.273511 1.271172 #>  [94,] 0.7483324 1.368885 1.378998 1.389908 1.386490 1.323373 1.273413 1.271074 #>  [95,] 0.7483897 1.368989 1.379104 1.390015 1.386384 1.323272 1.273316 1.270977 #>  [96,] 0.7484470 1.369094 1.379210 1.390121 1.386277 1.323171 1.273218 1.270880 #>  [97,] 0.7485043 1.369199 1.379315 1.390227 1.386171 1.323069 1.273121 1.270782 #>  [98,] 0.7485616 1.369304 1.379421 1.390259 1.386065 1.322968 1.273023 1.270685 #>  [99,] 0.7486189 1.369409 1.379526 1.390152 1.385959 1.322867 1.272926 1.270588 #> [100,] 0.7486763 1.369514 1.379632 1.390046 1.385853 1.322765 1.272828 1.270490 #> [101,] 0.7487336 1.369618 1.379738 1.389939 1.385747 1.322664 1.272731 1.270393 #> [102,] 0.7487909 1.369723 1.379843 1.389833 1.385641 1.322563 1.272633 1.270296 #> [103,] 0.7488483 1.369828 1.379949 1.389727 1.385535 1.322462 1.272536 1.270199 #> [104,] 0.7489056 1.369933 1.380055 1.389620 1.385429 1.322360 1.272439 1.270101 #> [105,] 0.7489629 1.370038 1.380160 1.389514 1.385322 1.322259 1.272341 1.270004 #> [106,] 0.7490203 1.370143 1.380266 1.389407 1.385216 1.322158 1.272244 1.269907 #> [107,] 0.7490776 1.370248 1.380372 1.389301 1.385110 1.322057 1.272146 1.269810 #> [108,] 0.7491350 1.370353 1.380477 1.389195 1.385004 1.321955 1.272049 1.269712 #> [109,] 0.7491924 1.370458 1.380583 1.389088 1.384898 1.321854 1.271951 1.269615 #> [110,] 0.7492497 1.370563 1.380689 1.388982 1.384792 1.321753 1.271854 1.269518 #> [111,] 0.7493071 1.370668 1.380795 1.388876 1.384686 1.321652 1.271757 1.269421 #> [112,] 0.7493645 1.370772 1.380900 1.388769 1.384580 1.321551 1.271659 1.269324 #> [113,] 0.7494219 1.370877 1.381006 1.388663 1.384474 1.321449 1.271562 1.269226 #> [114,] 0.7494792 1.370982 1.381112 1.388557 1.384368 1.321348 1.271465 1.269129 #> [115,] 0.7495366 1.371087 1.381218 1.388450 1.384262 1.321247 1.271367 1.269032 #> [116,] 0.7495940 1.371192 1.381323 1.388344 1.384156 1.321146 1.271270 1.268935 #> [117,] 0.7496514 1.371297 1.381429 1.388238 1.384050 1.321045 1.271173 1.268838 #> [118,] 0.7497088 1.371402 1.381535 1.388131 1.383944 1.320944 1.271075 1.268741 #> [119,] 0.7497662 1.371507 1.381641 1.388025 1.383838 1.320842 1.270978 1.268643 #> [120,] 0.7498236 1.371612 1.381746 1.387919 1.383732 1.320741 1.270881 1.268546 #> [121,] 0.7498810 1.371717 1.381852 1.387813 1.383626 1.320640 1.270783 1.268449 #> [122,] 0.7499385 1.371822 1.381958 1.387706 1.383520 1.320539 1.270686 1.268352 #> [123,] 0.7499959 1.371927 1.382064 1.387600 1.383415 1.320438 1.270589 1.268255 #> [124,] 0.7500533 1.372033 1.382170 1.387494 1.383309 1.320337 1.270491 1.268158 #> [125,] 0.7501107 1.372138 1.382276 1.387388 1.383203 1.320236 1.270394 1.268061 #> [126,] 0.7501682 1.372243 1.382381 1.387281 1.383097 1.320135 1.270297 1.267964 #> [127,] 0.7502256 1.372348 1.382487 1.387175 1.382991 1.320034 1.270200 1.267867 #> [128,] 0.7502831 1.372453 1.382593 1.387069 1.382885 1.319933 1.270102 1.267770 #> [129,] 0.7503405 1.372558 1.382699 1.386963 1.382779 1.319832 1.270005 1.267672 #> [130,] 0.7503980 1.372663 1.382805 1.386857 1.382673 1.319730 1.269908 1.267575 #> [131,] 0.7504554 1.372768 1.382911 1.386750 1.382567 1.319629 1.269811 1.267478 #> [132,] 0.7505129 1.372873 1.383017 1.386644 1.382462 1.319528 1.269713 1.267381 #> [133,] 0.7505704 1.372978 1.383122 1.386538 1.382356 1.319427 1.269616 1.267284 #> [134,] 0.7506278 1.373083 1.383228 1.386432 1.382250 1.319326 1.269519 1.267187 #> [135,] 0.7506853 1.373189 1.383334 1.386326 1.382144 1.319225 1.269422 1.267090 #> [136,] 0.7507428 1.373294 1.383440 1.386220 1.382038 1.319124 1.269325 1.266993 #> [137,] 0.7508003 1.373399 1.383546 1.386113 1.381932 1.319023 1.269227 1.266896 #> [138,] 0.7508578 1.373504 1.383652 1.386007 1.381827 1.318922 1.269130 1.266799 #> [139,] 0.7509153 1.373609 1.383758 1.385901 1.381721 1.318821 1.269033 1.266702 #> [140,] 0.7509728 1.373714 1.383864 1.385795 1.381615 1.318720 1.268936 1.266605 #> [141,] 0.7510303 1.373820 1.383970 1.385689 1.381509 1.318619 1.268839 1.266508 #> [142,] 0.7510878 1.373925 1.384076 1.385583 1.381403 1.318518 1.268742 1.266411 #> [143,] 0.7511453 1.374030 1.384182 1.385477 1.381298 1.318417 1.268645 1.266314 #> [144,] 0.7512028 1.374135 1.384288 1.385371 1.381192 1.318317 1.268547 1.266217 #> [145,] 0.7512603 1.374240 1.384394 1.385265 1.381086 1.318216 1.268450 1.266120 #> [146,] 0.7513178 1.374346 1.384500 1.385159 1.380980 1.318115 1.268353 1.266023 #> [147,] 0.7513754 1.374451 1.384606 1.385052 1.380875 1.318014 1.268256 1.265927 #> [148,] 0.7514329 1.374556 1.384712 1.384946 1.380769 1.317913 1.268159 1.265830 #> [149,] 0.7514904 1.374661 1.384818 1.384840 1.380663 1.317812 1.268062 1.265733 #> [150,] 0.7515480 1.374767 1.384924 1.384734 1.380558 1.317711 1.267965 1.265636 #> [151,] 0.7516055 1.374872 1.385030 1.384628 1.380452 1.317610 1.267868 1.265539 #> [152,] 0.7516631 1.374977 1.385136 1.384522 1.380346 1.317509 1.267771 1.265442 #> [153,] 0.7517206 1.375082 1.385242 1.384416 1.380240 1.317408 1.267674 1.265345 #> [154,] 0.7517782 1.375188 1.385348 1.384310 1.380135 1.317308 1.267576 1.265248 #> [155,] 0.7518358 1.375293 1.385454 1.384204 1.380029 1.317207 1.267479 1.265151 #> [156,] 0.7518933 1.375398 1.385560 1.384098 1.379923 1.317106 1.267382 1.265054 #> [157,] 0.7519509 1.375504 1.385666 1.383992 1.379818 1.317005 1.267285 1.264958 #> [158,] 0.7520085 1.375609 1.385773 1.383886 1.379712 1.316904 1.267188 1.264861 #> [159,] 0.7520661 1.375714 1.385879 1.383780 1.379606 1.316803 1.267091 1.264764 #> [160,] 0.7521236 1.375820 1.385985 1.383675 1.379501 1.316702 1.266994 1.264667 #> [161,] 0.7521812 1.375925 1.386091 1.383569 1.379395 1.316602 1.266897 1.264570 #> [162,] 0.7522388 1.376030 1.386197 1.383463 1.379290 1.316501 1.266800 1.264473 #> [163,] 0.7522964 1.376136 1.386303 1.383357 1.379184 1.316400 1.266703 1.264377 #> [164,] 0.7523540 1.376241 1.386409 1.383251 1.379078 1.316299 1.266606 1.264280 #> [165,] 0.7524116 1.376347 1.386516 1.383145 1.378973 1.316198 1.266509 1.264183 #> [166,] 0.7524693 1.376452 1.386622 1.383039 1.378867 1.316098 1.266412 1.264086 #> [167,] 0.7525269 1.376557 1.386728 1.382933 1.378762 1.315997 1.266315 1.263989 #> [168,] 0.7525845 1.376663 1.386834 1.382827 1.378656 1.315896 1.266218 1.263893 #> [169,] 0.7526421 1.376768 1.386940 1.382721 1.378551 1.315795 1.266121 1.263796 #> [170,] 0.7526997 1.376874 1.387046 1.382615 1.378445 1.315695 1.266025 1.263699 #> [171,] 0.7527574 1.376979 1.387153 1.382510 1.378339 1.315594 1.265928 1.263602 #> [172,] 0.7528150 1.377084 1.387259 1.382404 1.378234 1.315493 1.265831 1.263506 #> [173,] 0.7528727 1.377190 1.387365 1.382298 1.378128 1.315393 1.265734 1.263409 #> [174,] 0.7529303 1.377295 1.387471 1.382192 1.378023 1.315292 1.265637 1.263312 #> [175,] 0.7529880 1.377401 1.387578 1.382086 1.377917 1.315191 1.265540 1.263215 #> [176,] 0.7530456 1.377506 1.387684 1.381980 1.377812 1.315090 1.265443 1.263119 #> [177,] 0.7531033 1.377612 1.387790 1.381875 1.377706 1.314990 1.265346 1.263022 #> [178,] 0.7531610 1.377717 1.387896 1.381769 1.377601 1.314889 1.265249 1.262925 #> [179,] 0.7532186 1.377823 1.388003 1.381663 1.377495 1.314788 1.265152 1.262829 #> [180,] 0.7532763 1.377928 1.388109 1.381557 1.377390 1.314688 1.265056 1.262732 #> [181,] 0.7533340 1.378034 1.388215 1.381451 1.377285 1.314587 1.264959 1.262635 #> [182,] 0.7533917 1.378139 1.388321 1.381346 1.377179 1.314486 1.264862 1.262539 #> [183,] 0.7534493 1.378245 1.388428 1.381240 1.377074 1.314386 1.264765 1.262442 #> [184,] 0.7535070 1.378350 1.388534 1.381134 1.376968 1.314285 1.264668 1.262345 #> [185,] 0.7535647 1.378456 1.388640 1.381028 1.376863 1.314184 1.264571 1.262249 #> [186,] 0.7536224 1.378561 1.388747 1.380923 1.376757 1.314084 1.264474 1.262152 #> [187,] 0.7536801 1.378667 1.388853 1.380817 1.376652 1.313983 1.264378 1.262055 #> [188,] 0.7537379 1.378772 1.388959 1.380711 1.376547 1.313883 1.264281 1.261959 #> [189,] 0.7537956 1.378878 1.389066 1.380606 1.376441 1.313782 1.264184 1.261862 #> [190,] 0.7538533 1.378984 1.389172 1.380500 1.376336 1.313681 1.264087 1.261765 #> [191,] 0.7539110 1.379089 1.389278 1.380394 1.376230 1.313581 1.263990 1.261669 #> [192,] 0.7539687 1.379195 1.389385 1.380288 1.376125 1.313480 1.263894 1.261572 #> [193,] 0.7540265 1.379300 1.389491 1.380183 1.376020 1.313380 1.263797 1.261476 #> [194,] 0.7540842 1.379406 1.389598 1.380077 1.375914 1.313279 1.263700 1.261379 #> [195,] 0.7541419 1.379512 1.389704 1.379971 1.375809 1.313179 1.263603 1.261282 #> [196,] 0.7541997 1.379617 1.389810 1.379866 1.375704 1.313078 1.263507 1.261186 #> [197,] 0.7542574 1.379723 1.389917 1.379760 1.375598 1.312978 1.263410 1.261089 #> [198,] 0.7543152 1.379829 1.390023 1.379654 1.375493 1.312877 1.263313 1.260993 #> [199,] 0.7543730 1.379934 1.390130 1.379549 1.375388 1.312776 1.263216 1.260896 #> [200,] 0.7544307 1.380040 1.390236 1.379443 1.375282 1.312676 1.263120 1.260800 #> [201,] 0.7544885 1.380146 1.390250 1.379338 1.375177 1.312575 1.263023 1.260703 #> [202,] 0.7545463 1.380251 1.390143 1.379232 1.375072 1.312475 1.262926 1.260607 #> [203,] 0.7546040 1.380357 1.390037 1.379126 1.374966 1.312374 1.262830 1.260510 #> [204,] 0.7546618 1.380463 1.389931 1.379021 1.374861 1.312274 1.262733 1.260414 #> [205,] 0.7547196 1.380568 1.389824 1.378915 1.374756 1.312174 1.262636 1.260317 #> [206,] 0.7547774 1.380674 1.389718 1.378810 1.374651 1.312073 1.262540 1.260221 #> [207,] 0.7548352 1.380780 1.389611 1.378704 1.374545 1.311973 1.262443 1.260124 #> [208,] 0.7548930 1.380885 1.389505 1.378598 1.374440 1.311872 1.262346 1.260028 #> [209,] 0.7549508 1.380991 1.389399 1.378493 1.374335 1.311772 1.262250 1.259931 #> [210,] 0.7550086 1.381097 1.389292 1.378387 1.374230 1.311671 1.262153 1.259835 #> [211,] 0.7550664 1.381203 1.389186 1.378282 1.374124 1.311571 1.262056 1.259738 #> [212,] 0.7551242 1.381308 1.389080 1.378176 1.374019 1.311470 1.261960 1.259642 #> [213,] 0.7551820 1.381414 1.388973 1.378071 1.373914 1.311370 1.261863 1.259545 #> [214,] 0.7552399 1.381520 1.388867 1.377965 1.373809 1.311270 1.261766 1.259449 #> [215,] 0.7552977 1.381626 1.388760 1.377860 1.373704 1.311169 1.261670 1.259352 #> [216,] 0.7553555 1.381732 1.388654 1.377754 1.373598 1.311069 1.261573 1.259256 #> [217,] 0.7554134 1.381837 1.388548 1.377649 1.373493 1.310968 1.261477 1.259160 #> [218,] 0.7554712 1.381943 1.388441 1.377543 1.373388 1.310868 1.261380 1.259063 #> [219,] 0.7555290 1.382049 1.388335 1.377438 1.373283 1.310768 1.261284 1.258967 #> [220,] 0.7555869 1.382155 1.388229 1.377332 1.373178 1.310667 1.261187 1.258870 #> [221,] 0.7556448 1.382261 1.388123 1.377227 1.373073 1.310567 1.261090 1.258774 #> [222,] 0.7557026 1.382367 1.388016 1.377121 1.372968 1.310467 1.260994 1.258678 #> [223,] 0.7557605 1.382472 1.387910 1.377016 1.372862 1.310366 1.260897 1.258581 #> [224,] 0.7558183 1.382578 1.387804 1.376911 1.372757 1.310266 1.260801 1.258485 #> [225,] 0.7558762 1.382684 1.387698 1.376805 1.372652 1.310166 1.260704 1.258389 #> [226,] 0.7559341 1.382790 1.387591 1.376700 1.372547 1.310065 1.260608 1.258292 #> [227,] 0.7559920 1.382896 1.387485 1.376594 1.372442 1.309965 1.260511 1.258196 #> [228,] 0.7560499 1.383002 1.387379 1.376489 1.372337 1.309865 1.260415 1.258100 #> [229,] 0.7561078 1.383108 1.387273 1.376384 1.372232 1.309764 1.260318 1.258003 #> [230,] 0.7561657 1.383214 1.387166 1.376278 1.372127 1.309664 1.260222 1.257907 #> [231,] 0.7562236 1.383319 1.387060 1.376173 1.372022 1.309564 1.260125 1.257811 #> [232,] 0.7562815 1.383425 1.386954 1.376067 1.371917 1.309464 1.260029 1.257714 #> [233,] 0.7563394 1.383531 1.386848 1.375962 1.371812 1.309363 1.259932 1.257618 #> [234,] 0.7563973 1.383637 1.386742 1.375857 1.371707 1.309263 1.259836 1.257522 #> [235,] 0.7564552 1.383743 1.386635 1.375751 1.371602 1.309163 1.259739 1.257425 #> [236,] 0.7565131 1.383849 1.386529 1.375646 1.371497 1.309063 1.259643 1.257329 #> [237,] 0.7565711 1.383955 1.386423 1.375541 1.371392 1.308962 1.259546 1.257233 #> [238,] 0.7566290 1.384061 1.386317 1.375435 1.371287 1.308862 1.259450 1.257137 #> [239,] 0.7566869 1.384167 1.386211 1.375330 1.371182 1.308762 1.259354 1.257040 #> [240,] 0.7567449 1.384273 1.386105 1.375225 1.371077 1.308662 1.259257 1.256944 #> [241,] 0.7568028 1.384379 1.385999 1.375120 1.370972 1.308562 1.259161 1.256848 #> [242,] 0.7568608 1.384485 1.385892 1.375014 1.370867 1.308461 1.259064 1.256752 #> [243,] 0.7569187 1.384591 1.385786 1.374909 1.370762 1.308361 1.258968 1.256655 #> [244,] 0.7569767 1.384697 1.385680 1.374804 1.370657 1.308261 1.258871 1.256559 #> [245,] 0.7570346 1.384803 1.385574 1.374698 1.370552 1.308161 1.258775 1.256463 #> [246,] 0.7570926 1.384909 1.385468 1.374593 1.370447 1.308061 1.258679 1.256367 #> [247,] 0.7571506 1.385015 1.385362 1.374488 1.370342 1.307961 1.258582 1.256271 #> [248,] 0.7572085 1.385121 1.385256 1.374383 1.370237 1.307860 1.258486 1.256174 #> [249,] 0.7572665 1.385227 1.385150 1.374277 1.370132 1.307760 1.258390 1.256078 #> [250,] 0.7573245 1.385333 1.385044 1.374172 1.370027 1.307660 1.258293 1.255982 #> [251,] 0.7573825 1.385439 1.384938 1.374067 1.369922 1.307560 1.258197 1.255886 #> [252,] 0.7574405 1.385546 1.384832 1.373962 1.369817 1.307460 1.258101 1.255790 #> [253,] 0.7574985 1.385652 1.384726 1.373857 1.369713 1.307360 1.258004 1.255694 #> [254,] 0.7575565 1.385758 1.384620 1.373751 1.369608 1.307260 1.257908 1.255598 #> [255,] 0.7576145 1.385864 1.384514 1.373646 1.369503 1.307160 1.257812 1.255501 #> [256,] 0.7576725 1.385970 1.384408 1.373541 1.369398 1.307060 1.257715 1.255405 #> [257,] 0.7577305 1.386076 1.384302 1.373436 1.369293 1.306959 1.257619 1.255309 #> [258,] 0.7577885 1.386182 1.384196 1.373331 1.369188 1.306859 1.257523 1.255213 #> [259,] 0.7578466 1.386288 1.384090 1.373226 1.369083 1.306759 1.257426 1.255117 #> [260,] 0.7579046 1.386394 1.383984 1.373120 1.368979 1.306659 1.257330 1.255021 #> [261,] 0.7579626 1.386501 1.383878 1.373015 1.368874 1.306559 1.257234 1.254925 #> [262,] 0.7580207 1.386607 1.383772 1.372910 1.368769 1.306459 1.257138 1.254829 #> [263,] 0.7580787 1.386713 1.383666 1.372805 1.368664 1.306359 1.257041 1.254733 #> [264,] 0.7581368 1.386819 1.383560 1.372700 1.368559 1.306259 1.256945 1.254636 #> [265,] 0.7581948 1.386925 1.383454 1.372595 1.368455 1.306159 1.256849 1.254540 #> [266,] 0.7582529 1.387032 1.383348 1.372490 1.368350 1.306059 1.256753 1.254444 #> [267,] 0.7583109 1.387138 1.383242 1.372385 1.368245 1.305959 1.256656 1.254348 #> [268,] 0.7583690 1.387244 1.383136 1.372280 1.368140 1.305859 1.256560 1.254252 #> [269,] 0.7584271 1.387350 1.383030 1.372175 1.368036 1.305759 1.256464 1.254156 #> [270,] 0.7584851 1.387456 1.382924 1.372070 1.367931 1.305659 1.256368 1.254060 #> [271,] 0.7585432 1.387563 1.382818 1.371964 1.367826 1.305559 1.256272 1.253964 #> [272,] 0.7586013 1.387669 1.382713 1.371859 1.367721 1.305459 1.256175 1.253868 #> [273,] 0.7586594 1.387775 1.382607 1.371754 1.367617 1.305359 1.256079 1.253772 #> [274,] 0.7587175 1.387881 1.382501 1.371649 1.367512 1.305259 1.255983 1.253676 #> [275,] 0.7587756 1.387988 1.382395 1.371544 1.367407 1.305159 1.255887 1.253580 #> [276,] 0.7588337 1.388094 1.382289 1.371439 1.367303 1.305059 1.255791 1.253484 #> [277,] 0.7588918 1.388200 1.382183 1.371334 1.367198 1.304960 1.255695 1.253388 #> [278,] 0.7589499 1.388307 1.382078 1.371229 1.367093 1.304860 1.255599 1.253292 #> [279,] 0.7590080 1.388413 1.381972 1.371124 1.366989 1.304760 1.255502 1.253196 #> [280,] 0.7590661 1.388519 1.381866 1.371019 1.366884 1.304660 1.255406 1.253100 #> [281,] 0.7591242 1.388625 1.381760 1.370914 1.366779 1.304560 1.255310 1.253004 #> [282,] 0.7591824 1.388732 1.381654 1.370809 1.366675 1.304460 1.255214 1.252909 #> [283,] 0.7592405 1.388838 1.381549 1.370704 1.366570 1.304360 1.255118 1.252813 #> [284,] 0.7592986 1.388944 1.381443 1.370600 1.366465 1.304260 1.255022 1.252717 #> [285,] 0.7593568 1.389051 1.381337 1.370495 1.366361 1.304160 1.254926 1.252621 #> [286,] 0.7594149 1.389157 1.381231 1.370390 1.366256 1.304061 1.254830 1.252525 #> [287,] 0.7594731 1.389264 1.381125 1.370285 1.366151 1.303961 1.254734 1.252429 #> [288,] 0.7595312 1.389370 1.381020 1.370180 1.366047 1.303861 1.254638 1.252333 #> [289,] 0.7595894 1.389476 1.380914 1.370075 1.365942 1.303761 1.254541 1.252237 #> [290,] 0.7596475 1.389583 1.380808 1.369970 1.365838 1.303661 1.254445 1.252141 #> [291,] 0.7597057 1.389689 1.380703 1.369865 1.365733 1.303561 1.254349 1.252045 #> [292,] 0.7597639 1.389796 1.380597 1.369760 1.365629 1.303462 1.254253 1.251950 #> [293,] 0.7598220 1.389902 1.380491 1.369655 1.365524 1.303362 1.254157 1.251854 #> [294,] 0.7598802 1.390008 1.380385 1.369550 1.365419 1.303262 1.254061 1.251758 #> [295,] 0.7599384 1.390115 1.380280 1.369446 1.365315 1.303162 1.253965 1.251662 #> [296,] 0.7599966 1.390221 1.380174 1.369341 1.365210 1.303063 1.253869 1.251566 #> [297,] 0.7600548 1.390265 1.380068 1.369236 1.365106 1.302963 1.253773 1.251470 #> [298,] 0.7601130 1.390158 1.379963 1.369131 1.365001 1.302863 1.253677 1.251375 #> [299,] 0.7601712 1.390052 1.379857 1.369026 1.364897 1.302763 1.253581 1.251279 #> [300,] 0.7602294 1.389946 1.379751 1.368921 1.364792 1.302663 1.253485 1.251183 #> [301,] 0.7602876 1.389839 1.379646 1.368817 1.364688 1.302564 1.253389 1.251087 #> [302,] 0.7603458 1.389733 1.379540 1.368712 1.364583 1.302464 1.253293 1.250991 #> [303,] 0.7604040 1.389626 1.379434 1.368607 1.364479 1.302364 1.253197 1.250896 #> [304,] 0.7604623 1.389520 1.379329 1.368502 1.364374 1.302265 1.253101 1.250800 #> [305,] 0.7605205 1.389414 1.379223 1.368397 1.364270 1.302165 1.253005 1.250704 #> [306,] 0.7605787 1.389307 1.379118 1.368293 1.364165 1.302065 1.252910 1.250608 #> [307,] 0.7606370 1.389201 1.379012 1.368188 1.364061 1.301965 1.252814 1.250512 #> [308,] 0.7606952 1.389094 1.378906 1.368083 1.363957 1.301866 1.252718 1.250417 #> [309,] 0.7608117 1.388882 1.378695 1.367874 1.363748 1.301666 1.252526 1.250225 #> [310,] 0.7608700 1.388775 1.378590 1.367769 1.363643 1.301567 1.252430 1.250130 #> [311,] 0.7609282 1.388669 1.378484 1.367664 1.363539 1.301467 1.252334 1.250034 #> [312,] 0.7609865 1.388563 1.378379 1.367560 1.363434 1.301367 1.252238 1.249938 #> [313,] 0.7610448 1.388456 1.378273 1.367455 1.363330 1.301268 1.252142 1.249842 #> [314,] 0.7611030 1.388350 1.378168 1.367350 1.363226 1.301168 1.252046 1.249747 #> [315,] 0.7611613 1.388244 1.378062 1.367245 1.363121 1.301069 1.251951 1.249651 #> [316,] 0.7612196 1.388138 1.377957 1.367141 1.363017 1.300969 1.251855 1.249555 #> [317,] 0.7612779 1.388031 1.377851 1.367036 1.362913 1.300869 1.251759 1.249460 #> [318,] 0.7607535 1.388988 1.378801 1.367978 1.363852 1.301766 1.252622 1.250321 #> [319,] 0.7145846 1.307151 1.316809 1.327227 1.331242 1.385872 1.333553 1.331104 #> [320,] 0.7146393 1.307252 1.316910 1.327328 1.331344 1.385766 1.333451 1.331002 #> [321,] 0.7146940 1.307352 1.317011 1.327430 1.331446 1.385660 1.333349 1.330900 #> [322,] 0.7145298 1.307051 1.316708 1.327125 1.331140 1.385979 1.333655 1.331205 #> [323,] 0.7136550 1.305451 1.315096 1.325500 1.329511 1.387678 1.335290 1.332837 #> [324,] 0.7137097 1.305551 1.315197 1.325602 1.329612 1.387571 1.335188 1.332735 #> [325,] 0.7137643 1.305651 1.315298 1.325703 1.329714 1.387465 1.335085 1.332633 #> [326,] 0.7138190 1.305751 1.315398 1.325805 1.329816 1.387359 1.334983 1.332531 #> [327,] 0.7138736 1.305851 1.315499 1.325906 1.329918 1.387253 1.334881 1.332429 #> [328,] 0.7139283 1.305951 1.315600 1.326008 1.330020 1.387146 1.334779 1.332327 #> [329,] 0.7139829 1.306051 1.315701 1.326109 1.330122 1.387040 1.334677 1.332225 #> [330,] 0.7144204 1.306851 1.316507 1.326922 1.330937 1.386191 1.333859 1.331409 #> [331,] 0.7144751 1.306951 1.316608 1.327024 1.331039 1.386085 1.333757 1.331307 #> [332,] 0.7133819 1.304951 1.314593 1.324993 1.329002 1.388209 1.335801 1.333348 #> [333,] 0.7134365 1.305051 1.314694 1.325095 1.329104 1.388103 1.335699 1.333246 #> [334,] 0.7134911 1.305151 1.314794 1.325196 1.329205 1.387996 1.335597 1.333143 #> [335,] 0.7136004 1.305351 1.314996 1.325399 1.329409 1.387784 1.335392 1.332939 #> [336,] 0.7140376 1.306151 1.315801 1.326211 1.330223 1.386934 1.334574 1.332123 #> [337,] 0.7140923 1.306251 1.315902 1.326313 1.330325 1.386828 1.334472 1.332021 #> [338,] 0.7141470 1.306351 1.316003 1.326414 1.330427 1.386722 1.334370 1.331919 #> [339,] 0.7142017 1.306451 1.316104 1.326516 1.330529 1.386615 1.334268 1.331817 #> [340,] 0.7142563 1.306551 1.316204 1.326617 1.330631 1.386509 1.334166 1.331715 #> [341,] 0.7143110 1.306651 1.316305 1.326719 1.330733 1.386403 1.334064 1.331613 #> [342,] 0.7143657 1.306751 1.316406 1.326820 1.330835 1.386297 1.333961 1.331511 #> [343,] 0.7135457 1.305251 1.314895 1.325297 1.329307 1.387890 1.335494 1.333041 #> [344,] 0.7116658 1.301812 1.311431 1.321806 1.325805 1.389037 1.339022 1.336563 #> [345,] 0.7117748 1.302012 1.311632 1.322008 1.326008 1.389250 1.338817 1.336358 #> [346,] 0.7118293 1.302111 1.311732 1.322109 1.326110 1.389357 1.338715 1.336256 #> [347,] 0.7118838 1.302211 1.311832 1.322211 1.326211 1.389463 1.338612 1.336153 #> [348,] 0.7117203 1.301912 1.311531 1.321907 1.325906 1.389144 1.338920 1.336460 #> [349,] 0.7119929 1.302411 1.312033 1.322413 1.326414 1.389676 1.338407 1.335949 #> [350,] 0.7120474 1.302510 1.312134 1.322514 1.326516 1.389782 1.338305 1.335847 #> [351,] 0.7121019 1.302610 1.312234 1.322616 1.326617 1.389889 1.338202 1.335744 #> [352,] 0.7121564 1.302710 1.312335 1.322717 1.326719 1.389995 1.338100 1.335642 #> [353,] 0.7122110 1.302810 1.312435 1.322818 1.326820 1.390101 1.337997 1.335540 #> [354,] 0.7122655 1.302909 1.312536 1.322920 1.326922 1.390208 1.337895 1.335437 #> [355,] 0.7123200 1.303009 1.312636 1.323021 1.327024 1.390278 1.337792 1.335335 #> [356,] 0.7123746 1.303109 1.312737 1.323122 1.327125 1.390172 1.337690 1.335233 #> [357,] 0.7124291 1.303209 1.312837 1.323224 1.327227 1.390065 1.337588 1.335131 #> [358,] 0.7124837 1.303308 1.312938 1.323325 1.327329 1.389959 1.337485 1.335029 #> [359,] 0.7125382 1.303408 1.313038 1.323426 1.327430 1.389853 1.337383 1.334926 #> [360,] 0.7125928 1.303508 1.313139 1.323527 1.327532 1.389746 1.337280 1.334824 #> [361,] 0.7119384 1.302311 1.311933 1.322312 1.326313 1.389569 1.338510 1.336051 #> [362,] 0.7112301 1.301015 1.310628 1.320996 1.324993 1.388187 1.339843 1.337382 #> [363,] 0.7112845 1.301115 1.310728 1.321098 1.325095 1.388293 1.339740 1.337279 #> [364,] 0.7113390 1.301214 1.310828 1.321199 1.325196 1.388399 1.339637 1.337177 #> [365,] 0.7113934 1.301314 1.310929 1.321300 1.325297 1.388506 1.339535 1.337074 #> [366,] 0.7114479 1.301414 1.311029 1.321401 1.325399 1.388612 1.339432 1.336972 #> [367,] 0.7115024 1.301513 1.311130 1.321502 1.325500 1.388718 1.339330 1.336870 #> [368,] 0.7115569 1.301613 1.311230 1.321603 1.325602 1.388825 1.339227 1.336767 #> [369,] 0.7116114 1.301713 1.311330 1.321705 1.325703 1.388931 1.339125 1.336665 #> [370,] 0.7130840 1.304407 1.314044 1.324440 1.328447 1.388789 1.336359 1.333905 #> [371,] 0.7131386 1.304506 1.314145 1.324541 1.328549 1.388682 1.336257 1.333802 #> [372,] 0.7131932 1.304606 1.314245 1.324643 1.328650 1.388576 1.336155 1.333700 #> [373,] 0.7132478 1.304706 1.314346 1.324744 1.328752 1.388470 1.336052 1.333598 #> [374,] 0.7133024 1.304806 1.314447 1.324846 1.328854 1.388363 1.335950 1.333496 #> [375,] 0.7126474 1.303608 1.313239 1.323629 1.327633 1.389640 1.337178 1.334722 #> [376,] 0.7127019 1.303708 1.313340 1.323730 1.327735 1.389533 1.337076 1.334620 #> [377,] 0.7127565 1.303807 1.313441 1.323832 1.327837 1.389427 1.336973 1.334518 #> [378,] 0.7128111 1.303907 1.313541 1.323933 1.327938 1.389321 1.336871 1.334415 #> [379,] 0.7128657 1.304007 1.313642 1.324034 1.328040 1.389214 1.336768 1.334313 #> [380,] 0.7129202 1.304107 1.313742 1.324136 1.328142 1.389108 1.336666 1.334211 #> [381,] 0.7129748 1.304207 1.313843 1.324237 1.328244 1.389001 1.336564 1.334109 #> [382,] 0.7130294 1.304307 1.313943 1.324338 1.328345 1.388895 1.336461 1.334007 #> [383,] 0.6807122 1.245191 1.254390 1.264314 1.268140 1.328622 1.380748 1.383289 #> [384,] 0.6807643 1.245286 1.254487 1.264411 1.268237 1.328723 1.380854 1.383394 #> [385,] 0.6808164 1.245381 1.254583 1.264508 1.268334 1.328825 1.380959 1.383500 #> [386,] 0.6808686 1.245477 1.254679 1.264605 1.268431 1.328927 1.381065 1.383606 #> [387,] 0.6809207 1.245572 1.254775 1.264702 1.268528 1.329029 1.381171 1.383712 #> [388,] 0.6844228 1.251978 1.261228 1.271206 1.275052 1.335864 1.388274 1.389764 #> [389,] 0.6844752 1.252074 1.261325 1.271304 1.275150 1.335967 1.388381 1.389657 #> [390,] 0.6845277 1.252170 1.261421 1.271401 1.275248 1.336069 1.388487 1.389551 #> [391,] 0.6845801 1.252266 1.261518 1.271498 1.275345 1.336171 1.388593 1.389445 #> [392,] 0.6846325 1.252362 1.261615 1.271596 1.275443 1.336273 1.388700 1.389338 #> [393,] 0.6846849 1.252458 1.261711 1.271693 1.275541 1.336376 1.388806 1.389232 #> [394,] 0.6847373 1.252554 1.261808 1.271790 1.275638 1.336478 1.388912 1.389125 #> [395,] 0.6847898 1.252649 1.261904 1.271888 1.275736 1.336580 1.389019 1.389019 #> [396,] 0.6848422 1.252745 1.262001 1.271985 1.275834 1.336683 1.389125 1.388913 #> [397,] 0.6848946 1.252841 1.262098 1.272083 1.275931 1.336785 1.389231 1.388806 #> [398,] 0.6849471 1.252937 1.262194 1.272180 1.276029 1.336887 1.389338 1.388700 #> [399,] 0.6849995 1.253033 1.262291 1.272277 1.276127 1.336990 1.389444 1.388594 #> [400,] 0.6850520 1.253129 1.262388 1.272375 1.276224 1.337092 1.389551 1.388487 #> [401,] 0.6802433 1.244333 1.253526 1.263443 1.267266 1.327706 1.379797 1.382336 #> [402,] 0.6802954 1.244428 1.253622 1.263540 1.267363 1.327808 1.379902 1.382442 #> [403,] 0.6803474 1.244523 1.253718 1.263637 1.267460 1.327910 1.380008 1.382547 #> [404,] 0.6803995 1.244619 1.253814 1.263734 1.267557 1.328012 1.380114 1.382653 #> [405,] 0.6804516 1.244714 1.253910 1.263830 1.267654 1.328113 1.380219 1.382759 #> [406,] 0.6805037 1.244809 1.254006 1.263927 1.267751 1.328215 1.380325 1.382865 #> [407,] 0.6805558 1.244905 1.254102 1.264024 1.267848 1.328317 1.380431 1.382971 #> [408,] 0.6806080 1.245000 1.254198 1.264121 1.267945 1.328418 1.380536 1.383077 #> [409,] 0.6806601 1.245095 1.254294 1.264218 1.268042 1.328520 1.380642 1.383183 #> [410,] 0.6862595 1.255338 1.264613 1.274618 1.278474 1.339449 1.388595 1.386044 #> [411,] 0.6863120 1.255434 1.264710 1.274715 1.278572 1.339552 1.388489 1.385938 #> [412,] 0.6863646 1.255530 1.264806 1.274813 1.278670 1.339654 1.388382 1.385832 #> [413,] 0.6864171 1.255626 1.264903 1.274910 1.278768 1.339757 1.388276 1.385726 #> [414,] 0.6864697 1.255722 1.265000 1.275008 1.278865 1.339859 1.388170 1.385620 #> [415,] 0.6865222 1.255819 1.265097 1.275106 1.278963 1.339962 1.388063 1.385514 #> [416,] 0.6865748 1.255915 1.265194 1.275203 1.279061 1.340064 1.387957 1.385408 #> [417,] 0.6866274 1.256011 1.265291 1.275301 1.279159 1.340167 1.387851 1.385302 #> [418,] 0.6866800 1.256107 1.265388 1.275399 1.279257 1.340270 1.387745 1.385196 #> [419,] 0.6867325 1.256203 1.265485 1.275496 1.279355 1.340372 1.387638 1.385090 #> [420,] 0.6867851 1.256299 1.265581 1.275594 1.279453 1.340475 1.387532 1.384983 #> [421,] 0.6868377 1.256396 1.265678 1.275692 1.279551 1.340578 1.387426 1.384877 #> [422,] 0.6868903 1.256492 1.265775 1.275789 1.279649 1.340680 1.387320 1.384771 #> [423,] 0.6869429 1.256588 1.265872 1.275887 1.279747 1.340783 1.387213 1.384665 #> [424,] 0.6869955 1.256684 1.265969 1.275985 1.279845 1.340886 1.387107 1.384559 #> [425,] 0.6870481 1.256780 1.266066 1.276082 1.279943 1.340988 1.387001 1.384453 #> [426,] 0.6871007 1.256877 1.266163 1.276180 1.280041 1.341091 1.386895 1.384347 #> [427,] 0.6871533 1.256973 1.266260 1.276278 1.280139 1.341194 1.386789 1.384241 #> [428,] 0.6872059 1.257069 1.266357 1.276375 1.280237 1.341296 1.386682 1.384135 #> [429,] 0.6872585 1.257165 1.266454 1.276473 1.280335 1.341399 1.386576 1.384029 #> [430,] 0.6873112 1.257262 1.266551 1.276571 1.280433 1.341502 1.386470 1.383923 #> [431,] 0.6873638 1.257358 1.266648 1.276669 1.280531 1.341604 1.386364 1.383817 #> [432,] 0.6874164 1.257454 1.266745 1.276766 1.280629 1.341707 1.386258 1.383712 #> [433,] 0.6874691 1.257551 1.266842 1.276864 1.280727 1.341810 1.386152 1.383606 #> [434,] 0.6875217 1.257647 1.266939 1.276962 1.280825 1.341913 1.386045 1.383500 #> [435,] 0.6875744 1.257743 1.267036 1.277060 1.280923 1.342015 1.385939 1.383394 #> [436,] 0.6876270 1.257839 1.267133 1.277158 1.281022 1.342118 1.385833 1.383288 #> [437,] 0.6876797 1.257936 1.267230 1.277255 1.281120 1.342221 1.385727 1.383182 #> [438,] 0.6877323 1.258032 1.267327 1.277353 1.281218 1.342324 1.385621 1.383076 #> [439,] 0.6877850 1.258128 1.267424 1.277451 1.281316 1.342426 1.385515 1.382970 #> [440,] 0.6878376 1.258225 1.267521 1.277549 1.281414 1.342529 1.385409 1.382864 #> [441,] 0.6878903 1.258321 1.267618 1.277647 1.281512 1.342632 1.385303 1.382758 #> [442,] 0.6879430 1.258417 1.267715 1.277744 1.281610 1.342735 1.385197 1.382652 #> [443,] 0.6879956 1.258514 1.267812 1.277842 1.281708 1.342838 1.385091 1.382547 #> [444,] 0.6880483 1.258610 1.267909 1.277940 1.281806 1.342941 1.384985 1.382441 #> [445,] 0.6881010 1.258707 1.268006 1.278038 1.281905 1.343043 1.384879 1.382335 #> [446,] 0.6881537 1.258803 1.268103 1.278136 1.282003 1.343146 1.384773 1.382229 #> [447,] 0.6882064 1.258899 1.268201 1.278234 1.282101 1.343249 1.384666 1.382123 #> [448,] 0.6882591 1.258996 1.268298 1.278332 1.282199 1.343352 1.384560 1.382017 #> [449,] 0.6883118 1.259092 1.268395 1.278429 1.282297 1.343455 1.384454 1.381912 #> [450,] 0.6883645 1.259188 1.268492 1.278527 1.282395 1.343558 1.384348 1.381806 #> [451,] 0.6884172 1.259285 1.268589 1.278625 1.282494 1.343660 1.384242 1.381700 #> [452,] 0.6884699 1.259381 1.268686 1.278723 1.282592 1.343763 1.384136 1.381594 #> [453,] 0.6885226 1.259478 1.268783 1.278821 1.282690 1.343866 1.384031 1.381488 #> [454,] 0.6885754 1.259574 1.268880 1.278919 1.282788 1.343969 1.383925 1.381383 #> [455,] 0.6886281 1.259671 1.268978 1.279017 1.282887 1.344072 1.383819 1.381277 #> [456,] 0.6886808 1.259767 1.269075 1.279115 1.282985 1.344175 1.383713 1.381171 #> [457,] 0.6887335 1.259864 1.269172 1.279213 1.283083 1.344278 1.383607 1.381065 #> [458,] 0.6887863 1.259960 1.269269 1.279311 1.283181 1.344381 1.383501 1.380960 #> [459,] 0.6888390 1.260056 1.269366 1.279409 1.283279 1.344484 1.383395 1.380854 #> [460,] 0.6835849 1.250445 1.259684 1.269650 1.273491 1.334229 1.386575 1.389126 #> [461,] 0.6836372 1.250541 1.259781 1.269747 1.273589 1.334331 1.386681 1.389233 #> [462,] 0.6836896 1.250637 1.259877 1.269844 1.273686 1.334433 1.386787 1.389339 #> [463,] 0.6837419 1.250733 1.259974 1.269942 1.273784 1.334535 1.386893 1.389445 #> [464,] 0.6837943 1.250828 1.260070 1.270039 1.273881 1.334637 1.386999 1.389552 #> [465,] 0.6838466 1.250924 1.260167 1.270136 1.273979 1.334740 1.387106 1.389658 #> [466,] 0.6838990 1.251020 1.260263 1.270233 1.274076 1.334842 1.387212 1.389765 #> [467,] 0.6839514 1.251116 1.260360 1.270331 1.274174 1.334944 1.387318 1.389871 #> [468,] 0.6888918 1.260153 1.269464 1.279507 1.283378 1.344587 1.383289 1.380748 #> [469,] 0.6889445 1.260249 1.269561 1.279605 1.283476 1.344690 1.383183 1.380642 #> [470,] 0.6889973 1.260346 1.269658 1.279703 1.283574 1.344793 1.383077 1.380537 #> [471,] 0.6841609 1.251499 1.260746 1.270720 1.274564 1.335353 1.387743 1.390296 #> [472,] 0.6842132 1.251595 1.260842 1.270817 1.274662 1.335455 1.387849 1.390189 #> [473,] 0.6842656 1.251691 1.260939 1.270914 1.274759 1.335557 1.387956 1.390083 #> [474,] 0.6843180 1.251786 1.261035 1.271012 1.274857 1.335660 1.388062 1.389977 #> [475,] 0.6843704 1.251882 1.261132 1.271109 1.274955 1.335762 1.388168 1.389870 #> [476,] 0.6890500 1.260442 1.269755 1.279801 1.283673 1.344896 1.382971 1.380431 #> [477,] 0.6891028 1.260539 1.269852 1.279899 1.283771 1.344999 1.382865 1.380325 #> [478,] 0.6891555 1.260636 1.269950 1.279997 1.283869 1.345102 1.382759 1.380220 #> [479,] 0.6892083 1.260732 1.270047 1.280095 1.283967 1.345205 1.382654 1.380114 #> [480,] 0.6892611 1.260829 1.270144 1.280193 1.284066 1.345308 1.382548 1.380008 #> [481,] 0.6893139 1.260925 1.270241 1.280291 1.284164 1.345411 1.382442 1.379903 #> [482,] 0.6893666 1.261022 1.270339 1.280389 1.284262 1.345514 1.382336 1.379797 #> [483,] 0.6894194 1.261118 1.270436 1.280487 1.284361 1.345617 1.382230 1.379691 #> [484,] 0.6894722 1.261215 1.270533 1.280585 1.284459 1.345720 1.382124 1.379586 #> [485,] 0.6895250 1.261311 1.270630 1.280683 1.284557 1.345823 1.382019 1.379480 #> [486,] 0.6895778 1.261408 1.270728 1.280781 1.284656 1.345926 1.381913 1.379374 #> [487,] 0.6896306 1.261505 1.270825 1.280879 1.284754 1.346029 1.381807 1.379269 #> [488,] 0.6896834 1.261601 1.270922 1.280977 1.284853 1.346132 1.381701 1.379163 #> [489,] 0.6897362 1.261698 1.271020 1.281075 1.284951 1.346235 1.381595 1.379058 #> [490,] 0.6897890 1.261794 1.271117 1.281173 1.285049 1.346338 1.381490 1.378952 #> [491,] 0.6898419 1.261891 1.271214 1.281271 1.285148 1.346441 1.381384 1.378846 #> [492,] 0.6898947 1.261988 1.271312 1.281369 1.285246 1.346544 1.381278 1.378741 #> [493,] 0.6899475 1.262084 1.271409 1.281467 1.285345 1.346647 1.381172 1.378635 #> [494,] 0.6900003 1.262181 1.271506 1.281566 1.285443 1.346750 1.381066 1.378530 #> [495,] 0.6811814 1.246049 1.255255 1.265186 1.269014 1.329538 1.381700 1.384242 #> [496,] 0.6812336 1.246144 1.255351 1.265283 1.269111 1.329639 1.381805 1.384348 #> [497,] 0.6812858 1.246240 1.255447 1.265380 1.269208 1.329741 1.381911 1.384454 #> [498,] 0.6813379 1.246335 1.255544 1.265477 1.269305 1.329843 1.382017 1.384560 #> [499,] 0.6813901 1.246431 1.255640 1.265573 1.269402 1.329945 1.382123 1.384666 #> [500,] 0.6814423 1.246526 1.255736 1.265670 1.269500 1.330047 1.382229 1.384772 #> [501,] 0.6814945 1.246621 1.255832 1.265767 1.269597 1.330149 1.382335 1.384878 #> [502,] 0.6815466 1.246717 1.255928 1.265864 1.269694 1.330250 1.382440 1.384984 #> [503,] 0.6815988 1.246812 1.256024 1.265961 1.269791 1.330352 1.382546 1.385090 #> [504,] 0.6816510 1.246908 1.256121 1.266058 1.269889 1.330454 1.382652 1.385196 #> [505,] 0.6817032 1.247003 1.256217 1.266155 1.269986 1.330556 1.382758 1.385302 #> [506,] 0.6817554 1.247099 1.256313 1.266252 1.270083 1.330658 1.382864 1.385409 #> [507,] 0.6818076 1.247194 1.256409 1.266349 1.270180 1.330760 1.382970 1.385515 #> [508,] 0.6818598 1.247290 1.256505 1.266446 1.270278 1.330862 1.383076 1.385621 #> [509,] 0.6851044 1.253225 1.262484 1.272472 1.276322 1.337195 1.389657 1.388381 #> [510,] 0.6851569 1.253321 1.262581 1.272570 1.276420 1.337297 1.389763 1.388275 #> [511,] 0.6852094 1.253417 1.262678 1.272667 1.276518 1.337399 1.389870 1.388168 #> [512,] 0.6852618 1.253513 1.262774 1.272765 1.276615 1.337502 1.389976 1.388062 #> [513,] 0.6853143 1.253609 1.262871 1.272862 1.276713 1.337604 1.390083 1.387956 #> [514,] 0.6853668 1.253705 1.262968 1.272959 1.276811 1.337707 1.390189 1.387850 #> [515,] 0.6854192 1.253801 1.263064 1.273057 1.276909 1.337809 1.390296 1.387743 #> [516,] 0.6854717 1.253897 1.263161 1.273154 1.277006 1.337911 1.390191 1.387637 #> [517,] 0.6855242 1.253993 1.263258 1.273252 1.277104 1.338014 1.390084 1.387531 #> [518,] 0.6855767 1.254089 1.263355 1.273349 1.277202 1.338116 1.389978 1.387425 #> [519,] 0.6856292 1.254185 1.263451 1.273447 1.277300 1.338219 1.389871 1.387318 #> [520,] 0.6856817 1.254281 1.263548 1.273544 1.277398 1.338321 1.389765 1.387212 #> [521,] 0.6857342 1.254377 1.263645 1.273642 1.277495 1.338424 1.389658 1.387106 #> [522,] 0.6857867 1.254473 1.263742 1.273739 1.277593 1.338526 1.389552 1.387000 #> [523,] 0.6858392 1.254569 1.263838 1.273837 1.277691 1.338629 1.389446 1.386894 #> [524,] 0.6858917 1.254665 1.263935 1.273935 1.277789 1.338731 1.389339 1.386787 #> [525,] 0.6859443 1.254761 1.264032 1.274032 1.277887 1.338834 1.389233 1.386681 #> [526,] 0.6859968 1.254857 1.264129 1.274130 1.277985 1.338936 1.389127 1.386575 #> [527,] 0.6860493 1.254953 1.264226 1.274227 1.278082 1.339039 1.389020 1.386469 #> [528,] 0.6861018 1.255050 1.264322 1.274325 1.278180 1.339141 1.388914 1.386363 #> [529,] 0.6861544 1.255146 1.264419 1.274422 1.278278 1.339244 1.388807 1.386257 #> [530,] 0.6862069 1.255242 1.264516 1.274520 1.278376 1.339346 1.388701 1.386150 #> [531,] 0.6809728 1.245667 1.254871 1.264798 1.268625 1.329130 1.381277 1.383818 #> [532,] 0.6810250 1.245763 1.254967 1.264895 1.268722 1.329232 1.381382 1.383924 #> [533,] 0.6810771 1.245858 1.255063 1.264992 1.268819 1.329334 1.381488 1.384030 #> [534,] 0.6811293 1.245953 1.255159 1.265089 1.268917 1.329436 1.381594 1.384136 #> [535,] 0.6832709 1.249871 1.259106 1.269067 1.272906 1.333616 1.385938 1.388488 #> [536,] 0.6833232 1.249967 1.259202 1.269164 1.273004 1.333718 1.386044 1.388594 #> [537,] 0.6833755 1.250062 1.259298 1.269261 1.273101 1.333820 1.386150 1.388701 #> [538,] 0.6834279 1.250158 1.259395 1.269358 1.273199 1.333922 1.386256 1.388807 #> [539,] 0.6834802 1.250254 1.259491 1.269455 1.273296 1.334024 1.386362 1.388913 #> [540,] 0.6835325 1.250350 1.259588 1.269553 1.273394 1.334127 1.386469 1.389020 #> [541,] 0.6821731 1.247863 1.257083 1.267028 1.270861 1.331473 1.383711 1.386257 #> [542,] 0.6822254 1.247959 1.257179 1.267125 1.270959 1.331575 1.383817 1.386364 #> [543,] 0.6822776 1.248054 1.257275 1.267222 1.271056 1.331677 1.383923 1.386470 #> [544,] 0.6823299 1.248150 1.257371 1.267319 1.271153 1.331779 1.384029 1.386576 #> [545,] 0.6823821 1.248245 1.257468 1.267416 1.271250 1.331881 1.384135 1.386682 #> [546,] 0.6824343 1.248341 1.257564 1.267513 1.271348 1.331983 1.384241 1.386788 #> [547,] 0.6824866 1.248436 1.257660 1.267610 1.271445 1.332085 1.384347 1.386894 #> [548,] 0.6825389 1.248532 1.257757 1.267707 1.271543 1.332187 1.384453 1.387001 #> [549,] 0.6819120 1.247385 1.256602 1.266543 1.270375 1.330964 1.383182 1.385727 #> [550,] 0.6819642 1.247481 1.256698 1.266640 1.270472 1.331066 1.383287 1.385833 #> [551,] 0.6820165 1.247576 1.256794 1.266737 1.270569 1.331167 1.383393 1.385939 #> [552,] 0.6820687 1.247672 1.256890 1.266834 1.270667 1.331269 1.383499 1.386045 #> [553,] 0.6821209 1.247767 1.256986 1.266931 1.270764 1.331371 1.383605 1.386151 #> [554,] 0.6828525 1.249106 1.258335 1.268290 1.272127 1.332799 1.385089 1.387638 #> [555,] 0.6829048 1.249201 1.258431 1.268387 1.272224 1.332901 1.385195 1.387744 #> [556,] 0.6829571 1.249297 1.258527 1.268484 1.272322 1.333003 1.385301 1.387850 #> [557,] 0.6830094 1.249393 1.258624 1.268581 1.272419 1.333105 1.385407 1.387957 #> [558,] 0.6830617 1.249488 1.258720 1.268678 1.272517 1.333207 1.385513 1.388063 #> [559,] 0.6831140 1.249584 1.258816 1.268775 1.272614 1.333310 1.385620 1.388169 #> [560,] 0.6831663 1.249680 1.258913 1.268872 1.272711 1.333412 1.385726 1.388276 #> [561,] 0.6832186 1.249775 1.259009 1.268970 1.272809 1.333514 1.385832 1.388382 #> [562,] 0.6825911 1.248628 1.257853 1.267804 1.271640 1.332289 1.384559 1.387107 #> [563,] 0.6826434 1.248723 1.257949 1.267901 1.271737 1.332391 1.384665 1.387213 #> [564,] 0.6826957 1.248819 1.258046 1.267998 1.271835 1.332493 1.384771 1.387319 #> [565,] 0.6827479 1.248914 1.258142 1.268095 1.271932 1.332595 1.384877 1.387425 #> [566,] 0.6828002 1.249010 1.258238 1.268193 1.272029 1.332697 1.384983 1.387532 #> [567,] 0.6840037 1.251212 1.260456 1.270428 1.274272 1.335046 1.387424 1.389977 #> [568,] 0.6840561 1.251307 1.260553 1.270525 1.274369 1.335148 1.387531 1.390084 #> [569,] 0.6841085 1.251403 1.260649 1.270622 1.274467 1.335251 1.387637 1.390190 #> [570,] 0.6080655 1.112302 1.120520 1.129385 1.132802 1.186829 1.233392 1.235662 #> [571,] 0.6082518 1.112643 1.120863 1.129731 1.133149 1.187193 1.233770 1.236040 #> [572,] 0.6081121 1.112387 1.120606 1.129471 1.132888 1.186920 1.233487 1.235756 #> [573,] 0.6081586 1.112472 1.120692 1.129558 1.132975 1.187011 1.233581 1.235851 #> [574,] 0.6082052 1.112557 1.120777 1.129644 1.133062 1.187102 1.233676 1.235946 #> [575,] 0.6077862 1.111791 1.120005 1.128866 1.132281 1.186284 1.232826 1.235094 #> [576,] 0.6082983 1.112728 1.120949 1.129817 1.133235 1.187284 1.233864 1.236135 #> [577,] 0.6083449 1.112813 1.121035 1.129904 1.133322 1.187374 1.233959 1.236230 #> [578,] 0.6083915 1.112898 1.121121 1.129990 1.133409 1.187465 1.234053 1.236324 #> [579,] 0.6079724 1.112131 1.120348 1.129212 1.132628 1.186647 1.233203 1.235473 #> [580,] 0.6080189 1.112217 1.120434 1.129298 1.132715 1.186738 1.233298 1.235567 #> [581,] 0.6096039 1.115116 1.123355 1.132242 1.135668 1.189832 1.236513 1.238788 #> [582,] 0.6096505 1.115201 1.123441 1.132329 1.135755 1.189923 1.236607 1.238883 #> [583,] 0.6087177 1.113495 1.121722 1.130596 1.134017 1.188102 1.234715 1.236987 #> [584,] 0.6087643 1.113580 1.121808 1.130683 1.134103 1.188193 1.234810 1.237082 #> [585,] 0.6088109 1.113665 1.121894 1.130769 1.134190 1.188284 1.234904 1.237177 #> [586,] 0.6088575 1.113751 1.121979 1.130856 1.134277 1.188375 1.234999 1.237271 #> [587,] 0.6089041 1.113836 1.122065 1.130942 1.134364 1.188466 1.235093 1.237366 #> [588,] 0.6089508 1.113921 1.122151 1.131029 1.134451 1.188557 1.235188 1.237461 #> [589,] 0.6078328 1.111876 1.120091 1.128952 1.132368 1.186375 1.232920 1.235189 #> [590,] 0.6078793 1.111961 1.120177 1.129039 1.132455 1.186466 1.233015 1.235283 #> [591,] 0.6079258 1.112046 1.120263 1.129125 1.132541 1.186557 1.233109 1.235378 #> [592,] 0.6085313 1.113154 1.121378 1.130250 1.133669 1.187738 1.234337 1.236608 #> [593,] 0.6085779 1.113239 1.121464 1.130336 1.133756 1.187829 1.234431 1.236703 #> [594,] 0.6086245 1.113324 1.121550 1.130423 1.133843 1.187920 1.234526 1.236798 #> [595,] 0.6086711 1.113410 1.121636 1.130509 1.133930 1.188011 1.234621 1.236892 #> [596,] 0.6093239 1.114604 1.122839 1.131722 1.135146 1.189285 1.235945 1.238219 #> [597,] 0.6093705 1.114689 1.122925 1.131809 1.135233 1.189376 1.236039 1.238314 #> [598,] 0.6094172 1.114774 1.123011 1.131895 1.135320 1.189467 1.236134 1.238409 #> [599,] 0.6094639 1.114860 1.123097 1.131982 1.135407 1.189558 1.236229 1.238503 #> [600,] 0.6095105 1.114945 1.123183 1.132069 1.135494 1.189650 1.236323 1.238598 #> [601,] 0.6095572 1.115030 1.123269 1.132155 1.135581 1.189741 1.236418 1.238693 #> [602,] 0.6089974 1.114006 1.122237 1.131116 1.134538 1.188648 1.235282 1.237556 #> [603,] 0.6090440 1.114092 1.122323 1.131202 1.134625 1.188739 1.235377 1.237650 #> [604,] 0.6090906 1.114177 1.122409 1.131289 1.134711 1.188830 1.235472 1.237745 #> [605,] 0.6091373 1.114262 1.122495 1.131375 1.134798 1.188921 1.235566 1.237840 #> [606,] 0.6091839 1.114348 1.122581 1.131462 1.134885 1.189012 1.235661 1.237935 #> [607,] 0.6092306 1.114433 1.122667 1.131549 1.134972 1.189103 1.235755 1.238029 #> [608,] 0.6092772 1.114518 1.122753 1.131635 1.135059 1.189194 1.235850 1.238124 #> [609,] 0.6084381 1.112983 1.121207 1.130077 1.133496 1.187556 1.234148 1.236419 #> [610,] 0.6084847 1.113069 1.121292 1.130163 1.133583 1.187647 1.234242 1.236514 #> [611,] 0.5790742 1.059270 1.067096 1.075538 1.078792 1.130244 1.174587 1.176748 #> [612,] 0.5791186 1.059351 1.067178 1.075620 1.078875 1.130330 1.174677 1.176838 #> [613,] 0.5789856 1.059107 1.066933 1.075373 1.078627 1.130071 1.174407 1.176568 #> [614,] 0.5790299 1.059189 1.067014 1.075456 1.078710 1.130157 1.174497 1.176658 #> [615,] 0.5802726 1.061462 1.069304 1.077764 1.081025 1.132583 1.177017 1.179183 #> [616,] 0.5803171 1.061543 1.069386 1.077846 1.081107 1.132669 1.177108 1.179274 #> [617,] 0.5791629 1.059432 1.067259 1.075703 1.078957 1.130417 1.174767 1.176928 #> [618,] 0.5792073 1.059513 1.067341 1.075785 1.079040 1.130503 1.174857 1.177018 #> [619,] 0.5804504 1.061787 1.069632 1.078094 1.081356 1.132930 1.177378 1.179545 #> [620,] 0.5804948 1.061868 1.069714 1.078177 1.081439 1.133016 1.177468 1.179635 #> [621,] 0.5805393 1.061950 1.069796 1.078259 1.081521 1.133103 1.177558 1.179725 #> [622,] 0.5805837 1.062031 1.069878 1.078342 1.081604 1.133190 1.177649 1.179816 #> [623,] 0.5806282 1.062112 1.069959 1.078424 1.081687 1.133277 1.177739 1.179906 #> [624,] 0.5806726 1.062193 1.070041 1.078507 1.081770 1.133363 1.177829 1.179996 #> [625,] 0.5807171 1.062275 1.070123 1.078589 1.081853 1.133450 1.177919 1.180087 #> [626,] 0.5807616 1.062356 1.070205 1.078672 1.081936 1.133537 1.178009 1.180177 #> [627,] 0.5808060 1.062438 1.070287 1.078755 1.082018 1.133624 1.178099 1.180267 #> [628,] 0.5808505 1.062519 1.070369 1.078837 1.082101 1.133711 1.178190 1.180358 #> [629,] 0.5808950 1.062600 1.070451 1.078920 1.082184 1.133797 1.178280 1.180448 #> [630,] 0.5803615 1.061624 1.069468 1.077929 1.081190 1.132756 1.177198 1.179364 #> [631,] 0.5804059 1.061706 1.069550 1.078011 1.081273 1.132843 1.177288 1.179454 #> [632,] 0.5792516 1.059594 1.067423 1.075868 1.079123 1.130590 1.174947 1.177109 #> [633,] 0.5792960 1.059675 1.067505 1.075950 1.079205 1.130676 1.175036 1.177199 #> [634,] 0.5793403 1.059756 1.067586 1.076032 1.079288 1.130763 1.175126 1.177289 #> [635,] 0.5793847 1.059838 1.067668 1.076115 1.079370 1.130850 1.175216 1.177379 #> [636,] 0.5794291 1.059919 1.067750 1.076197 1.079453 1.130936 1.175306 1.177469 #> [637,] 0.5794734 1.060000 1.067832 1.076280 1.079536 1.131023 1.175396 1.177559 #> [638,] 0.5795178 1.060081 1.067913 1.076362 1.079618 1.131109 1.175486 1.177649 #> [639,] 0.5795622 1.060162 1.067995 1.076444 1.079701 1.131196 1.175576 1.177740 #> [640,] 0.5796066 1.060243 1.068077 1.076527 1.079784 1.131283 1.175666 1.177830 #> [641,] 0.5796509 1.060325 1.068159 1.076609 1.079866 1.131369 1.175756 1.177920 #> [642,] 0.5796953 1.060406 1.068240 1.076692 1.079949 1.131456 1.175846 1.178010 #> [643,] 0.5797397 1.060487 1.068322 1.076774 1.080032 1.131543 1.175937 1.178100 #> [644,] 0.5797841 1.060568 1.068404 1.076857 1.080115 1.131629 1.176027 1.178191 #> [645,] 0.5798285 1.060649 1.068486 1.076939 1.080197 1.131716 1.176117 1.178281 #> [646,] 0.5798729 1.060731 1.068568 1.077021 1.080280 1.131802 1.176207 1.178371 #> [647,] 0.5799173 1.060812 1.068649 1.077104 1.080363 1.131889 1.176297 1.178461 #> [648,] 0.5799617 1.060893 1.068731 1.077186 1.080445 1.131976 1.176387 1.178552 #> [649,] 0.5800061 1.060974 1.068813 1.077269 1.080528 1.132062 1.176477 1.178642 #> [650,] 0.5800505 1.061055 1.068895 1.077351 1.080611 1.132149 1.176567 1.178732 #> [651,] 0.5800949 1.061137 1.068977 1.077434 1.080694 1.132236 1.176657 1.178822 #> [652,] 0.5801393 1.061218 1.069059 1.077516 1.080776 1.132323 1.176747 1.178913 #> [653,] 0.5801838 1.061299 1.069141 1.077599 1.080859 1.132409 1.176837 1.179003 #> [654,] 0.5802282 1.061381 1.069222 1.077681 1.080942 1.132496 1.176927 1.179093 #>           [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] #>   [1,] 1.085189     0     0     0     0     0     0     0     0     0     0 #>   [2,] 1.085106     0     0     0     0     0     0     0     0     0     0 #>   [3,] 1.085023     0     0     0     0     0     0     0     0     0     0 #>   [4,] 1.084940     0     0     0     0     0     0     0     0     0     0 #>   [5,] 1.084857     0     0     0     0     0     0     0     0     0     0 #>   [6,] 1.084773     0     0     0     0     0     0     0     0     0     0 #>   [7,] 1.084690     0     0     0     0     0     0     0     0     0     0 #>   [8,] 1.084607     0     0     0     0     0     0     0     0     0     0 #>   [9,] 1.084524     0     0     0     0     0     0     0     0     0     0 #>  [10,] 1.084441     0     0     0     0     0     0     0     0     0     0 #>  [11,] 1.084358     0     0     0     0     0     0     0     0     0     0 #>  [12,] 1.084275     0     0     0     0     0     0     0     0     0     0 #>  [13,] 1.084192     0     0     0     0     0     0     0     0     0     0 #>  [14,] 1.084109     0     0     0     0     0     0     0     0     0     0 #>  [15,] 1.084026     0     0     0     0     0     0     0     0     0     0 #>  [16,] 1.083943     0     0     0     0     0     0     0     0     0     0 #>  [17,] 1.083860     0     0     0     0     0     0     0     0     0     0 #>  [18,] 1.083777     0     0     0     0     0     0     0     0     0     0 #>  [19,] 1.083694     0     0     0     0     0     0     0     0     0     0 #>  [20,] 1.083611     0     0     0     0     0     0     0     0     0     0 #>  [21,] 1.083528     0     0     0     0     0     0     0     0     0     0 #>  [22,] 1.083445     0     0     0     0     0     0     0     0     0     0 #>  [23,] 1.083362     0     0     0     0     0     0     0     0     0     0 #>  [24,] 1.083279     0     0     0     0     0     0     0     0     0     0 #>  [25,] 1.083196     0     0     0     0     0     0     0     0     0     0 #>  [26,] 1.083114     0     0     0     0     0     0     0     0     0     0 #>  [27,] 1.083031     0     0     0     0     0     0     0     0     0     0 #>  [28,] 1.082948     0     0     0     0     0     0     0     0     0     0 #>  [29,] 1.082865     0     0     0     0     0     0     0     0     0     0 #>  [30,] 1.082782     0     0     0     0     0     0     0     0     0     0 #>  [31,] 1.082699     0     0     0     0     0     0     0     0     0     0 #>  [32,] 1.082616     0     0     0     0     0     0     0     0     0     0 #>  [33,] 1.082533     0     0     0     0     0     0     0     0     0     0 #>  [34,] 1.082450     0     0     0     0     0     0     0     0     0     0 #>  [35,] 1.082367     0     0     0     0     0     0     0     0     0     0 #>  [36,] 1.082285     0     0     0     0     0     0     0     0     0     0 #>  [37,] 1.082202     0     0     0     0     0     0     0     0     0     0 #>  [38,] 1.082119     0     0     0     0     0     0     0     0     0     0 #>  [39,] 1.082036     0     0     0     0     0     0     0     0     0     0 #>  [40,] 1.081953     0     0     0     0     0     0     0     0     0     0 #>  [41,] 1.081870     0     0     0     0     0     0     0     0     0     0 #>  [42,] 1.081787     0     0     0     0     0     0     0     0     0     0 #>  [43,] 1.081705     0     0     0     0     0     0     0     0     0     0 #>  [44,] 1.081622     0     0     0     0     0     0     0     0     0     0 #>  [45,] 1.081539     0     0     0     0     0     0     0     0     0     0 #>  [46,] 1.081456     0     0     0     0     0     0     0     0     0     0 #>  [47,] 1.081373     0     0     0     0     0     0     0     0     0     0 #>  [48,] 1.081291     0     0     0     0     0     0     0     0     0     0 #>  [49,] 1.081208     0     0     0     0     0     0     0     0     0     0 #>  [50,] 1.081125     0     0     0     0     0     0     0     0     0     0 #>  [51,] 1.081042     0     0     0     0     0     0     0     0     0     0 #>  [52,] 1.080959     0     0     0     0     0     0     0     0     0     0 #>  [53,] 1.080877     0     0     0     0     0     0     0     0     0     0 #>  [54,] 1.080794     0     0     0     0     0     0     0     0     0     0 #>  [55,] 1.080711     0     0     0     0     0     0     0     0     0     0 #>  [56,] 1.080628     0     0     0     0     0     0     0     0     0     0 #>  [57,] 1.080546     0     0     0     0     0     0     0     0     0     0 #>  [58,] 1.080463     0     0     0     0     0     0     0     0     0     0 #>  [59,] 1.080380     0     0     0     0     0     0     0     0     0     0 #>  [60,] 1.080298     0     0     0     0     0     0     0     0     0     0 #>  [61,] 1.080215     0     0     0     0     0     0     0     0     0     0 #>  [62,] 1.080132     0     0     0     0     0     0     0     0     0     0 #>  [63,] 1.080049     0     0     0     0     0     0     0     0     0     0 #>  [64,] 1.079967     0     0     0     0     0     0     0     0     0     0 #>  [65,] 1.079884     0     0     0     0     0     0     0     0     0     0 #>  [66,] 1.079801     0     0     0     0     0     0     0     0     0     0 #>  [67,] 1.079719     0     0     0     0     0     0     0     0     0     0 #>  [68,] 1.079636     0     0     0     0     0     0     0     0     0     0 #>  [69,] 1.079553     0     0     0     0     0     0     0     0     0     0 #>  [70,] 1.079471     0     0     0     0     0     0     0     0     0     0 #>  [71,] 1.079388     0     0     0     0     0     0     0     0     0     0 #>  [72,] 1.079305     0     0     0     0     0     0     0     0     0     0 #>  [73,] 1.079223     0     0     0     0     0     0     0     0     0     0 #>  [74,] 1.079140     0     0     0     0     0     0     0     0     0     0 #>  [75,] 1.079058     0     0     0     0     0     0     0     0     0     0 #>  [76,] 1.078975     0     0     0     0     0     0     0     0     0     0 #>  [77,] 1.078892     0     0     0     0     0     0     0     0     0     0 #>  [78,] 1.078810     0     0     0     0     0     0     0     0     0     0 #>  [79,] 1.078727     0     0     0     0     0     0     0     0     0     0 #>  [80,] 1.078644     0     0     0     0     0     0     0     0     0     0 #>  [81,] 1.078562     0     0     0     0     0     0     0     0     0     0 #>  [82,] 1.078479     0     0     0     0     0     0     0     0     0     0 #>  [83,] 1.078397     0     0     0     0     0     0     0     0     0     0 #>  [84,] 1.078314     0     0     0     0     0     0     0     0     0     0 #>  [85,] 1.078232     0     0     0     0     0     0     0     0     0     0 #>  [86,] 1.078149     0     0     0     0     0     0     0     0     0     0 #>  [87,] 1.078067     0     0     0     0     0     0     0     0     0     0 #>  [88,] 1.077984     0     0     0     0     0     0     0     0     0     0 #>  [89,] 1.077901     0     0     0     0     0     0     0     0     0     0 #>  [90,] 1.077819     0     0     0     0     0     0     0     0     0     0 #>  [91,] 1.077736     0     0     0     0     0     0     0     0     0     0 #>  [92,] 1.077654     0     0     0     0     0     0     0     0     0     0 #>  [93,] 1.077571     0     0     0     0     0     0     0     0     0     0 #>  [94,] 1.077489     0     0     0     0     0     0     0     0     0     0 #>  [95,] 1.077406     0     0     0     0     0     0     0     0     0     0 #>  [96,] 1.077324     0     0     0     0     0     0     0     0     0     0 #>  [97,] 1.077241     0     0     0     0     0     0     0     0     0     0 #>  [98,] 1.077159     0     0     0     0     0     0     0     0     0     0 #>  [99,] 1.077076     0     0     0     0     0     0     0     0     0     0 #> [100,] 1.076994     0     0     0     0     0     0     0     0     0     0 #> [101,] 1.076912     0     0     0     0     0     0     0     0     0     0 #> [102,] 1.076829     0     0     0     0     0     0     0     0     0     0 #> [103,] 1.076747     0     0     0     0     0     0     0     0     0     0 #> [104,] 1.076664     0     0     0     0     0     0     0     0     0     0 #> [105,] 1.076582     0     0     0     0     0     0     0     0     0     0 #> [106,] 1.076499     0     0     0     0     0     0     0     0     0     0 #> [107,] 1.076417     0     0     0     0     0     0     0     0     0     0 #> [108,] 1.076334     0     0     0     0     0     0     0     0     0     0 #> [109,] 1.076252     0     0     0     0     0     0     0     0     0     0 #> [110,] 1.076170     0     0     0     0     0     0     0     0     0     0 #> [111,] 1.076087     0     0     0     0     0     0     0     0     0     0 #> [112,] 1.076005     0     0     0     0     0     0     0     0     0     0 #> [113,] 1.075922     0     0     0     0     0     0     0     0     0     0 #> [114,] 1.075840     0     0     0     0     0     0     0     0     0     0 #> [115,] 1.075758     0     0     0     0     0     0     0     0     0     0 #> [116,] 1.075675     0     0     0     0     0     0     0     0     0     0 #> [117,] 1.075593     0     0     0     0     0     0     0     0     0     0 #> [118,] 1.075511     0     0     0     0     0     0     0     0     0     0 #> [119,] 1.075428     0     0     0     0     0     0     0     0     0     0 #> [120,] 1.075346     0     0     0     0     0     0     0     0     0     0 #> [121,] 1.075264     0     0     0     0     0     0     0     0     0     0 #> [122,] 1.075181     0     0     0     0     0     0     0     0     0     0 #> [123,] 1.075099     0     0     0     0     0     0     0     0     0     0 #> [124,] 1.075017     0     0     0     0     0     0     0     0     0     0 #> [125,] 1.074934     0     0     0     0     0     0     0     0     0     0 #> [126,] 1.074852     0     0     0     0     0     0     0     0     0     0 #> [127,] 1.074770     0     0     0     0     0     0     0     0     0     0 #> [128,] 1.074687     0     0     0     0     0     0     0     0     0     0 #> [129,] 1.074605     0     0     0     0     0     0     0     0     0     0 #> [130,] 1.074523     0     0     0     0     0     0     0     0     0     0 #> [131,] 1.074441     0     0     0     0     0     0     0     0     0     0 #> [132,] 1.074358     0     0     0     0     0     0     0     0     0     0 #> [133,] 1.074276     0     0     0     0     0     0     0     0     0     0 #> [134,] 1.074194     0     0     0     0     0     0     0     0     0     0 #> [135,] 1.074112     0     0     0     0     0     0     0     0     0     0 #> [136,] 1.074029     0     0     0     0     0     0     0     0     0     0 #> [137,] 1.073947     0     0     0     0     0     0     0     0     0     0 #> [138,] 1.073865     0     0     0     0     0     0     0     0     0     0 #> [139,] 1.073783     0     0     0     0     0     0     0     0     0     0 #> [140,] 1.073700     0     0     0     0     0     0     0     0     0     0 #> [141,] 1.073618     0     0     0     0     0     0     0     0     0     0 #> [142,] 1.073536     0     0     0     0     0     0     0     0     0     0 #> [143,] 1.073454     0     0     0     0     0     0     0     0     0     0 #> [144,] 1.073372     0     0     0     0     0     0     0     0     0     0 #> [145,] 1.073290     0     0     0     0     0     0     0     0     0     0 #> [146,] 1.073207     0     0     0     0     0     0     0     0     0     0 #> [147,] 1.073125     0     0     0     0     0     0     0     0     0     0 #> [148,] 1.073043     0     0     0     0     0     0     0     0     0     0 #> [149,] 1.072961     0     0     0     0     0     0     0     0     0     0 #> [150,] 1.072879     0     0     0     0     0     0     0     0     0     0 #> [151,] 1.072797     0     0     0     0     0     0     0     0     0     0 #> [152,] 1.072714     0     0     0     0     0     0     0     0     0     0 #> [153,] 1.072632     0     0     0     0     0     0     0     0     0     0 #> [154,] 1.072550     0     0     0     0     0     0     0     0     0     0 #> [155,] 1.072468     0     0     0     0     0     0     0     0     0     0 #> [156,] 1.072386     0     0     0     0     0     0     0     0     0     0 #> [157,] 1.072304     0     0     0     0     0     0     0     0     0     0 #> [158,] 1.072222     0     0     0     0     0     0     0     0     0     0 #> [159,] 1.072140     0     0     0     0     0     0     0     0     0     0 #> [160,] 1.072058     0     0     0     0     0     0     0     0     0     0 #> [161,] 1.071975     0     0     0     0     0     0     0     0     0     0 #> [162,] 1.071893     0     0     0     0     0     0     0     0     0     0 #> [163,] 1.071811     0     0     0     0     0     0     0     0     0     0 #> [164,] 1.071729     0     0     0     0     0     0     0     0     0     0 #> [165,] 1.071647     0     0     0     0     0     0     0     0     0     0 #> [166,] 1.071565     0     0     0     0     0     0     0     0     0     0 #> [167,] 1.071483     0     0     0     0     0     0     0     0     0     0 #> [168,] 1.071401     0     0     0     0     0     0     0     0     0     0 #> [169,] 1.071319     0     0     0     0     0     0     0     0     0     0 #> [170,] 1.071237     0     0     0     0     0     0     0     0     0     0 #> [171,] 1.071155     0     0     0     0     0     0     0     0     0     0 #> [172,] 1.071073     0     0     0     0     0     0     0     0     0     0 #> [173,] 1.070991     0     0     0     0     0     0     0     0     0     0 #> [174,] 1.070909     0     0     0     0     0     0     0     0     0     0 #> [175,] 1.070827     0     0     0     0     0     0     0     0     0     0 #> [176,] 1.070745     0     0     0     0     0     0     0     0     0     0 #> [177,] 1.070663     0     0     0     0     0     0     0     0     0     0 #> [178,] 1.070581     0     0     0     0     0     0     0     0     0     0 #> [179,] 1.070499     0     0     0     0     0     0     0     0     0     0 #> [180,] 1.070417     0     0     0     0     0     0     0     0     0     0 #> [181,] 1.070335     0     0     0     0     0     0     0     0     0     0 #> [182,] 1.070253     0     0     0     0     0     0     0     0     0     0 #> [183,] 1.070171     0     0     0     0     0     0     0     0     0     0 #> [184,] 1.070089     0     0     0     0     0     0     0     0     0     0 #> [185,] 1.070007     0     0     0     0     0     0     0     0     0     0 #> [186,] 1.069925     0     0     0     0     0     0     0     0     0     0 #> [187,] 1.069844     0     0     0     0     0     0     0     0     0     0 #> [188,] 1.069762     0     0     0     0     0     0     0     0     0     0 #> [189,] 1.069680     0     0     0     0     0     0     0     0     0     0 #> [190,] 1.069598     0     0     0     0     0     0     0     0     0     0 #> [191,] 1.069516     0     0     0     0     0     0     0     0     0     0 #> [192,] 1.069434     0     0     0     0     0     0     0     0     0     0 #> [193,] 1.069352     0     0     0     0     0     0     0     0     0     0 #> [194,] 1.069270     0     0     0     0     0     0     0     0     0     0 #> [195,] 1.069188     0     0     0     0     0     0     0     0     0     0 #> [196,] 1.069107     0     0     0     0     0     0     0     0     0     0 #> [197,] 1.069025     0     0     0     0     0     0     0     0     0     0 #> [198,] 1.068943     0     0     0     0     0     0     0     0     0     0 #> [199,] 1.068861     0     0     0     0     0     0     0     0     0     0 #> [200,] 1.068779     0     0     0     0     0     0     0     0     0     0 #> [201,] 1.068697     0     0     0     0     0     0     0     0     0     0 #> [202,] 1.068615     0     0     0     0     0     0     0     0     0     0 #> [203,] 1.068534     0     0     0     0     0     0     0     0     0     0 #> [204,] 1.068452     0     0     0     0     0     0     0     0     0     0 #> [205,] 1.068370     0     0     0     0     0     0     0     0     0     0 #> [206,] 1.068288     0     0     0     0     0     0     0     0     0     0 #> [207,] 1.068206     0     0     0     0     0     0     0     0     0     0 #> [208,] 1.068125     0     0     0     0     0     0     0     0     0     0 #> [209,] 1.068043     0     0     0     0     0     0     0     0     0     0 #> [210,] 1.067961     0     0     0     0     0     0     0     0     0     0 #> [211,] 1.067879     0     0     0     0     0     0     0     0     0     0 #> [212,] 1.067798     0     0     0     0     0     0     0     0     0     0 #> [213,] 1.067716     0     0     0     0     0     0     0     0     0     0 #> [214,] 1.067634     0     0     0     0     0     0     0     0     0     0 #> [215,] 1.067552     0     0     0     0     0     0     0     0     0     0 #> [216,] 1.067471     0     0     0     0     0     0     0     0     0     0 #> [217,] 1.067389     0     0     0     0     0     0     0     0     0     0 #> [218,] 1.067307     0     0     0     0     0     0     0     0     0     0 #> [219,] 1.067225     0     0     0     0     0     0     0     0     0     0 #> [220,] 1.067144     0     0     0     0     0     0     0     0     0     0 #> [221,] 1.067062     0     0     0     0     0     0     0     0     0     0 #> [222,] 1.066980     0     0     0     0     0     0     0     0     0     0 #> [223,] 1.066899     0     0     0     0     0     0     0     0     0     0 #> [224,] 1.066817     0     0     0     0     0     0     0     0     0     0 #> [225,] 1.066735     0     0     0     0     0     0     0     0     0     0 #> [226,] 1.066654     0     0     0     0     0     0     0     0     0     0 #> [227,] 1.066572     0     0     0     0     0     0     0     0     0     0 #> [228,] 1.066490     0     0     0     0     0     0     0     0     0     0 #> [229,] 1.066409     0     0     0     0     0     0     0     0     0     0 #> [230,] 1.066327     0     0     0     0     0     0     0     0     0     0 #> [231,] 1.066245     0     0     0     0     0     0     0     0     0     0 #> [232,] 1.066164     0     0     0     0     0     0     0     0     0     0 #> [233,] 1.066082     0     0     0     0     0     0     0     0     0     0 #> [234,] 1.066000     0     0     0     0     0     0     0     0     0     0 #> [235,] 1.065919     0     0     0     0     0     0     0     0     0     0 #> [236,] 1.065837     0     0     0     0     0     0     0     0     0     0 #> [237,] 1.065756     0     0     0     0     0     0     0     0     0     0 #> [238,] 1.065674     0     0     0     0     0     0     0     0     0     0 #> [239,] 1.065592     0     0     0     0     0     0     0     0     0     0 #> [240,] 1.065511     0     0     0     0     0     0     0     0     0     0 #> [241,] 1.065429     0     0     0     0     0     0     0     0     0     0 #> [242,] 1.065348     0     0     0     0     0     0     0     0     0     0 #> [243,] 1.065266     0     0     0     0     0     0     0     0     0     0 #> [244,] 1.065185     0     0     0     0     0     0     0     0     0     0 #> [245,] 1.065103     0     0     0     0     0     0     0     0     0     0 #> [246,] 1.065021     0     0     0     0     0     0     0     0     0     0 #> [247,] 1.064940     0     0     0     0     0     0     0     0     0     0 #> [248,] 1.064858     0     0     0     0     0     0     0     0     0     0 #> [249,] 1.064777     0     0     0     0     0     0     0     0     0     0 #> [250,] 1.064695     0     0     0     0     0     0     0     0     0     0 #> [251,] 1.064614     0     0     0     0     0     0     0     0     0     0 #> [252,] 1.064532     0     0     0     0     0     0     0     0     0     0 #> [253,] 1.064451     0     0     0     0     0     0     0     0     0     0 #> [254,] 1.064369     0     0     0     0     0     0     0     0     0     0 #> [255,] 1.064288     0     0     0     0     0     0     0     0     0     0 #> [256,] 1.064206     0     0     0     0     0     0     0     0     0     0 #> [257,] 1.064125     0     0     0     0     0     0     0     0     0     0 #> [258,] 1.064043     0     0     0     0     0     0     0     0     0     0 #> [259,] 1.063962     0     0     0     0     0     0     0     0     0     0 #> [260,] 1.063880     0     0     0     0     0     0     0     0     0     0 #> [261,] 1.063799     0     0     0     0     0     0     0     0     0     0 #> [262,] 1.063717     0     0     0     0     0     0     0     0     0     0 #> [263,] 1.063636     0     0     0     0     0     0     0     0     0     0 #> [264,] 1.063555     0     0     0     0     0     0     0     0     0     0 #> [265,] 1.063473     0     0     0     0     0     0     0     0     0     0 #> [266,] 1.063392     0     0     0     0     0     0     0     0     0     0 #> [267,] 1.063310     0     0     0     0     0     0     0     0     0     0 #> [268,] 1.063229     0     0     0     0     0     0     0     0     0     0 #> [269,] 1.063147     0     0     0     0     0     0     0     0     0     0 #> [270,] 1.063066     0     0     0     0     0     0     0     0     0     0 #> [271,] 1.062985     0     0     0     0     0     0     0     0     0     0 #> [272,] 1.062903     0     0     0     0     0     0     0     0     0     0 #> [273,] 1.062822     0     0     0     0     0     0     0     0     0     0 #> [274,] 1.062741     0     0     0     0     0     0     0     0     0     0 #> [275,] 1.062659     0     0     0     0     0     0     0     0     0     0 #> [276,] 1.062578     0     0     0     0     0     0     0     0     0     0 #> [277,] 1.062496     0     0     0     0     0     0     0     0     0     0 #> [278,] 1.062415     0     0     0     0     0     0     0     0     0     0 #> [279,] 1.062334     0     0     0     0     0     0     0     0     0     0 #> [280,] 1.062252     0     0     0     0     0     0     0     0     0     0 #> [281,] 1.062171     0     0     0     0     0     0     0     0     0     0 #> [282,] 1.062090     0     0     0     0     0     0     0     0     0     0 #> [283,] 1.062008     0     0     0     0     0     0     0     0     0     0 #> [284,] 1.061927     0     0     0     0     0     0     0     0     0     0 #> [285,] 1.061846     0     0     0     0     0     0     0     0     0     0 #> [286,] 1.061765     0     0     0     0     0     0     0     0     0     0 #> [287,] 1.061683     0     0     0     0     0     0     0     0     0     0 #> [288,] 1.061602     0     0     0     0     0     0     0     0     0     0 #> [289,] 1.061521     0     0     0     0     0     0     0     0     0     0 #> [290,] 1.061439     0     0     0     0     0     0     0     0     0     0 #> [291,] 1.061358     0     0     0     0     0     0     0     0     0     0 #> [292,] 1.061277     0     0     0     0     0     0     0     0     0     0 #> [293,] 1.061196     0     0     0     0     0     0     0     0     0     0 #> [294,] 1.061114     0     0     0     0     0     0     0     0     0     0 #> [295,] 1.061033     0     0     0     0     0     0     0     0     0     0 #> [296,] 1.060952     0     0     0     0     0     0     0     0     0     0 #> [297,] 1.060871     0     0     0     0     0     0     0     0     0     0 #> [298,] 1.060789     0     0     0     0     0     0     0     0     0     0 #> [299,] 1.060708     0     0     0     0     0     0     0     0     0     0 #> [300,] 1.060627     0     0     0     0     0     0     0     0     0     0 #> [301,] 1.060546     0     0     0     0     0     0     0     0     0     0 #> [302,] 1.060465     0     0     0     0     0     0     0     0     0     0 #> [303,] 1.060383     0     0     0     0     0     0     0     0     0     0 #> [304,] 1.060302     0     0     0     0     0     0     0     0     0     0 #> [305,] 1.060221     0     0     0     0     0     0     0     0     0     0 #> [306,] 1.060140     0     0     0     0     0     0     0     0     0     0 #> [307,] 1.060059     0     0     0     0     0     0     0     0     0     0 #> [308,] 1.059978     0     0     0     0     0     0     0     0     0     0 #> [309,] 1.059815     0     0     0     0     0     0     0     0     0     0 #> [310,] 1.059734     0     0     0     0     0     0     0     0     0     0 #> [311,] 1.059653     0     0     0     0     0     0     0     0     0     0 #> [312,] 1.059572     0     0     0     0     0     0     0     0     0     0 #> [313,] 1.059491     0     0     0     0     0     0     0     0     0     0 #> [314,] 1.059410     0     0     0     0     0     0     0     0     0     0 #> [315,] 1.059328     0     0     0     0     0     0     0     0     0     0 #> [316,] 1.059247     0     0     0     0     0     0     0     0     0     0 #> [317,] 1.059166     0     0     0     0     0     0     0     0     0     0 #> [318,] 1.059896     0     0     0     0     0     0     0     0     0     0 #> [319,] 1.128376     0     0     0     0     0     0     0     0     0     0 #> [320,] 1.128289     0     0     0     0     0     0     0     0     0     0 #> [321,] 1.128203     0     0     0     0     0     0     0     0     0     0 #> [322,] 1.128462     0     0     0     0     0     0     0     0     0     0 #> [323,] 1.129845     0     0     0     0     0     0     0     0     0     0 #> [324,] 1.129759     0     0     0     0     0     0     0     0     0     0 #> [325,] 1.129672     0     0     0     0     0     0     0     0     0     0 #> [326,] 1.129586     0     0     0     0     0     0     0     0     0     0 #> [327,] 1.129499     0     0     0     0     0     0     0     0     0     0 #> [328,] 1.129413     0     0     0     0     0     0     0     0     0     0 #> [329,] 1.129326     0     0     0     0     0     0     0     0     0     0 #> [330,] 1.128635     0     0     0     0     0     0     0     0     0     0 #> [331,] 1.128548     0     0     0     0     0     0     0     0     0     0 #> [332,] 1.130278     0     0     0     0     0     0     0     0     0     0 #> [333,] 1.130191     0     0     0     0     0     0     0     0     0     0 #> [334,] 1.130105     0     0     0     0     0     0     0     0     0     0 #> [335,] 1.129932     0     0     0     0     0     0     0     0     0     0 #> [336,] 1.129240     0     0     0     0     0     0     0     0     0     0 #> [337,] 1.129154     0     0     0     0     0     0     0     0     0     0 #> [338,] 1.129067     0     0     0     0     0     0     0     0     0     0 #> [339,] 1.128981     0     0     0     0     0     0     0     0     0     0 #> [340,] 1.128894     0     0     0     0     0     0     0     0     0     0 #> [341,] 1.128808     0     0     0     0     0     0     0     0     0     0 #> [342,] 1.128721     0     0     0     0     0     0     0     0     0     0 #> [343,] 1.130018     0     0     0     0     0     0     0     0     0     0 #> [344,] 1.133003     0     0     0     0     0     0     0     0     0     0 #> [345,] 1.132830     0     0     0     0     0     0     0     0     0     0 #> [346,] 1.132743     0     0     0     0     0     0     0     0     0     0 #> [347,] 1.132656     0     0     0     0     0     0     0     0     0     0 #> [348,] 1.132917     0     0     0     0     0     0     0     0     0     0 #> [349,] 1.132483     0     0     0     0     0     0     0     0     0     0 #> [350,] 1.132396     0     0     0     0     0     0     0     0     0     0 #> [351,] 1.132310     0     0     0     0     0     0     0     0     0     0 #> [352,] 1.132223     0     0     0     0     0     0     0     0     0     0 #> [353,] 1.132136     0     0     0     0     0     0     0     0     0     0 #> [354,] 1.132050     0     0     0     0     0     0     0     0     0     0 #> [355,] 1.131963     0     0     0     0     0     0     0     0     0     0 #> [356,] 1.131876     0     0     0     0     0     0     0     0     0     0 #> [357,] 1.131790     0     0     0     0     0     0     0     0     0     0 #> [358,] 1.131703     0     0     0     0     0     0     0     0     0     0 #> [359,] 1.131616     0     0     0     0     0     0     0     0     0     0 #> [360,] 1.131530     0     0     0     0     0     0     0     0     0     0 #> [361,] 1.132570     0     0     0     0     0     0     0     0     0     0 #> [362,] 1.133698     0     0     0     0     0     0     0     0     0     0 #> [363,] 1.133611     0     0     0     0     0     0     0     0     0     0 #> [364,] 1.133524     0     0     0     0     0     0     0     0     0     0 #> [365,] 1.133437     0     0     0     0     0     0     0     0     0     0 #> [366,] 1.133350     0     0     0     0     0     0     0     0     0     0 #> [367,] 1.133264     0     0     0     0     0     0     0     0     0     0 #> [368,] 1.133177     0     0     0     0     0     0     0     0     0     0 #> [369,] 1.133090     0     0     0     0     0     0     0     0     0     0 #> [370,] 1.130750     0     0     0     0     0     0     0     0     0     0 #> [371,] 1.130664     0     0     0     0     0     0     0     0     0     0 #> [372,] 1.130577     0     0     0     0     0     0     0     0     0     0 #> [373,] 1.130490     0     0     0     0     0     0     0     0     0     0 #> [374,] 1.130404     0     0     0     0     0     0     0     0     0     0 #> [375,] 1.131443     0     0     0     0     0     0     0     0     0     0 #> [376,] 1.131356     0     0     0     0     0     0     0     0     0     0 #> [377,] 1.131270     0     0     0     0     0     0     0     0     0     0 #> [378,] 1.131183     0     0     0     0     0     0     0     0     0     0 #> [379,] 1.131096     0     0     0     0     0     0     0     0     0     0 #> [380,] 1.131010     0     0     0     0     0     0     0     0     0     0 #> [381,] 1.130923     0     0     0     0     0     0     0     0     0     0 #> [382,] 1.130837     0     0     0     0     0     0     0     0     0     0 #> [383,] 1.184524     0     0     0     0     0     0     0     0     0     0 #> [384,] 1.184433     0     0     0     0     0     0     0     0     0     0 #> [385,] 1.184342     0     0     0     0     0     0     0     0     0     0 #> [386,] 1.184252     0     0     0     0     0     0     0     0     0     0 #> [387,] 1.184161     0     0     0     0     0     0     0     0     0     0 #> [388,] 1.178102     0     0     0     0     0     0     0     0     0     0 #> [389,] 1.178012     0     0     0     0     0     0     0     0     0     0 #> [390,] 1.177921     0     0     0     0     0     0     0     0     0     0 #> [391,] 1.177831     0     0     0     0     0     0     0     0     0     0 #> [392,] 1.177741     0     0     0     0     0     0     0     0     0     0 #> [393,] 1.177651     0     0     0     0     0     0     0     0     0     0 #> [394,] 1.177561     0     0     0     0     0     0     0     0     0     0 #> [395,] 1.177471     0     0     0     0     0     0     0     0     0     0 #> [396,] 1.177380     0     0     0     0     0     0     0     0     0     0 #> [397,] 1.177290     0     0     0     0     0     0     0     0     0     0 #> [398,] 1.177200     0     0     0     0     0     0     0     0     0     0 #> [399,] 1.177110     0     0     0     0     0     0     0     0     0     0 #> [400,] 1.177020     0     0     0     0     0     0     0     0     0     0 #> [401,] 1.185340     0     0     0     0     0     0     0     0     0     0 #> [402,] 1.185250     0     0     0     0     0     0     0     0     0     0 #> [403,] 1.185159     0     0     0     0     0     0     0     0     0     0 #> [404,] 1.185068     0     0     0     0     0     0     0     0     0     0 #> [405,] 1.184977     0     0     0     0     0     0     0     0     0     0 #> [406,] 1.184887     0     0     0     0     0     0     0     0     0     0 #> [407,] 1.184796     0     0     0     0     0     0     0     0     0     0 #> [408,] 1.184705     0     0     0     0     0     0     0     0     0     0 #> [409,] 1.184615     0     0     0     0     0     0     0     0     0     0 #> [410,] 1.174949     0     0     0     0     0     0     0     0     0     0 #> [411,] 1.174859     0     0     0     0     0     0     0     0     0     0 #> [412,] 1.174769     0     0     0     0     0     0     0     0     0     0 #> [413,] 1.174679     0     0     0     0     0     0     0     0     0     0 #> [414,] 1.174589     0     0     0     0     0     0     0     0     0     0 #> [415,] 1.174499     0     0     0     0     0     0     0     0     0     0 #> [416,] 1.174409     0     0     0     0     0     0     0     0     0     0 #> [417,] 1.174319     0     0     0     0     0     0     0     0     0     0 #> [418,] 1.174229     0     0     0     0     0     0     0     0     0     0 #> [419,] 1.174140     0     0     0     0     0     0     0     0     0     0 #> [420,] 1.174050     0     0     0     0     0     0     0     0     0     0 #> [421,] 1.173960     0     0     0     0     0     0     0     0     0     0 #> [422,] 1.173870     0     0     0     0     0     0     0     0     0     0 #> [423,] 1.173780     0     0     0     0     0     0     0     0     0     0 #> [424,] 1.173690     0     0     0     0     0     0     0     0     0     0 #> [425,] 1.173600     0     0     0     0     0     0     0     0     0     0 #> [426,] 1.173510     0     0     0     0     0     0     0     0     0     0 #> [427,] 1.173421     0     0     0     0     0     0     0     0     0     0 #> [428,] 1.173331     0     0     0     0     0     0     0     0     0     0 #> [429,] 1.173241     0     0     0     0     0     0     0     0     0     0 #> [430,] 1.173151     0     0     0     0     0     0     0     0     0     0 #> [431,] 1.173061     0     0     0     0     0     0     0     0     0     0 #> [432,] 1.172971     0     0     0     0     0     0     0     0     0     0 #> [433,] 1.172882     0     0     0     0     0     0     0     0     0     0 #> [434,] 1.172792     0     0     0     0     0     0     0     0     0     0 #> [435,] 1.172702     0     0     0     0     0     0     0     0     0     0 #> [436,] 1.172612     0     0     0     0     0     0     0     0     0     0 #> [437,] 1.172522     0     0     0     0     0     0     0     0     0     0 #> [438,] 1.172433     0     0     0     0     0     0     0     0     0     0 #> [439,] 1.172343     0     0     0     0     0     0     0     0     0     0 #> [440,] 1.172253     0     0     0     0     0     0     0     0     0     0 #> [441,] 1.172163     0     0     0     0     0     0     0     0     0     0 #> [442,] 1.172074     0     0     0     0     0     0     0     0     0     0 #> [443,] 1.171984     0     0     0     0     0     0     0     0     0     0 #> [444,] 1.171894     0     0     0     0     0     0     0     0     0     0 #> [445,] 1.171804     0     0     0     0     0     0     0     0     0     0 #> [446,] 1.171715     0     0     0     0     0     0     0     0     0     0 #> [447,] 1.171625     0     0     0     0     0     0     0     0     0     0 #> [448,] 1.171535     0     0     0     0     0     0     0     0     0     0 #> [449,] 1.171446     0     0     0     0     0     0     0     0     0     0 #> [450,] 1.171356     0     0     0     0     0     0     0     0     0     0 #> [451,] 1.171266     0     0     0     0     0     0     0     0     0     0 #> [452,] 1.171177     0     0     0     0     0     0     0     0     0     0 #> [453,] 1.171087     0     0     0     0     0     0     0     0     0     0 #> [454,] 1.170997     0     0     0     0     0     0     0     0     0     0 #> [455,] 1.170908     0     0     0     0     0     0     0     0     0     0 #> [456,] 1.170818     0     0     0     0     0     0     0     0     0     0 #> [457,] 1.170728     0     0     0     0     0     0     0     0     0     0 #> [458,] 1.170639     0     0     0     0     0     0     0     0     0     0 #> [459,] 1.170549     0     0     0     0     0     0     0     0     0     0 #> [460,] 1.179546     0     0     0     0     0     0     0     0     0     0 #> [461,] 1.179456     0     0     0     0     0     0     0     0     0     0 #> [462,] 1.179365     0     0     0     0     0     0     0     0     0     0 #> [463,] 1.179275     0     0     0     0     0     0     0     0     0     0 #> [464,] 1.179185     0     0     0     0     0     0     0     0     0     0 #> [465,] 1.179095     0     0     0     0     0     0     0     0     0     0 #> [466,] 1.179004     0     0     0     0     0     0     0     0     0     0 #> [467,] 1.178914     0     0     0     0     0     0     0     0     0     0 #> [468,] 1.170459     0     0     0     0     0     0     0     0     0     0 #> [469,] 1.170370     0     0     0     0     0     0     0     0     0     0 #> [470,] 1.170280     0     0     0     0     0     0     0     0     0     0 #> [471,] 1.178553     0     0     0     0     0     0     0     0     0     0 #> [472,] 1.178463     0     0     0     0     0     0     0     0     0     0 #> [473,] 1.178373     0     0     0     0     0     0     0     0     0     0 #> [474,] 1.178282     0     0     0     0     0     0     0     0     0     0 #> [475,] 1.178192     0     0     0     0     0     0     0     0     0     0 #> [476,] 1.170191     0     0     0     0     0     0     0     0     0     0 #> [477,] 1.170101     0     0     0     0     0     0     0     0     0     0 #> [478,] 1.170011     0     0     0     0     0     0     0     0     0     0 #> [479,] 1.169922     0     0     0     0     0     0     0     0     0     0 #> [480,] 1.169832     0     0     0     0     0     0     0     0     0     0 #> [481,] 1.169743     0     0     0     0     0     0     0     0     0     0 #> [482,] 1.169653     0     0     0     0     0     0     0     0     0     0 #> [483,] 1.169564     0     0     0     0     0     0     0     0     0     0 #> [484,] 1.169474     0     0     0     0     0     0     0     0     0     0 #> [485,] 1.169384     0     0     0     0     0     0     0     0     0     0 #> [486,] 1.169295     0     0     0     0     0     0     0     0     0     0 #> [487,] 1.169205     0     0     0     0     0     0     0     0     0     0 #> [488,] 1.169116     0     0     0     0     0     0     0     0     0     0 #> [489,] 1.169026     0     0     0     0     0     0     0     0     0     0 #> [490,] 1.168937     0     0     0     0     0     0     0     0     0     0 #> [491,] 1.168847     0     0     0     0     0     0     0     0     0     0 #> [492,] 1.168758     0     0     0     0     0     0     0     0     0     0 #> [493,] 1.168668     0     0     0     0     0     0     0     0     0     0 #> [494,] 1.168579     0     0     0     0     0     0     0     0     0     0 #> [495,] 1.183708     0     0     0     0     0     0     0     0     0     0 #> [496,] 1.183617     0     0     0     0     0     0     0     0     0     0 #> [497,] 1.183527     0     0     0     0     0     0     0     0     0     0 #> [498,] 1.183436     0     0     0     0     0     0     0     0     0     0 #> [499,] 1.183345     0     0     0     0     0     0     0     0     0     0 #> [500,] 1.183255     0     0     0     0     0     0     0     0     0     0 #> [501,] 1.183164     0     0     0     0     0     0     0     0     0     0 #> [502,] 1.183074     0     0     0     0     0     0     0     0     0     0 #> [503,] 1.182983     0     0     0     0     0     0     0     0     0     0 #> [504,] 1.182892     0     0     0     0     0     0     0     0     0     0 #> [505,] 1.182802     0     0     0     0     0     0     0     0     0     0 #> [506,] 1.182711     0     0     0     0     0     0     0     0     0     0 #> [507,] 1.182621     0     0     0     0     0     0     0     0     0     0 #> [508,] 1.182530     0     0     0     0     0     0     0     0     0     0 #> [509,] 1.176930     0     0     0     0     0     0     0     0     0     0 #> [510,] 1.176840     0     0     0     0     0     0     0     0     0     0 #> [511,] 1.176750     0     0     0     0     0     0     0     0     0     0 #> [512,] 1.176659     0     0     0     0     0     0     0     0     0     0 #> [513,] 1.176569     0     0     0     0     0     0     0     0     0     0 #> [514,] 1.176479     0     0     0     0     0     0     0     0     0     0 #> [515,] 1.176389     0     0     0     0     0     0     0     0     0     0 #> [516,] 1.176299     0     0     0     0     0     0     0     0     0     0 #> [517,] 1.176209     0     0     0     0     0     0     0     0     0     0 #> [518,] 1.176119     0     0     0     0     0     0     0     0     0     0 #> [519,] 1.176029     0     0     0     0     0     0     0     0     0     0 #> [520,] 1.175939     0     0     0     0     0     0     0     0     0     0 #> [521,] 1.175849     0     0     0     0     0     0     0     0     0     0 #> [522,] 1.175759     0     0     0     0     0     0     0     0     0     0 #> [523,] 1.175669     0     0     0     0     0     0     0     0     0     0 #> [524,] 1.175579     0     0     0     0     0     0     0     0     0     0 #> [525,] 1.175489     0     0     0     0     0     0     0     0     0     0 #> [526,] 1.175399     0     0     0     0     0     0     0     0     0     0 #> [527,] 1.175309     0     0     0     0     0     0     0     0     0     0 #> [528,] 1.175219     0     0     0     0     0     0     0     0     0     0 #> [529,] 1.175129     0     0     0     0     0     0     0     0     0     0 #> [530,] 1.175039     0     0     0     0     0     0     0     0     0     0 #> [531,] 1.184070     0     0     0     0     0     0     0     0     0     0 #> [532,] 1.183980     0     0     0     0     0     0     0     0     0     0 #> [533,] 1.183889     0     0     0     0     0     0     0     0     0     0 #> [534,] 1.183798     0     0     0     0     0     0     0     0     0     0 #> [535,] 1.180088     0     0     0     0     0     0     0     0     0     0 #> [536,] 1.179998     0     0     0     0     0     0     0     0     0     0 #> [537,] 1.179907     0     0     0     0     0     0     0     0     0     0 #> [538,] 1.179817     0     0     0     0     0     0     0     0     0     0 #> [539,] 1.179727     0     0     0     0     0     0     0     0     0     0 #> [540,] 1.179636     0     0     0     0     0     0     0     0     0     0 #> [541,] 1.181987     0     0     0     0     0     0     0     0     0     0 #> [542,] 1.181897     0     0     0     0     0     0     0     0     0     0 #> [543,] 1.181806     0     0     0     0     0     0     0     0     0     0 #> [544,] 1.181716     0     0     0     0     0     0     0     0     0     0 #> [545,] 1.181625     0     0     0     0     0     0     0     0     0     0 #> [546,] 1.181535     0     0     0     0     0     0     0     0     0     0 #> [547,] 1.181444     0     0     0     0     0     0     0     0     0     0 #> [548,] 1.181354     0     0     0     0     0     0     0     0     0     0 #> [549,] 1.182440     0     0     0     0     0     0     0     0     0     0 #> [550,] 1.182349     0     0     0     0     0     0     0     0     0     0 #> [551,] 1.182259     0     0     0     0     0     0     0     0     0     0 #> [552,] 1.182168     0     0     0     0     0     0     0     0     0     0 #> [553,] 1.182078     0     0     0     0     0     0     0     0     0     0 #> [554,] 1.180811     0     0     0     0     0     0     0     0     0     0 #> [555,] 1.180721     0     0     0     0     0     0     0     0     0     0 #> [556,] 1.180630     0     0     0     0     0     0     0     0     0     0 #> [557,] 1.180540     0     0     0     0     0     0     0     0     0     0 #> [558,] 1.180450     0     0     0     0     0     0     0     0     0     0 #> [559,] 1.180359     0     0     0     0     0     0     0     0     0     0 #> [560,] 1.180269     0     0     0     0     0     0     0     0     0     0 #> [561,] 1.180178     0     0     0     0     0     0     0     0     0     0 #> [562,] 1.181263     0     0     0     0     0     0     0     0     0     0 #> [563,] 1.181173     0     0     0     0     0     0     0     0     0     0 #> [564,] 1.181082     0     0     0     0     0     0     0     0     0     0 #> [565,] 1.180992     0     0     0     0     0     0     0     0     0     0 #> [566,] 1.180902     0     0     0     0     0     0     0     0     0     0 #> [567,] 1.178824     0     0     0     0     0     0     0     0     0     0 #> [568,] 1.178733     0     0     0     0     0     0     0     0     0     0 #> [569,] 1.178643     0     0     0     0     0     0     0     0     0     0 #> [570,] 1.326041     0     0     0     0     0     0     0     0     0     0 #> [571,] 1.325635     0     0     0     0     0     0     0     0     0     0 #> [572,] 1.325940     0     0     0     0     0     0     0     0     0     0 #> [573,] 1.325838     0     0     0     0     0     0     0     0     0     0 #> [574,] 1.325736     0     0     0     0     0     0     0     0     0     0 #> [575,] 1.326650     0     0     0     0     0     0     0     0     0     0 #> [576,] 1.325533     0     0     0     0     0     0     0     0     0     0 #> [577,] 1.325432     0     0     0     0     0     0     0     0     0     0 #> [578,] 1.325331     0     0     0     0     0     0     0     0     0     0 #> [579,] 1.326244     0     0     0     0     0     0     0     0     0     0 #> [580,] 1.326143     0     0     0     0     0     0     0     0     0     0 #> [581,] 1.322695     0     0     0     0     0     0     0     0     0     0 #> [582,] 1.322593     0     0     0     0     0     0     0     0     0     0 #> [583,] 1.324620     0     0     0     0     0     0     0     0     0     0 #> [584,] 1.324519     0     0     0     0     0     0     0     0     0     0 #> [585,] 1.324418     0     0     0     0     0     0     0     0     0     0 #> [586,] 1.324316     0     0     0     0     0     0     0     0     0     0 #> [587,] 1.324215     0     0     0     0     0     0     0     0     0     0 #> [588,] 1.324113     0     0     0     0     0     0     0     0     0     0 #> [589,] 1.326549     0     0     0     0     0     0     0     0     0     0 #> [590,] 1.326447     0     0     0     0     0     0     0     0     0     0 #> [591,] 1.326346     0     0     0     0     0     0     0     0     0     0 #> [592,] 1.325026     0     0     0     0     0     0     0     0     0     0 #> [593,] 1.324925     0     0     0     0     0     0     0     0     0     0 #> [594,] 1.324823     0     0     0     0     0     0     0     0     0     0 #> [595,] 1.324722     0     0     0     0     0     0     0     0     0     0 #> [596,] 1.323303     0     0     0     0     0     0     0     0     0     0 #> [597,] 1.323201     0     0     0     0     0     0     0     0     0     0 #> [598,] 1.323100     0     0     0     0     0     0     0     0     0     0 #> [599,] 1.322999     0     0     0     0     0     0     0     0     0     0 #> [600,] 1.322897     0     0     0     0     0     0     0     0     0     0 #> [601,] 1.322796     0     0     0     0     0     0     0     0     0     0 #> [602,] 1.324012     0     0     0     0     0     0     0     0     0     0 #> [603,] 1.323911     0     0     0     0     0     0     0     0     0     0 #> [604,] 1.323809     0     0     0     0     0     0     0     0     0     0 #> [605,] 1.323708     0     0     0     0     0     0     0     0     0     0 #> [606,] 1.323607     0     0     0     0     0     0     0     0     0     0 #> [607,] 1.323505     0     0     0     0     0     0     0     0     0     0 #> [608,] 1.323404     0     0     0     0     0     0     0     0     0     0 #> [609,] 1.325229     0     0     0     0     0     0     0     0     0     0 #> [610,] 1.325128     0     0     0     0     0     0     0     0     0     0 #> [611,] 1.388167     0     0     0     0     0     0     0     0     0     0 #> [612,] 1.388273     0     0     0     0     0     0     0     0     0     0 #> [613,] 1.387954     0     0     0     0     0     0     0     0     0     0 #> [614,] 1.388060     0     0     0     0     0     0     0     0     0     0 #> [615,] 1.389553     0     0     0     0     0     0     0     0     0     0 #> [616,] 1.389447     0     0     0     0     0     0     0     0     0     0 #> [617,] 1.388379     0     0     0     0     0     0     0     0     0     0 #> [618,] 1.388486     0     0     0     0     0     0     0     0     0     0 #> [619,] 1.389128     0     0     0     0     0     0     0     0     0     0 #> [620,] 1.389022     0     0     0     0     0     0     0     0     0     0 #> [621,] 1.388915     0     0     0     0     0     0     0     0     0     0 #> [622,] 1.388809     0     0     0     0     0     0     0     0     0     0 #> [623,] 1.388703     0     0     0     0     0     0     0     0     0     0 #> [624,] 1.388596     0     0     0     0     0     0     0     0     0     0 #> [625,] 1.388490     0     0     0     0     0     0     0     0     0     0 #> [626,] 1.388384     0     0     0     0     0     0     0     0     0     0 #> [627,] 1.388277     0     0     0     0     0     0     0     0     0     0 #> [628,] 1.388171     0     0     0     0     0     0     0     0     0     0 #> [629,] 1.388065     0     0     0     0     0     0     0     0     0     0 #> [630,] 1.389341     0     0     0     0     0     0     0     0     0     0 #> [631,] 1.389234     0     0     0     0     0     0     0     0     0     0 #> [632,] 1.388592     0     0     0     0     0     0     0     0     0     0 #> [633,] 1.388698     0     0     0     0     0     0     0     0     0     0 #> [634,] 1.388805     0     0     0     0     0     0     0     0     0     0 #> [635,] 1.388911     0     0     0     0     0     0     0     0     0     0 #> [636,] 1.389017     0     0     0     0     0     0     0     0     0     0 #> [637,] 1.389124     0     0     0     0     0     0     0     0     0     0 #> [638,] 1.389230     0     0     0     0     0     0     0     0     0     0 #> [639,] 1.389336     0     0     0     0     0     0     0     0     0     0 #> [640,] 1.389443     0     0     0     0     0     0     0     0     0     0 #> [641,] 1.389549     0     0     0     0     0     0     0     0     0     0 #> [642,] 1.389656     0     0     0     0     0     0     0     0     0     0 #> [643,] 1.389762     0     0     0     0     0     0     0     0     0     0 #> [644,] 1.389868     0     0     0     0     0     0     0     0     0     0 #> [645,] 1.389975     0     0     0     0     0     0     0     0     0     0 #> [646,] 1.390081     0     0     0     0     0     0     0     0     0     0 #> [647,] 1.390188     0     0     0     0     0     0     0     0     0     0 #> [648,] 1.390294     0     0     0     0     0     0     0     0     0     0 #> [649,] 1.390192     0     0     0     0     0     0     0     0     0     0 #> [650,] 1.390086     0     0     0     0     0     0     0     0     0     0 #> [651,] 1.389979     0     0     0     0     0     0     0     0     0     0 #> [652,] 1.389873     0     0     0     0     0     0     0     0     0     0 #> [653,] 1.389766     0     0     0     0     0     0     0     0     0     0 #> [654,] 1.389660     0     0     0     0     0     0     0     0     0     0 #>        [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [,41] [,42] [,43] #>   [1,]     0     0     0     0     0     0     0     0     0     0     0 #>   [2,]     0     0     0     0     0     0     0     0     0     0     0 #>   [3,]     0     0     0     0     0     0     0     0     0     0     0 #>   [4,]     0     0     0     0     0     0     0     0     0     0     0 #>   [5,]     0     0     0     0     0     0     0     0     0     0     0 #>   [6,]     0     0     0     0     0     0     0     0     0     0     0 #>   [7,]     0     0     0     0     0     0     0     0     0     0     0 #>   [8,]     0     0     0     0     0     0     0     0     0     0     0 #>   [9,]     0     0     0     0     0     0     0     0     0     0     0 #>  [10,]     0     0     0     0     0     0     0     0     0     0     0 #>  [11,]     0     0     0     0     0     0     0     0     0     0     0 #>  [12,]     0     0     0     0     0     0     0     0     0     0     0 #>  [13,]     0     0     0     0     0     0     0     0     0     0     0 #>  [14,]     0     0     0     0     0     0     0     0     0     0     0 #>  [15,]     0     0     0     0     0     0     0     0     0     0     0 #>  [16,]     0     0     0     0     0     0     0     0     0     0     0 #>  [17,]     0     0     0     0     0     0     0     0     0     0     0 #>  [18,]     0     0     0     0     0     0     0     0     0     0     0 #>  [19,]     0     0     0     0     0     0     0     0     0     0     0 #>  [20,]     0     0     0     0     0     0     0     0     0     0     0 #>  [21,]     0     0     0     0     0     0     0     0     0     0     0 #>  [22,]     0     0     0     0     0     0     0     0     0     0     0 #>  [23,]     0     0     0     0     0     0     0     0     0     0     0 #>  [24,]     0     0     0     0     0     0     0     0     0     0     0 #>  [25,]     0     0     0     0     0     0     0     0     0     0     0 #>  [26,]     0     0     0     0     0     0     0     0     0     0     0 #>  [27,]     0     0     0     0     0     0     0     0     0     0     0 #>  [28,]     0     0     0     0     0     0     0     0     0     0     0 #>  [29,]     0     0     0     0     0     0     0     0     0     0     0 #>  [30,]     0     0     0     0     0     0     0     0     0     0     0 #>  [31,]     0     0     0     0     0     0     0     0     0     0     0 #>  [32,]     0     0     0     0     0     0     0     0     0     0     0 #>  [33,]     0     0     0     0     0     0     0     0     0     0     0 #>  [34,]     0     0     0     0     0     0     0     0     0     0     0 #>  [35,]     0     0     0     0     0     0     0     0     0     0     0 #>  [36,]     0     0     0     0     0     0     0     0     0     0     0 #>  [37,]     0     0     0     0     0     0     0     0     0     0     0 #>  [38,]     0     0     0     0     0     0     0     0     0     0     0 #>  [39,]     0     0     0     0     0     0     0     0     0     0     0 #>  [40,]     0     0     0     0     0     0     0     0     0     0     0 #>  [41,]     0     0     0     0     0     0     0     0     0     0     0 #>  [42,]     0     0     0     0     0     0     0     0     0     0     0 #>  [43,]     0     0     0     0     0     0     0     0     0     0     0 #>  [44,]     0     0     0     0     0     0     0     0     0     0     0 #>  [45,]     0     0     0     0     0     0     0     0     0     0     0 #>  [46,]     0     0     0     0     0     0     0     0     0     0     0 #>  [47,]     0     0     0     0     0     0     0     0     0     0     0 #>  [48,]     0     0     0     0     0     0     0     0     0     0     0 #>  [49,]     0     0     0     0     0     0     0     0     0     0     0 #>  [50,]     0     0     0     0     0     0     0     0     0     0     0 #>  [51,]     0     0     0     0     0     0     0     0     0     0     0 #>  [52,]     0     0     0     0     0     0     0     0     0     0     0 #>  [53,]     0     0     0     0     0     0     0     0     0     0     0 #>  [54,]     0     0     0     0     0     0     0     0     0     0     0 #>  [55,]     0     0     0     0     0     0     0     0     0     0     0 #>  [56,]     0     0     0     0     0     0     0     0     0     0     0 #>  [57,]     0     0     0     0     0     0     0     0     0     0     0 #>  [58,]     0     0     0     0     0     0     0     0     0     0     0 #>  [59,]     0     0     0     0     0     0     0     0     0     0     0 #>  [60,]     0     0     0     0     0     0     0     0     0     0     0 #>  [61,]     0     0     0     0     0     0     0     0     0     0     0 #>  [62,]     0     0     0     0     0     0     0     0     0     0     0 #>  [63,]     0     0     0     0     0     0     0     0     0     0     0 #>  [64,]     0     0     0     0     0     0     0     0     0     0     0 #>  [65,]     0     0     0     0     0     0     0     0     0     0     0 #>  [66,]     0     0     0     0     0     0     0     0     0     0     0 #>  [67,]     0     0     0     0     0     0     0     0     0     0     0 #>  [68,]     0     0     0     0     0     0     0     0     0     0     0 #>  [69,]     0     0     0     0     0     0     0     0     0     0     0 #>  [70,]     0     0     0     0     0     0     0     0     0     0     0 #>  [71,]     0     0     0     0     0     0     0     0     0     0     0 #>  [72,]     0     0     0     0     0     0     0     0     0     0     0 #>  [73,]     0     0     0     0     0     0     0     0     0     0     0 #>  [74,]     0     0     0     0     0     0     0     0     0     0     0 #>  [75,]     0     0     0     0     0     0     0     0     0     0     0 #>  [76,]     0     0     0     0     0     0     0     0     0     0     0 #>  [77,]     0     0     0     0     0     0     0     0     0     0     0 #>  [78,]     0     0     0     0     0     0     0     0     0     0     0 #>  [79,]     0     0     0     0     0     0     0     0     0     0     0 #>  [80,]     0     0     0     0     0     0     0     0     0     0     0 #>  [81,]     0     0     0     0     0     0     0     0     0     0     0 #>  [82,]     0     0     0     0     0     0     0     0     0     0     0 #>  [83,]     0     0     0     0     0     0     0     0     0     0     0 #>  [84,]     0     0     0     0     0     0     0     0     0     0     0 #>  [85,]     0     0     0     0     0     0     0     0     0     0     0 #>  [86,]     0     0     0     0     0     0     0     0     0     0     0 #>  [87,]     0     0     0     0     0     0     0     0     0     0     0 #>  [88,]     0     0     0     0     0     0     0     0     0     0     0 #>  [89,]     0     0     0     0     0     0     0     0     0     0     0 #>  [90,]     0     0     0     0     0     0     0     0     0     0     0 #>  [91,]     0     0     0     0     0     0     0     0     0     0     0 #>  [92,]     0     0     0     0     0     0     0     0     0     0     0 #>  [93,]     0     0     0     0     0     0     0     0     0     0     0 #>  [94,]     0     0     0     0     0     0     0     0     0     0     0 #>  [95,]     0     0     0     0     0     0     0     0     0     0     0 #>  [96,]     0     0     0     0     0     0     0     0     0     0     0 #>  [97,]     0     0     0     0     0     0     0     0     0     0     0 #>  [98,]     0     0     0     0     0     0     0     0     0     0     0 #>  [99,]     0     0     0     0     0     0     0     0     0     0     0 #> [100,]     0     0     0     0     0     0     0     0     0     0     0 #> [101,]     0     0     0     0     0     0     0     0     0     0     0 #> [102,]     0     0     0     0     0     0     0     0     0     0     0 #> [103,]     0     0     0     0     0     0     0     0     0     0     0 #> [104,]     0     0     0     0     0     0     0     0     0     0     0 #> [105,]     0     0     0     0     0     0     0     0     0     0     0 #> [106,]     0     0     0     0     0     0     0     0     0     0     0 #> [107,]     0     0     0     0     0     0     0     0     0     0     0 #> [108,]     0     0     0     0     0     0     0     0     0     0     0 #> [109,]     0     0     0     0     0     0     0     0     0     0     0 #> [110,]     0     0     0     0     0     0     0     0     0     0     0 #> [111,]     0     0     0     0     0     0     0     0     0     0     0 #> [112,]     0     0     0     0     0     0     0     0     0     0     0 #> [113,]     0     0     0     0     0     0     0     0     0     0     0 #> [114,]     0     0     0     0     0     0     0     0     0     0     0 #> [115,]     0     0     0     0     0     0     0     0     0     0     0 #> [116,]     0     0     0     0     0     0     0     0     0     0     0 #> [117,]     0     0     0     0     0     0     0     0     0     0     0 #> [118,]     0     0     0     0     0     0     0     0     0     0     0 #> [119,]     0     0     0     0     0     0     0     0     0     0     0 #> [120,]     0     0     0     0     0     0     0     0     0     0     0 #> [121,]     0     0     0     0     0     0     0     0     0     0     0 #> [122,]     0     0     0     0     0     0     0     0     0     0     0 #> [123,]     0     0     0     0     0     0     0     0     0     0     0 #> [124,]     0     0     0     0     0     0     0     0     0     0     0 #> [125,]     0     0     0     0     0     0     0     0     0     0     0 #> [126,]     0     0     0     0     0     0     0     0     0     0     0 #> [127,]     0     0     0     0     0     0     0     0     0     0     0 #> [128,]     0     0     0     0     0     0     0     0     0     0     0 #> [129,]     0     0     0     0     0     0     0     0     0     0     0 #> [130,]     0     0     0     0     0     0     0     0     0     0     0 #> [131,]     0     0     0     0     0     0     0     0     0     0     0 #> [132,]     0     0     0     0     0     0     0     0     0     0     0 #> [133,]     0     0     0     0     0     0     0     0     0     0     0 #> [134,]     0     0     0     0     0     0     0     0     0     0     0 #> [135,]     0     0     0     0     0     0     0     0     0     0     0 #> [136,]     0     0     0     0     0     0     0     0     0     0     0 #> [137,]     0     0     0     0     0     0     0     0     0     0     0 #> [138,]     0     0     0     0     0     0     0     0     0     0     0 #> [139,]     0     0     0     0     0     0     0     0     0     0     0 #> [140,]     0     0     0     0     0     0     0     0     0     0     0 #> [141,]     0     0     0     0     0     0     0     0     0     0     0 #> [142,]     0     0     0     0     0     0     0     0     0     0     0 #> [143,]     0     0     0     0     0     0     0     0     0     0     0 #> [144,]     0     0     0     0     0     0     0     0     0     0     0 #> [145,]     0     0     0     0     0     0     0     0     0     0     0 #> [146,]     0     0     0     0     0     0     0     0     0     0     0 #> [147,]     0     0     0     0     0     0     0     0     0     0     0 #> [148,]     0     0     0     0     0     0     0     0     0     0     0 #> [149,]     0     0     0     0     0     0     0     0     0     0     0 #> [150,]     0     0     0     0     0     0     0     0     0     0     0 #> [151,]     0     0     0     0     0     0     0     0     0     0     0 #> [152,]     0     0     0     0     0     0     0     0     0     0     0 #> [153,]     0     0     0     0     0     0     0     0     0     0     0 #> [154,]     0     0     0     0     0     0     0     0     0     0     0 #> [155,]     0     0     0     0     0     0     0     0     0     0     0 #> [156,]     0     0     0     0     0     0     0     0     0     0     0 #> [157,]     0     0     0     0     0     0     0     0     0     0     0 #> [158,]     0     0     0     0     0     0     0     0     0     0     0 #> [159,]     0     0     0     0     0     0     0     0     0     0     0 #> [160,]     0     0     0     0     0     0     0     0     0     0     0 #> [161,]     0     0     0     0     0     0     0     0     0     0     0 #> [162,]     0     0     0     0     0     0     0     0     0     0     0 #> [163,]     0     0     0     0     0     0     0     0     0     0     0 #> [164,]     0     0     0     0     0     0     0     0     0     0     0 #> [165,]     0     0     0     0     0     0     0     0     0     0     0 #> [166,]     0     0     0     0     0     0     0     0     0     0     0 #> [167,]     0     0     0     0     0     0     0     0     0     0     0 #> [168,]     0     0     0     0     0     0     0     0     0     0     0 #> [169,]     0     0     0     0     0     0     0     0     0     0     0 #> [170,]     0     0     0     0     0     0     0     0     0     0     0 #> [171,]     0     0     0     0     0     0     0     0     0     0     0 #> [172,]     0     0     0     0     0     0     0     0     0     0     0 #> [173,]     0     0     0     0     0     0     0     0     0     0     0 #> [174,]     0     0     0     0     0     0     0     0     0     0     0 #> [175,]     0     0     0     0     0     0     0     0     0     0     0 #> [176,]     0     0     0     0     0     0     0     0     0     0     0 #> [177,]     0     0     0     0     0     0     0     0     0     0     0 #> [178,]     0     0     0     0     0     0     0     0     0     0     0 #> [179,]     0     0     0     0     0     0     0     0     0     0     0 #> [180,]     0     0     0     0     0     0     0     0     0     0     0 #> [181,]     0     0     0     0     0     0     0     0     0     0     0 #> [182,]     0     0     0     0     0     0     0     0     0     0     0 #> [183,]     0     0     0     0     0     0     0     0     0     0     0 #> [184,]     0     0     0     0     0     0     0     0     0     0     0 #> [185,]     0     0     0     0     0     0     0     0     0     0     0 #> [186,]     0     0     0     0     0     0     0     0     0     0     0 #> [187,]     0     0     0     0     0     0     0     0     0     0     0 #> [188,]     0     0     0     0     0     0     0     0     0     0     0 #> [189,]     0     0     0     0     0     0     0     0     0     0     0 #> [190,]     0     0     0     0     0     0     0     0     0     0     0 #> [191,]     0     0     0     0     0     0     0     0     0     0     0 #> [192,]     0     0     0     0     0     0     0     0     0     0     0 #> [193,]     0     0     0     0     0     0     0     0     0     0     0 #> [194,]     0     0     0     0     0     0     0     0     0     0     0 #> [195,]     0     0     0     0     0     0     0     0     0     0     0 #> [196,]     0     0     0     0     0     0     0     0     0     0     0 #> [197,]     0     0     0     0     0     0     0     0     0     0     0 #> [198,]     0     0     0     0     0     0     0     0     0     0     0 #> [199,]     0     0     0     0     0     0     0     0     0     0     0 #> [200,]     0     0     0     0     0     0     0     0     0     0     0 #> [201,]     0     0     0     0     0     0     0     0     0     0     0 #> [202,]     0     0     0     0     0     0     0     0     0     0     0 #> [203,]     0     0     0     0     0     0     0     0     0     0     0 #> [204,]     0     0     0     0     0     0     0     0     0     0     0 #> [205,]     0     0     0     0     0     0     0     0     0     0     0 #> [206,]     0     0     0     0     0     0     0     0     0     0     0 #> [207,]     0     0     0     0     0     0     0     0     0     0     0 #> [208,]     0     0     0     0     0     0     0     0     0     0     0 #> [209,]     0     0     0     0     0     0     0     0     0     0     0 #> [210,]     0     0     0     0     0     0     0     0     0     0     0 #> [211,]     0     0     0     0     0     0     0     0     0     0     0 #> [212,]     0     0     0     0     0     0     0     0     0     0     0 #> [213,]     0     0     0     0     0     0     0     0     0     0     0 #> [214,]     0     0     0     0     0     0     0     0     0     0     0 #> [215,]     0     0     0     0     0     0     0     0     0     0     0 #> [216,]     0     0     0     0     0     0     0     0     0     0     0 #> [217,]     0     0     0     0     0     0     0     0     0     0     0 #> [218,]     0     0     0     0     0     0     0     0     0     0     0 #> [219,]     0     0     0     0     0     0     0     0     0     0     0 #> [220,]     0     0     0     0     0     0     0     0     0     0     0 #> [221,]     0     0     0     0     0     0     0     0     0     0     0 #> [222,]     0     0     0     0     0     0     0     0     0     0     0 #> [223,]     0     0     0     0     0     0     0     0     0     0     0 #> [224,]     0     0     0     0     0     0     0     0     0     0     0 #> [225,]     0     0     0     0     0     0     0     0     0     0     0 #> [226,]     0     0     0     0     0     0     0     0     0     0     0 #> [227,]     0     0     0     0     0     0     0     0     0     0     0 #> [228,]     0     0     0     0     0     0     0     0     0     0     0 #> [229,]     0     0     0     0     0     0     0     0     0     0     0 #> [230,]     0     0     0     0     0     0     0     0     0     0     0 #> [231,]     0     0     0     0     0     0     0     0     0     0     0 #> [232,]     0     0     0     0     0     0     0     0     0     0     0 #> [233,]     0     0     0     0     0     0     0     0     0     0     0 #> [234,]     0     0     0     0     0     0     0     0     0     0     0 #> [235,]     0     0     0     0     0     0     0     0     0     0     0 #> [236,]     0     0     0     0     0     0     0     0     0     0     0 #> [237,]     0     0     0     0     0     0     0     0     0     0     0 #> [238,]     0     0     0     0     0     0     0     0     0     0     0 #> [239,]     0     0     0     0     0     0     0     0     0     0     0 #> [240,]     0     0     0     0     0     0     0     0     0     0     0 #> [241,]     0     0     0     0     0     0     0     0     0     0     0 #> [242,]     0     0     0     0     0     0     0     0     0     0     0 #> [243,]     0     0     0     0     0     0     0     0     0     0     0 #> [244,]     0     0     0     0     0     0     0     0     0     0     0 #> [245,]     0     0     0     0     0     0     0     0     0     0     0 #> [246,]     0     0     0     0     0     0     0     0     0     0     0 #> [247,]     0     0     0     0     0     0     0     0     0     0     0 #> [248,]     0     0     0     0     0     0     0     0     0     0     0 #> [249,]     0     0     0     0     0     0     0     0     0     0     0 #> [250,]     0     0     0     0     0     0     0     0     0     0     0 #> [251,]     0     0     0     0     0     0     0     0     0     0     0 #> [252,]     0     0     0     0     0     0     0     0     0     0     0 #> [253,]     0     0     0     0     0     0     0     0     0     0     0 #> [254,]     0     0     0     0     0     0     0     0     0     0     0 #> [255,]     0     0     0     0     0     0     0     0     0     0     0 #> [256,]     0     0     0     0     0     0     0     0     0     0     0 #> [257,]     0     0     0     0     0     0     0     0     0     0     0 #> [258,]     0     0     0     0     0     0     0     0     0     0     0 #> [259,]     0     0     0     0     0     0     0     0     0     0     0 #> [260,]     0     0     0     0     0     0     0     0     0     0     0 #> [261,]     0     0     0     0     0     0     0     0     0     0     0 #> [262,]     0     0     0     0     0     0     0     0     0     0     0 #> [263,]     0     0     0     0     0     0     0     0     0     0     0 #> [264,]     0     0     0     0     0     0     0     0     0     0     0 #> [265,]     0     0     0     0     0     0     0     0     0     0     0 #> [266,]     0     0     0     0     0     0     0     0     0     0     0 #> [267,]     0     0     0     0     0     0     0     0     0     0     0 #> [268,]     0     0     0     0     0     0     0     0     0     0     0 #> [269,]     0     0     0     0     0     0     0     0     0     0     0 #> [270,]     0     0     0     0     0     0     0     0     0     0     0 #> [271,]     0     0     0     0     0     0     0     0     0     0     0 #> [272,]     0     0     0     0     0     0     0     0     0     0     0 #> [273,]     0     0     0     0     0     0     0     0     0     0     0 #> [274,]     0     0     0     0     0     0     0     0     0     0     0 #> [275,]     0     0     0     0     0     0     0     0     0     0     0 #> [276,]     0     0     0     0     0     0     0     0     0     0     0 #> [277,]     0     0     0     0     0     0     0     0     0     0     0 #> [278,]     0     0     0     0     0     0     0     0     0     0     0 #> [279,]     0     0     0     0     0     0     0     0     0     0     0 #> [280,]     0     0     0     0     0     0     0     0     0     0     0 #> [281,]     0     0     0     0     0     0     0     0     0     0     0 #> [282,]     0     0     0     0     0     0     0     0     0     0     0 #> [283,]     0     0     0     0     0     0     0     0     0     0     0 #> [284,]     0     0     0     0     0     0     0     0     0     0     0 #> [285,]     0     0     0     0     0     0     0     0     0     0     0 #> [286,]     0     0     0     0     0     0     0     0     0     0     0 #> [287,]     0     0     0     0     0     0     0     0     0     0     0 #> [288,]     0     0     0     0     0     0     0     0     0     0     0 #> [289,]     0     0     0     0     0     0     0     0     0     0     0 #> [290,]     0     0     0     0     0     0     0     0     0     0     0 #> [291,]     0     0     0     0     0     0     0     0     0     0     0 #> [292,]     0     0     0     0     0     0     0     0     0     0     0 #> [293,]     0     0     0     0     0     0     0     0     0     0     0 #> [294,]     0     0     0     0     0     0     0     0     0     0     0 #> [295,]     0     0     0     0     0     0     0     0     0     0     0 #> [296,]     0     0     0     0     0     0     0     0     0     0     0 #> [297,]     0     0     0     0     0     0     0     0     0     0     0 #> [298,]     0     0     0     0     0     0     0     0     0     0     0 #> [299,]     0     0     0     0     0     0     0     0     0     0     0 #> [300,]     0     0     0     0     0     0     0     0     0     0     0 #> [301,]     0     0     0     0     0     0     0     0     0     0     0 #> [302,]     0     0     0     0     0     0     0     0     0     0     0 #> [303,]     0     0     0     0     0     0     0     0     0     0     0 #> [304,]     0     0     0     0     0     0     0     0     0     0     0 #> [305,]     0     0     0     0     0     0     0     0     0     0     0 #> [306,]     0     0     0     0     0     0     0     0     0     0     0 #> [307,]     0     0     0     0     0     0     0     0     0     0     0 #> [308,]     0     0     0     0     0     0     0     0     0     0     0 #> [309,]     0     0     0     0     0     0     0     0     0     0     0 #> [310,]     0     0     0     0     0     0     0     0     0     0     0 #> [311,]     0     0     0     0     0     0     0     0     0     0     0 #> [312,]     0     0     0     0     0     0     0     0     0     0     0 #> [313,]     0     0     0     0     0     0     0     0     0     0     0 #> [314,]     0     0     0     0     0     0     0     0     0     0     0 #> [315,]     0     0     0     0     0     0     0     0     0     0     0 #> [316,]     0     0     0     0     0     0     0     0     0     0     0 #> [317,]     0     0     0     0     0     0     0     0     0     0     0 #> [318,]     0     0     0     0     0     0     0     0     0     0     0 #> [319,]     0     0     0     0     0     0     0     0     0     0     0 #> [320,]     0     0     0     0     0     0     0     0     0     0     0 #> [321,]     0     0     0     0     0     0     0     0     0     0     0 #> [322,]     0     0     0     0     0     0     0     0     0     0     0 #> [323,]     0     0     0     0     0     0     0     0     0     0     0 #> [324,]     0     0     0     0     0     0     0     0     0     0     0 #> [325,]     0     0     0     0     0     0     0     0     0     0     0 #> [326,]     0     0     0     0     0     0     0     0     0     0     0 #> [327,]     0     0     0     0     0     0     0     0     0     0     0 #> [328,]     0     0     0     0     0     0     0     0     0     0     0 #> [329,]     0     0     0     0     0     0     0     0     0     0     0 #> [330,]     0     0     0     0     0     0     0     0     0     0     0 #> [331,]     0     0     0     0     0     0     0     0     0     0     0 #> [332,]     0     0     0     0     0     0     0     0     0     0     0 #> [333,]     0     0     0     0     0     0     0     0     0     0     0 #> [334,]     0     0     0     0     0     0     0     0     0     0     0 #> [335,]     0     0     0     0     0     0     0     0     0     0     0 #> [336,]     0     0     0     0     0     0     0     0     0     0     0 #> [337,]     0     0     0     0     0     0     0     0     0     0     0 #> [338,]     0     0     0     0     0     0     0     0     0     0     0 #> [339,]     0     0     0     0     0     0     0     0     0     0     0 #> [340,]     0     0     0     0     0     0     0     0     0     0     0 #> [341,]     0     0     0     0     0     0     0     0     0     0     0 #> [342,]     0     0     0     0     0     0     0     0     0     0     0 #> [343,]     0     0     0     0     0     0     0     0     0     0     0 #> [344,]     0     0     0     0     0     0     0     0     0     0     0 #> [345,]     0     0     0     0     0     0     0     0     0     0     0 #> [346,]     0     0     0     0     0     0     0     0     0     0     0 #> [347,]     0     0     0     0     0     0     0     0     0     0     0 #> [348,]     0     0     0     0     0     0     0     0     0     0     0 #> [349,]     0     0     0     0     0     0     0     0     0     0     0 #> [350,]     0     0     0     0     0     0     0     0     0     0     0 #> [351,]     0     0     0     0     0     0     0     0     0     0     0 #> [352,]     0     0     0     0     0     0     0     0     0     0     0 #> [353,]     0     0     0     0     0     0     0     0     0     0     0 #> [354,]     0     0     0     0     0     0     0     0     0     0     0 #> [355,]     0     0     0     0     0     0     0     0     0     0     0 #> [356,]     0     0     0     0     0     0     0     0     0     0     0 #> [357,]     0     0     0     0     0     0     0     0     0     0     0 #> [358,]     0     0     0     0     0     0     0     0     0     0     0 #> [359,]     0     0     0     0     0     0     0     0     0     0     0 #> [360,]     0     0     0     0     0     0     0     0     0     0     0 #> [361,]     0     0     0     0     0     0     0     0     0     0     0 #> [362,]     0     0     0     0     0     0     0     0     0     0     0 #> [363,]     0     0     0     0     0     0     0     0     0     0     0 #> [364,]     0     0     0     0     0     0     0     0     0     0     0 #> [365,]     0     0     0     0     0     0     0     0     0     0     0 #> [366,]     0     0     0     0     0     0     0     0     0     0     0 #> [367,]     0     0     0     0     0     0     0     0     0     0     0 #> [368,]     0     0     0     0     0     0     0     0     0     0     0 #> [369,]     0     0     0     0     0     0     0     0     0     0     0 #> [370,]     0     0     0     0     0     0     0     0     0     0     0 #> [371,]     0     0     0     0     0     0     0     0     0     0     0 #> [372,]     0     0     0     0     0     0     0     0     0     0     0 #> [373,]     0     0     0     0     0     0     0     0     0     0     0 #> [374,]     0     0     0     0     0     0     0     0     0     0     0 #> [375,]     0     0     0     0     0     0     0     0     0     0     0 #> [376,]     0     0     0     0     0     0     0     0     0     0     0 #> [377,]     0     0     0     0     0     0     0     0     0     0     0 #> [378,]     0     0     0     0     0     0     0     0     0     0     0 #> [379,]     0     0     0     0     0     0     0     0     0     0     0 #> [380,]     0     0     0     0     0     0     0     0     0     0     0 #> [381,]     0     0     0     0     0     0     0     0     0     0     0 #> [382,]     0     0     0     0     0     0     0     0     0     0     0 #> [383,]     0     0     0     0     0     0     0     0     0     0     0 #> [384,]     0     0     0     0     0     0     0     0     0     0     0 #> [385,]     0     0     0     0     0     0     0     0     0     0     0 #> [386,]     0     0     0     0     0     0     0     0     0     0     0 #> [387,]     0     0     0     0     0     0     0     0     0     0     0 #> [388,]     0     0     0     0     0     0     0     0     0     0     0 #> [389,]     0     0     0     0     0     0     0     0     0     0     0 #> [390,]     0     0     0     0     0     0     0     0     0     0     0 #> [391,]     0     0     0     0     0     0     0     0     0     0     0 #> [392,]     0     0     0     0     0     0     0     0     0     0     0 #> [393,]     0     0     0     0     0     0     0     0     0     0     0 #> [394,]     0     0     0     0     0     0     0     0     0     0     0 #> [395,]     0     0     0     0     0     0     0     0     0     0     0 #> [396,]     0     0     0     0     0     0     0     0     0     0     0 #> [397,]     0     0     0     0     0     0     0     0     0     0     0 #> [398,]     0     0     0     0     0     0     0     0     0     0     0 #> [399,]     0     0     0     0     0     0     0     0     0     0     0 #> [400,]     0     0     0     0     0     0     0     0     0     0     0 #> [401,]     0     0     0     0     0     0     0     0     0     0     0 #> [402,]     0     0     0     0     0     0     0     0     0     0     0 #> [403,]     0     0     0     0     0     0     0     0     0     0     0 #> [404,]     0     0     0     0     0     0     0     0     0     0     0 #> [405,]     0     0     0     0     0     0     0     0     0     0     0 #> [406,]     0     0     0     0     0     0     0     0     0     0     0 #> [407,]     0     0     0     0     0     0     0     0     0     0     0 #> [408,]     0     0     0     0     0     0     0     0     0     0     0 #> [409,]     0     0     0     0     0     0     0     0     0     0     0 #> [410,]     0     0     0     0     0     0     0     0     0     0     0 #> [411,]     0     0     0     0     0     0     0     0     0     0     0 #> [412,]     0     0     0     0     0     0     0     0     0     0     0 #> [413,]     0     0     0     0     0     0     0     0     0     0     0 #> [414,]     0     0     0     0     0     0     0     0     0     0     0 #> [415,]     0     0     0     0     0     0     0     0     0     0     0 #> [416,]     0     0     0     0     0     0     0     0     0     0     0 #> [417,]     0     0     0     0     0     0     0     0     0     0     0 #> [418,]     0     0     0     0     0     0     0     0     0     0     0 #> [419,]     0     0     0     0     0     0     0     0     0     0     0 #> [420,]     0     0     0     0     0     0     0     0     0     0     0 #> [421,]     0     0     0     0     0     0     0     0     0     0     0 #> [422,]     0     0     0     0     0     0     0     0     0     0     0 #> [423,]     0     0     0     0     0     0     0     0     0     0     0 #> [424,]     0     0     0     0     0     0     0     0     0     0     0 #> [425,]     0     0     0     0     0     0     0     0     0     0     0 #> [426,]     0     0     0     0     0     0     0     0     0     0     0 #> [427,]     0     0     0     0     0     0     0     0     0     0     0 #> [428,]     0     0     0     0     0     0     0     0     0     0     0 #> [429,]     0     0     0     0     0     0     0     0     0     0     0 #> [430,]     0     0     0     0     0     0     0     0     0     0     0 #> [431,]     0     0     0     0     0     0     0     0     0     0     0 #> [432,]     0     0     0     0     0     0     0     0     0     0     0 #> [433,]     0     0     0     0     0     0     0     0     0     0     0 #> [434,]     0     0     0     0     0     0     0     0     0     0     0 #> [435,]     0     0     0     0     0     0     0     0     0     0     0 #> [436,]     0     0     0     0     0     0     0     0     0     0     0 #> [437,]     0     0     0     0     0     0     0     0     0     0     0 #> [438,]     0     0     0     0     0     0     0     0     0     0     0 #> [439,]     0     0     0     0     0     0     0     0     0     0     0 #> [440,]     0     0     0     0     0     0     0     0     0     0     0 #> [441,]     0     0     0     0     0     0     0     0     0     0     0 #> [442,]     0     0     0     0     0     0     0     0     0     0     0 #> [443,]     0     0     0     0     0     0     0     0     0     0     0 #> [444,]     0     0     0     0     0     0     0     0     0     0     0 #> [445,]     0     0     0     0     0     0     0     0     0     0     0 #> [446,]     0     0     0     0     0     0     0     0     0     0     0 #> [447,]     0     0     0     0     0     0     0     0     0     0     0 #> [448,]     0     0     0     0     0     0     0     0     0     0     0 #> [449,]     0     0     0     0     0     0     0     0     0     0     0 #> [450,]     0     0     0     0     0     0     0     0     0     0     0 #> [451,]     0     0     0     0     0     0     0     0     0     0     0 #> [452,]     0     0     0     0     0     0     0     0     0     0     0 #> [453,]     0     0     0     0     0     0     0     0     0     0     0 #> [454,]     0     0     0     0     0     0     0     0     0     0     0 #> [455,]     0     0     0     0     0     0     0     0     0     0     0 #> [456,]     0     0     0     0     0     0     0     0     0     0     0 #> [457,]     0     0     0     0     0     0     0     0     0     0     0 #> [458,]     0     0     0     0     0     0     0     0     0     0     0 #> [459,]     0     0     0     0     0     0     0     0     0     0     0 #> [460,]     0     0     0     0     0     0     0     0     0     0     0 #> [461,]     0     0     0     0     0     0     0     0     0     0     0 #> [462,]     0     0     0     0     0     0     0     0     0     0     0 #> [463,]     0     0     0     0     0     0     0     0     0     0     0 #> [464,]     0     0     0     0     0     0     0     0     0     0     0 #> [465,]     0     0     0     0     0     0     0     0     0     0     0 #> [466,]     0     0     0     0     0     0     0     0     0     0     0 #> [467,]     0     0     0     0     0     0     0     0     0     0     0 #> [468,]     0     0     0     0     0     0     0     0     0     0     0 #> [469,]     0     0     0     0     0     0     0     0     0     0     0 #> [470,]     0     0     0     0     0     0     0     0     0     0     0 #> [471,]     0     0     0     0     0     0     0     0     0     0     0 #> [472,]     0     0     0     0     0     0     0     0     0     0     0 #> [473,]     0     0     0     0     0     0     0     0     0     0     0 #> [474,]     0     0     0     0     0     0     0     0     0     0     0 #> [475,]     0     0     0     0     0     0     0     0     0     0     0 #> [476,]     0     0     0     0     0     0     0     0     0     0     0 #> [477,]     0     0     0     0     0     0     0     0     0     0     0 #> [478,]     0     0     0     0     0     0     0     0     0     0     0 #> [479,]     0     0     0     0     0     0     0     0     0     0     0 #> [480,]     0     0     0     0     0     0     0     0     0     0     0 #> [481,]     0     0     0     0     0     0     0     0     0     0     0 #> [482,]     0     0     0     0     0     0     0     0     0     0     0 #> [483,]     0     0     0     0     0     0     0     0     0     0     0 #> [484,]     0     0     0     0     0     0     0     0     0     0     0 #> [485,]     0     0     0     0     0     0     0     0     0     0     0 #> [486,]     0     0     0     0     0     0     0     0     0     0     0 #> [487,]     0     0     0     0     0     0     0     0     0     0     0 #> [488,]     0     0     0     0     0     0     0     0     0     0     0 #> [489,]     0     0     0     0     0     0     0     0     0     0     0 #> [490,]     0     0     0     0     0     0     0     0     0     0     0 #> [491,]     0     0     0     0     0     0     0     0     0     0     0 #> [492,]     0     0     0     0     0     0     0     0     0     0     0 #> [493,]     0     0     0     0     0     0     0     0     0     0     0 #> [494,]     0     0     0     0     0     0     0     0     0     0     0 #> [495,]     0     0     0     0     0     0     0     0     0     0     0 #> [496,]     0     0     0     0     0     0     0     0     0     0     0 #> [497,]     0     0     0     0     0     0     0     0     0     0     0 #> [498,]     0     0     0     0     0     0     0     0     0     0     0 #> [499,]     0     0     0     0     0     0     0     0     0     0     0 #> [500,]     0     0     0     0     0     0     0     0     0     0     0 #> [501,]     0     0     0     0     0     0     0     0     0     0     0 #> [502,]     0     0     0     0     0     0     0     0     0     0     0 #> [503,]     0     0     0     0     0     0     0     0     0     0     0 #> [504,]     0     0     0     0     0     0     0     0     0     0     0 #> [505,]     0     0     0     0     0     0     0     0     0     0     0 #> [506,]     0     0     0     0     0     0     0     0     0     0     0 #> [507,]     0     0     0     0     0     0     0     0     0     0     0 #> [508,]     0     0     0     0     0     0     0     0     0     0     0 #> [509,]     0     0     0     0     0     0     0     0     0     0     0 #> [510,]     0     0     0     0     0     0     0     0     0     0     0 #> [511,]     0     0     0     0     0     0     0     0     0     0     0 #> [512,]     0     0     0     0     0     0     0     0     0     0     0 #> [513,]     0     0     0     0     0     0     0     0     0     0     0 #> [514,]     0     0     0     0     0     0     0     0     0     0     0 #> [515,]     0     0     0     0     0     0     0     0     0     0     0 #> [516,]     0     0     0     0     0     0     0     0     0     0     0 #> [517,]     0     0     0     0     0     0     0     0     0     0     0 #> [518,]     0     0     0     0     0     0     0     0     0     0     0 #> [519,]     0     0     0     0     0     0     0     0     0     0     0 #> [520,]     0     0     0     0     0     0     0     0     0     0     0 #> [521,]     0     0     0     0     0     0     0     0     0     0     0 #> [522,]     0     0     0     0     0     0     0     0     0     0     0 #> [523,]     0     0     0     0     0     0     0     0     0     0     0 #> [524,]     0     0     0     0     0     0     0     0     0     0     0 #> [525,]     0     0     0     0     0     0     0     0     0     0     0 #> [526,]     0     0     0     0     0     0     0     0     0     0     0 #> [527,]     0     0     0     0     0     0     0     0     0     0     0 #> [528,]     0     0     0     0     0     0     0     0     0     0     0 #> [529,]     0     0     0     0     0     0     0     0     0     0     0 #> [530,]     0     0     0     0     0     0     0     0     0     0     0 #> [531,]     0     0     0     0     0     0     0     0     0     0     0 #> [532,]     0     0     0     0     0     0     0     0     0     0     0 #> [533,]     0     0     0     0     0     0     0     0     0     0     0 #> [534,]     0     0     0     0     0     0     0     0     0     0     0 #> [535,]     0     0     0     0     0     0     0     0     0     0     0 #> [536,]     0     0     0     0     0     0     0     0     0     0     0 #> [537,]     0     0     0     0     0     0     0     0     0     0     0 #> [538,]     0     0     0     0     0     0     0     0     0     0     0 #> [539,]     0     0     0     0     0     0     0     0     0     0     0 #> [540,]     0     0     0     0     0     0     0     0     0     0     0 #> [541,]     0     0     0     0     0     0     0     0     0     0     0 #> [542,]     0     0     0     0     0     0     0     0     0     0     0 #> [543,]     0     0     0     0     0     0     0     0     0     0     0 #> [544,]     0     0     0     0     0     0     0     0     0     0     0 #> [545,]     0     0     0     0     0     0     0     0     0     0     0 #> [546,]     0     0     0     0     0     0     0     0     0     0     0 #> [547,]     0     0     0     0     0     0     0     0     0     0     0 #> [548,]     0     0     0     0     0     0     0     0     0     0     0 #> [549,]     0     0     0     0     0     0     0     0     0     0     0 #> [550,]     0     0     0     0     0     0     0     0     0     0     0 #> [551,]     0     0     0     0     0     0     0     0     0     0     0 #> [552,]     0     0     0     0     0     0     0     0     0     0     0 #> [553,]     0     0     0     0     0     0     0     0     0     0     0 #> [554,]     0     0     0     0     0     0     0     0     0     0     0 #> [555,]     0     0     0     0     0     0     0     0     0     0     0 #> [556,]     0     0     0     0     0     0     0     0     0     0     0 #> [557,]     0     0     0     0     0     0     0     0     0     0     0 #> [558,]     0     0     0     0     0     0     0     0     0     0     0 #> [559,]     0     0     0     0     0     0     0     0     0     0     0 #> [560,]     0     0     0     0     0     0     0     0     0     0     0 #> [561,]     0     0     0     0     0     0     0     0     0     0     0 #> [562,]     0     0     0     0     0     0     0     0     0     0     0 #> [563,]     0     0     0     0     0     0     0     0     0     0     0 #> [564,]     0     0     0     0     0     0     0     0     0     0     0 #> [565,]     0     0     0     0     0     0     0     0     0     0     0 #> [566,]     0     0     0     0     0     0     0     0     0     0     0 #> [567,]     0     0     0     0     0     0     0     0     0     0     0 #> [568,]     0     0     0     0     0     0     0     0     0     0     0 #> [569,]     0     0     0     0     0     0     0     0     0     0     0 #> [570,]     0     0     0     0     0     0     0     0     0     0     0 #> [571,]     0     0     0     0     0     0     0     0     0     0     0 #> [572,]     0     0     0     0     0     0     0     0     0     0     0 #> [573,]     0     0     0     0     0     0     0     0     0     0     0 #> [574,]     0     0     0     0     0     0     0     0     0     0     0 #> [575,]     0     0     0     0     0     0     0     0     0     0     0 #> [576,]     0     0     0     0     0     0     0     0     0     0     0 #> [577,]     0     0     0     0     0     0     0     0     0     0     0 #> [578,]     0     0     0     0     0     0     0     0     0     0     0 #> [579,]     0     0     0     0     0     0     0     0     0     0     0 #> [580,]     0     0     0     0     0     0     0     0     0     0     0 #> [581,]     0     0     0     0     0     0     0     0     0     0     0 #> [582,]     0     0     0     0     0     0     0     0     0     0     0 #> [583,]     0     0     0     0     0     0     0     0     0     0     0 #> [584,]     0     0     0     0     0     0     0     0     0     0     0 #> [585,]     0     0     0     0     0     0     0     0     0     0     0 #> [586,]     0     0     0     0     0     0     0     0     0     0     0 #> [587,]     0     0     0     0     0     0     0     0     0     0     0 #> [588,]     0     0     0     0     0     0     0     0     0     0     0 #> [589,]     0     0     0     0     0     0     0     0     0     0     0 #> [590,]     0     0     0     0     0     0     0     0     0     0     0 #> [591,]     0     0     0     0     0     0     0     0     0     0     0 #> [592,]     0     0     0     0     0     0     0     0     0     0     0 #> [593,]     0     0     0     0     0     0     0     0     0     0     0 #> [594,]     0     0     0     0     0     0     0     0     0     0     0 #> [595,]     0     0     0     0     0     0     0     0     0     0     0 #> [596,]     0     0     0     0     0     0     0     0     0     0     0 #> [597,]     0     0     0     0     0     0     0     0     0     0     0 #> [598,]     0     0     0     0     0     0     0     0     0     0     0 #> [599,]     0     0     0     0     0     0     0     0     0     0     0 #> [600,]     0     0     0     0     0     0     0     0     0     0     0 #> [601,]     0     0     0     0     0     0     0     0     0     0     0 #> [602,]     0     0     0     0     0     0     0     0     0     0     0 #> [603,]     0     0     0     0     0     0     0     0     0     0     0 #> [604,]     0     0     0     0     0     0     0     0     0     0     0 #> [605,]     0     0     0     0     0     0     0     0     0     0     0 #> [606,]     0     0     0     0     0     0     0     0     0     0     0 #> [607,]     0     0     0     0     0     0     0     0     0     0     0 #> [608,]     0     0     0     0     0     0     0     0     0     0     0 #> [609,]     0     0     0     0     0     0     0     0     0     0     0 #> [610,]     0     0     0     0     0     0     0     0     0     0     0 #> [611,]     0     0     0     0     0     0     0     0     0     0     0 #> [612,]     0     0     0     0     0     0     0     0     0     0     0 #> [613,]     0     0     0     0     0     0     0     0     0     0     0 #> [614,]     0     0     0     0     0     0     0     0     0     0     0 #> [615,]     0     0     0     0     0     0     0     0     0     0     0 #> [616,]     0     0     0     0     0     0     0     0     0     0     0 #> [617,]     0     0     0     0     0     0     0     0     0     0     0 #> [618,]     0     0     0     0     0     0     0     0     0     0     0 #> [619,]     0     0     0     0     0     0     0     0     0     0     0 #> [620,]     0     0     0     0     0     0     0     0     0     0     0 #> [621,]     0     0     0     0     0     0     0     0     0     0     0 #> [622,]     0     0     0     0     0     0     0     0     0     0     0 #> [623,]     0     0     0     0     0     0     0     0     0     0     0 #> [624,]     0     0     0     0     0     0     0     0     0     0     0 #> [625,]     0     0     0     0     0     0     0     0     0     0     0 #> [626,]     0     0     0     0     0     0     0     0     0     0     0 #> [627,]     0     0     0     0     0     0     0     0     0     0     0 #> [628,]     0     0     0     0     0     0     0     0     0     0     0 #> [629,]     0     0     0     0     0     0     0     0     0     0     0 #> [630,]     0     0     0     0     0     0     0     0     0     0     0 #> [631,]     0     0     0     0     0     0     0     0     0     0     0 #> [632,]     0     0     0     0     0     0     0     0     0     0     0 #> [633,]     0     0     0     0     0     0     0     0     0     0     0 #> [634,]     0     0     0     0     0     0     0     0     0     0     0 #> [635,]     0     0     0     0     0     0     0     0     0     0     0 #> [636,]     0     0     0     0     0     0     0     0     0     0     0 #> [637,]     0     0     0     0     0     0     0     0     0     0     0 #> [638,]     0     0     0     0     0     0     0     0     0     0     0 #> [639,]     0     0     0     0     0     0     0     0     0     0     0 #> [640,]     0     0     0     0     0     0     0     0     0     0     0 #> [641,]     0     0     0     0     0     0     0     0     0     0     0 #> [642,]     0     0     0     0     0     0     0     0     0     0     0 #> [643,]     0     0     0     0     0     0     0     0     0     0     0 #> [644,]     0     0     0     0     0     0     0     0     0     0     0 #> [645,]     0     0     0     0     0     0     0     0     0     0     0 #> [646,]     0     0     0     0     0     0     0     0     0     0     0 #> [647,]     0     0     0     0     0     0     0     0     0     0     0 #> [648,]     0     0     0     0     0     0     0     0     0     0     0 #> [649,]     0     0     0     0     0     0     0     0     0     0     0 #> [650,]     0     0     0     0     0     0     0     0     0     0     0 #> [651,]     0     0     0     0     0     0     0     0     0     0     0 #> [652,]     0     0     0     0     0     0     0     0     0     0     0 #> [653,]     0     0     0     0     0     0     0     0     0     0     0 #> [654,]     0     0     0     0     0     0     0     0     0     0     0 #>            [,44] [,45] #>   [1,] 0.8232503     0 #>   [2,] 0.8231873     0 #>   [3,] 0.8231242     0 #>   [4,] 0.8230612     0 #>   [5,] 0.8229982     0 #>   [6,] 0.8229352     0 #>   [7,] 0.8228722     0 #>   [8,] 0.8228092     0 #>   [9,] 0.8227462     0 #>  [10,] 0.8226832     0 #>  [11,] 0.8226202     0 #>  [12,] 0.8225572     0 #>  [13,] 0.8224942     0 #>  [14,] 0.8224313     0 #>  [15,] 0.8223683     0 #>  [16,] 0.8223053     0 #>  [17,] 0.8222424     0 #>  [18,] 0.8221794     0 #>  [19,] 0.8221165     0 #>  [20,] 0.8220535     0 #>  [21,] 0.8219906     0 #>  [22,] 0.8219276     0 #>  [23,] 0.8218647     0 #>  [24,] 0.8218018     0 #>  [25,] 0.8217389     0 #>  [26,] 0.8216759     0 #>  [27,] 0.8216130     0 #>  [28,] 0.8215501     0 #>  [29,] 0.8214872     0 #>  [30,] 0.8214243     0 #>  [31,] 0.8213614     0 #>  [32,] 0.8212986     0 #>  [33,] 0.8212357     0 #>  [34,] 0.8211728     0 #>  [35,] 0.8211099     0 #>  [36,] 0.8210470     0 #>  [37,] 0.8209842     0 #>  [38,] 0.8209213     0 #>  [39,] 0.8208585     0 #>  [40,] 0.8207956     0 #>  [41,] 0.8207328     0 #>  [42,] 0.8206699     0 #>  [43,] 0.8206071     0 #>  [44,] 0.8205443     0 #>  [45,] 0.8204815     0 #>  [46,] 0.8204186     0 #>  [47,] 0.8203558     0 #>  [48,] 0.8202930     0 #>  [49,] 0.8202302     0 #>  [50,] 0.8201674     0 #>  [51,] 0.8201046     0 #>  [52,] 0.8200418     0 #>  [53,] 0.8199790     0 #>  [54,] 0.8199162     0 #>  [55,] 0.8198535     0 #>  [56,] 0.8197907     0 #>  [57,] 0.8197279     0 #>  [58,] 0.8196652     0 #>  [59,] 0.8196024     0 #>  [60,] 0.8195397     0 #>  [61,] 0.8194769     0 #>  [62,] 0.8194142     0 #>  [63,] 0.8193514     0 #>  [64,] 0.8192887     0 #>  [65,] 0.8192260     0 #>  [66,] 0.8191632     0 #>  [67,] 0.8191005     0 #>  [68,] 0.8190378     0 #>  [69,] 0.8189751     0 #>  [70,] 0.8189124     0 #>  [71,] 0.8188497     0 #>  [72,] 0.8187870     0 #>  [73,] 0.8187243     0 #>  [74,] 0.8186616     0 #>  [75,] 0.8185989     0 #>  [76,] 0.8185363     0 #>  [77,] 0.8184736     0 #>  [78,] 0.8184109     0 #>  [79,] 0.8183483     0 #>  [80,] 0.8182856     0 #>  [81,] 0.8182230     0 #>  [82,] 0.8181603     0 #>  [83,] 0.8180977     0 #>  [84,] 0.8180350     0 #>  [85,] 0.8179724     0 #>  [86,] 0.8179098     0 #>  [87,] 0.8178471     0 #>  [88,] 0.8177845     0 #>  [89,] 0.8177219     0 #>  [90,] 0.8176593     0 #>  [91,] 0.8175967     0 #>  [92,] 0.8175341     0 #>  [93,] 0.8174715     0 #>  [94,] 0.8174089     0 #>  [95,] 0.8173463     0 #>  [96,] 0.8172838     0 #>  [97,] 0.8172212     0 #>  [98,] 0.8171586     0 #>  [99,] 0.8170960     0 #> [100,] 0.8170335     0 #> [101,] 0.8169709     0 #> [102,] 0.8169084     0 #> [103,] 0.8168458     0 #> [104,] 0.8167833     0 #> [105,] 0.8167208     0 #> [106,] 0.8166582     0 #> [107,] 0.8165957     0 #> [108,] 0.8165332     0 #> [109,] 0.8164707     0 #> [110,] 0.8164081     0 #> [111,] 0.8163456     0 #> [112,] 0.8162831     0 #> [113,] 0.8162206     0 #> [114,] 0.8161581     0 #> [115,] 0.8160956     0 #> [116,] 0.8160332     0 #> [117,] 0.8159707     0 #> [118,] 0.8159082     0 #> [119,] 0.8158457     0 #> [120,] 0.8157833     0 #> [121,] 0.8157208     0 #> [122,] 0.8156584     0 #> [123,] 0.8155959     0 #> [124,] 0.8155335     0 #> [125,] 0.8154710     0 #> [126,] 0.8154086     0 #> [127,] 0.8153461     0 #> [128,] 0.8152837     0 #> [129,] 0.8152213     0 #> [130,] 0.8151589     0 #> [131,] 0.8150965     0 #> [132,] 0.8150341     0 #> [133,] 0.8149717     0 #> [134,] 0.8149093     0 #> [135,] 0.8148469     0 #> [136,] 0.8147845     0 #> [137,] 0.8147221     0 #> [138,] 0.8146597     0 #> [139,] 0.8145973     0 #> [140,] 0.8145350     0 #> [141,] 0.8144726     0 #> [142,] 0.8144102     0 #> [143,] 0.8143479     0 #> [144,] 0.8142855     0 #> [145,] 0.8142232     0 #> [146,] 0.8141609     0 #> [147,] 0.8140985     0 #> [148,] 0.8140362     0 #> [149,] 0.8139739     0 #> [150,] 0.8139115     0 #> [151,] 0.8138492     0 #> [152,] 0.8137869     0 #> [153,] 0.8137246     0 #> [154,] 0.8136623     0 #> [155,] 0.8136000     0 #> [156,] 0.8135377     0 #> [157,] 0.8134754     0 #> [158,] 0.8134131     0 #> [159,] 0.8133509     0 #> [160,] 0.8132886     0 #> [161,] 0.8132263     0 #> [162,] 0.8131641     0 #> [163,] 0.8131018     0 #> [164,] 0.8130395     0 #> [165,] 0.8129773     0 #> [166,] 0.8129150     0 #> [167,] 0.8128528     0 #> [168,] 0.8127906     0 #> [169,] 0.8127283     0 #> [170,] 0.8126661     0 #> [171,] 0.8126039     0 #> [172,] 0.8125417     0 #> [173,] 0.8124795     0 #> [174,] 0.8124172     0 #> [175,] 0.8123550     0 #> [176,] 0.8122928     0 #> [177,] 0.8122307     0 #> [178,] 0.8121685     0 #> [179,] 0.8121063     0 #> [180,] 0.8120441     0 #> [181,] 0.8119819     0 #> [182,] 0.8119198     0 #> [183,] 0.8118576     0 #> [184,] 0.8117954     0 #> [185,] 0.8117333     0 #> [186,] 0.8116711     0 #> [187,] 0.8116090     0 #> [188,] 0.8115468     0 #> [189,] 0.8114847     0 #> [190,] 0.8114226     0 #> [191,] 0.8113605     0 #> [192,] 0.8112983     0 #> [193,] 0.8112362     0 #> [194,] 0.8111741     0 #> [195,] 0.8111120     0 #> [196,] 0.8110499     0 #> [197,] 0.8109878     0 #> [198,] 0.8109257     0 #> [199,] 0.8108636     0 #> [200,] 0.8108015     0 #> [201,] 0.8107394     0 #> [202,] 0.8106774     0 #> [203,] 0.8106153     0 #> [204,] 0.8105532     0 #> [205,] 0.8104912     0 #> [206,] 0.8104291     0 #> [207,] 0.8103671     0 #> [208,] 0.8103050     0 #> [209,] 0.8102430     0 #> [210,] 0.8101810     0 #> [211,] 0.8101189     0 #> [212,] 0.8100569     0 #> [213,] 0.8099949     0 #> [214,] 0.8099329     0 #> [215,] 0.8098708     0 #> [216,] 0.8098088     0 #> [217,] 0.8097468     0 #> [218,] 0.8096848     0 #> [219,] 0.8096228     0 #> [220,] 0.8095609     0 #> [221,] 0.8094989     0 #> [222,] 0.8094369     0 #> [223,] 0.8093749     0 #> [224,] 0.8093129     0 #> [225,] 0.8092510     0 #> [226,] 0.8091890     0 #> [227,] 0.8091271     0 #> [228,] 0.8090651     0 #> [229,] 0.8090032     0 #> [230,] 0.8089412     0 #> [231,] 0.8088793     0 #> [232,] 0.8088174     0 #> [233,] 0.8087554     0 #> [234,] 0.8086935     0 #> [235,] 0.8086316     0 #> [236,] 0.8085697     0 #> [237,] 0.8085078     0 #> [238,] 0.8084459     0 #> [239,] 0.8083840     0 #> [240,] 0.8083221     0 #> [241,] 0.8082602     0 #> [242,] 0.8081983     0 #> [243,] 0.8081364     0 #> [244,] 0.8080745     0 #> [245,] 0.8080127     0 #> [246,] 0.8079508     0 #> [247,] 0.8078890     0 #> [248,] 0.8078271     0 #> [249,] 0.8077652     0 #> [250,] 0.8077034     0 #> [251,] 0.8076416     0 #> [252,] 0.8075797     0 #> [253,] 0.8075179     0 #> [254,] 0.8074561     0 #> [255,] 0.8073942     0 #> [256,] 0.8073324     0 #> [257,] 0.8072706     0 #> [258,] 0.8072088     0 #> [259,] 0.8071470     0 #> [260,] 0.8070852     0 #> [261,] 0.8070234     0 #> [262,] 0.8069616     0 #> [263,] 0.8068998     0 #> [264,] 0.8068380     0 #> [265,] 0.8067763     0 #> [266,] 0.8067145     0 #> [267,] 0.8066527     0 #> [268,] 0.8065910     0 #> [269,] 0.8065292     0 #> [270,] 0.8064675     0 #> [271,] 0.8064057     0 #> [272,] 0.8063440     0 #> [273,] 0.8062822     0 #> [274,] 0.8062205     0 #> [275,] 0.8061588     0 #> [276,] 0.8060971     0 #> [277,] 0.8060353     0 #> [278,] 0.8059736     0 #> [279,] 0.8059119     0 #> [280,] 0.8058502     0 #> [281,] 0.8057885     0 #> [282,] 0.8057268     0 #> [283,] 0.8056651     0 #> [284,] 0.8056034     0 #> [285,] 0.8055418     0 #> [286,] 0.8054801     0 #> [287,] 0.8054184     0 #> [288,] 0.8053567     0 #> [289,] 0.8052951     0 #> [290,] 0.8052334     0 #> [291,] 0.8051718     0 #> [292,] 0.8051101     0 #> [293,] 0.8050485     0 #> [294,] 0.8049868     0 #> [295,] 0.8049252     0 #> [296,] 0.8048636     0 #> [297,] 0.8048019     0 #> [298,] 0.8047403     0 #> [299,] 0.8046787     0 #> [300,] 0.8046171     0 #> [301,] 0.8045555     0 #> [302,] 0.8044939     0 #> [303,] 0.8044323     0 #> [304,] 0.8043707     0 #> [305,] 0.8043091     0 #> [306,] 0.8042475     0 #> [307,] 0.8041860     0 #> [308,] 0.8041244     0 #> [309,] 0.8040013     0 #> [310,] 0.8039397     0 #> [311,] 0.8038782     0 #> [312,] 0.8038166     0 #> [313,] 0.8037551     0 #> [314,] 0.8036935     0 #> [315,] 0.8036320     0 #> [316,] 0.8035705     0 #> [317,] 0.8035089     0 #> [318,] 0.8040628     0 #> [319,] 0.8560129     0 #> [320,] 0.8559473     0 #> [321,] 0.8558818     0 #> [322,] 0.8560784     0 #> [323,] 0.8571278     0 #> [324,] 0.8570622     0 #> [325,] 0.8569966     0 #> [326,] 0.8569310     0 #> [327,] 0.8568654     0 #> [328,] 0.8567997     0 #> [329,] 0.8567341     0 #> [330,] 0.8562095     0 #> [331,] 0.8561440     0 #> [332,] 0.8574560     0 #> [333,] 0.8573904     0 #> [334,] 0.8573247     0 #> [335,] 0.8571935     0 #> [336,] 0.8566686     0 #> [337,] 0.8566030     0 #> [338,] 0.8565374     0 #> [339,] 0.8564718     0 #> [340,] 0.8564062     0 #> [341,] 0.8563406     0 #> [342,] 0.8562751     0 #> [343,] 0.8572591     0 #> [344,] 0.8595236     0 #> [345,] 0.8593920     0 #> [346,] 0.8593262     0 #> [347,] 0.8592604     0 #> [348,] 0.8594578     0 #> [349,] 0.8591288     0 #> [350,] 0.8590630     0 #> [351,] 0.8589972     0 #> [352,] 0.8589315     0 #> [353,] 0.8588657     0 #> [354,] 0.8588000     0 #> [355,] 0.8587342     0 #> [356,] 0.8586684     0 #> [357,] 0.8586027     0 #> [358,] 0.8585370     0 #> [359,] 0.8584712     0 #> [360,] 0.8584055     0 #> [361,] 0.8591946     0 #> [362,] 0.8600502     0 #> [363,] 0.8599844     0 #> [364,] 0.8599185     0 #> [365,] 0.8598527     0 #> [366,] 0.8597869     0 #> [367,] 0.8597210     0 #> [368,] 0.8596552     0 #> [369,] 0.8595894     0 #> [370,] 0.8578142     0 #> [371,] 0.8577485     0 #> [372,] 0.8576828     0 #> [373,] 0.8576172     0 #> [374,] 0.8575515     0 #> [375,] 0.8583398     0 #> [376,] 0.8582741     0 #> [377,] 0.8582083     0 #> [378,] 0.8581426     0 #> [379,] 0.8580769     0 #> [380,] 0.8580112     0 #> [381,] 0.8579455     0 #> [382,] 0.8578799     0 #> [383,] 0.8986082     0 #> [384,] 0.8985394     0 #> [385,] 0.8984706     0 #> [386,] 0.8984018     0 #> [387,] 0.8983330     0 #> [388,] 0.8937364     0 #> [389,] 0.8936679     0 #> [390,] 0.8935995     0 #> [391,] 0.8935311     0 #> [392,] 0.8934627     0 #> [393,] 0.8933943     0 #> [394,] 0.8933259     0 #> [395,] 0.8932575     0 #> [396,] 0.8931891     0 #> [397,] 0.8931207     0 #> [398,] 0.8930523     0 #> [399,] 0.8929839     0 #> [400,] 0.8929156     0 #> [401,] 0.8992277     0 #> [402,] 0.8991588     0 #> [403,] 0.8990900     0 #> [404,] 0.8990211     0 #> [405,] 0.8989523     0 #> [406,] 0.8988835     0 #> [407,] 0.8988147     0 #> [408,] 0.8987458     0 #> [409,] 0.8986770     0 #> [410,] 0.8913445     0 #> [411,] 0.8912762     0 #> [412,] 0.8912080     0 #> [413,] 0.8911397     0 #> [414,] 0.8910715     0 #> [415,] 0.8910033     0 #> [416,] 0.8909351     0 #> [417,] 0.8908669     0 #> [418,] 0.8907987     0 #> [419,] 0.8907304     0 #> [420,] 0.8906622     0 #> [421,] 0.8905941     0 #> [422,] 0.8905259     0 #> [423,] 0.8904577     0 #> [424,] 0.8903895     0 #> [425,] 0.8903213     0 #> [426,] 0.8902532     0 #> [427,] 0.8901850     0 #> [428,] 0.8901168     0 #> [429,] 0.8900487     0 #> [430,] 0.8899805     0 #> [431,] 0.8899124     0 #> [432,] 0.8898443     0 #> [433,] 0.8897761     0 #> [434,] 0.8897080     0 #> [435,] 0.8896399     0 #> [436,] 0.8895718     0 #> [437,] 0.8895037     0 #> [438,] 0.8894356     0 #> [439,] 0.8893675     0 #> [440,] 0.8892994     0 #> [441,] 0.8892313     0 #> [442,] 0.8891632     0 #> [443,] 0.8890951     0 #> [444,] 0.8890270     0 #> [445,] 0.8889590     0 #> [446,] 0.8888909     0 #> [447,] 0.8888229     0 #> [448,] 0.8887548     0 #> [449,] 0.8886868     0 #> [450,] 0.8886187     0 #> [451,] 0.8885507     0 #> [452,] 0.8884826     0 #> [453,] 0.8884146     0 #> [454,] 0.8883466     0 #> [455,] 0.8882786     0 #> [456,] 0.8882106     0 #> [457,] 0.8881426     0 #> [458,] 0.8880746     0 #> [459,] 0.8880066     0 #> [460,] 0.8948319     0 #> [461,] 0.8947634     0 #> [462,] 0.8946949     0 #> [463,] 0.8946264     0 #> [464,] 0.8945579     0 #> [465,] 0.8944894     0 #> [466,] 0.8944209     0 #> [467,] 0.8943525     0 #> [468,] 0.8879386     0 #> [469,] 0.8878706     0 #> [470,] 0.8878026     0 #> [471,] 0.8940786     0 #> [472,] 0.8940101     0 #> [473,] 0.8939417     0 #> [474,] 0.8938732     0 #> [475,] 0.8938048     0 #> [476,] 0.8877346     0 #> [477,] 0.8876667     0 #> [478,] 0.8875987     0 #> [479,] 0.8875307     0 #> [480,] 0.8874628     0 #> [481,] 0.8873948     0 #> [482,] 0.8873269     0 #> [483,] 0.8872590     0 #> [484,] 0.8871910     0 #> [485,] 0.8871231     0 #> [486,] 0.8870552     0 #> [487,] 0.8869873     0 #> [488,] 0.8869194     0 #> [489,] 0.8868514     0 #> [490,] 0.8867835     0 #> [491,] 0.8867156     0 #> [492,] 0.8866478     0 #> [493,] 0.8865799     0 #> [494,] 0.8865120     0 #> [495,] 0.8979892     0 #> [496,] 0.8979204     0 #> [497,] 0.8978517     0 #> [498,] 0.8977830     0 #> [499,] 0.8977142     0 #> [500,] 0.8976455     0 #> [501,] 0.8975768     0 #> [502,] 0.8975080     0 #> [503,] 0.8974393     0 #> [504,] 0.8973706     0 #> [505,] 0.8973019     0 #> [506,] 0.8972332     0 #> [507,] 0.8971645     0 #> [508,] 0.8970958     0 #> [509,] 0.8928472     0 #> [510,] 0.8927788     0 #> [511,] 0.8927105     0 #> [512,] 0.8926421     0 #> [513,] 0.8925738     0 #> [514,] 0.8925054     0 #> [515,] 0.8924371     0 #> [516,] 0.8923688     0 #> [517,] 0.8923005     0 #> [518,] 0.8922321     0 #> [519,] 0.8921638     0 #> [520,] 0.8920955     0 #> [521,] 0.8920272     0 #> [522,] 0.8919589     0 #> [523,] 0.8918906     0 #> [524,] 0.8918223     0 #> [525,] 0.8917541     0 #> [526,] 0.8916858     0 #> [527,] 0.8916175     0 #> [528,] 0.8915492     0 #> [529,] 0.8914810     0 #> [530,] 0.8914127     0 #> [531,] 0.8982643     0 #> [532,] 0.8981955     0 #> [533,] 0.8981267     0 #> [534,] 0.8980580     0 #> [535,] 0.8952431     0 #> [536,] 0.8951746     0 #> [537,] 0.8951060     0 #> [538,] 0.8950375     0 #> [539,] 0.8949690     0 #> [540,] 0.8949005     0 #> [541,] 0.8966838     0 #> [542,] 0.8966151     0 #> [543,] 0.8965465     0 #> [544,] 0.8964778     0 #> [545,] 0.8964092     0 #> [546,] 0.8963405     0 #> [547,] 0.8962719     0 #> [548,] 0.8962033     0 #> [549,] 0.8970271     0 #> [550,] 0.8969584     0 #> [551,] 0.8968898     0 #> [552,] 0.8968211     0 #> [553,] 0.8967524     0 #> [554,] 0.8957917     0 #> [555,] 0.8957231     0 #> [556,] 0.8956545     0 #> [557,] 0.8955859     0 #> [558,] 0.8955174     0 #> [559,] 0.8954488     0 #> [560,] 0.8953802     0 #> [561,] 0.8953117     0 #> [562,] 0.8961347     0 #> [563,] 0.8960661     0 #> [564,] 0.8959975     0 #> [565,] 0.8959289     0 #> [566,] 0.8958603     0 #> [567,] 0.8942840     0 #> [568,] 0.8942155     0 #> [569,] 0.8941470     0 #> [570,] 1.0059666     0 #> [571,] 1.0056585     0 #> [572,] 1.0058896     0 #> [573,] 1.0058125     0 #> [574,] 1.0057355     0 #> [575,] 1.0064288     0 #> [576,] 1.0055815     0 #> [577,] 1.0055045     0 #> [578,] 1.0054276     0 #> [579,] 1.0061206     0 #> [580,] 1.0060436     0 #> [581,] 1.0034280     0 #> [582,] 1.0033512     0 #> [583,] 1.0048888     0 #> [584,] 1.0048119     0 #> [585,] 1.0047349     0 #> [586,] 1.0046580     0 #> [587,] 1.0045811     0 #> [588,] 1.0045042     0 #> [589,] 1.0063518     0 #> [590,] 1.0062747     0 #> [591,] 1.0061977     0 #> [592,] 1.0051966     0 #> [593,] 1.0051197     0 #> [594,] 1.0050427     0 #> [595,] 1.0049658     0 #> [596,] 1.0038891     0 #> [597,] 1.0038122     0 #> [598,] 1.0037353     0 #> [599,] 1.0036585     0 #> [600,] 1.0035817     0 #> [601,] 1.0035048     0 #> [602,] 1.0044273     0 #> [603,] 1.0043504     0 #> [604,] 1.0042735     0 #> [605,] 1.0041966     0 #> [606,] 1.0041197     0 #> [607,] 1.0040428     0 #> [608,] 1.0039659     0 #> [609,] 1.0053506     0 #> [610,] 1.0052736     0 #> [611,] 1.0563301     0 #> [612,] 1.0562493     0 #> [613,] 1.0564919     0 #> [614,] 1.0564110     0 #> [615,] 1.0541486     0 #> [616,] 1.0540679     0 #> [617,] 1.0561684     0 #> [618,] 1.0560875     0 #> [619,] 1.0538258     0 #> [620,] 1.0537451     0 #> [621,] 1.0536644     0 #> [622,] 1.0535838     0 #> [623,] 1.0535031     0 #> [624,] 1.0534224     0 #> [625,] 1.0533418     0 #> [626,] 1.0532611     0 #> [627,] 1.0531805     0 #> [628,] 1.0530998     0 #> [629,] 1.0530192     0 #> [630,] 1.0539872     0 #> [631,] 1.0539065     0 #> [632,] 1.0560067     0 #> [633,] 1.0559258     0 #> [634,] 1.0558450     0 #> [635,] 1.0557641     0 #> [636,] 1.0556833     0 #> [637,] 1.0556025     0 #> [638,] 1.0555216     0 #> [639,] 1.0554408     0 #> [640,] 1.0553600     0 #> [641,] 1.0552792     0 #> [642,] 1.0551984     0 #> [643,] 1.0551176     0 #> [644,] 1.0550368     0 #> [645,] 1.0549561     0 #> [646,] 1.0548753     0 #> [647,] 1.0547945     0 #> [648,] 1.0547138     0 #> [649,] 1.0546330     0 #> [650,] 1.0545523     0 #> [651,] 1.0544715     0 #> [652,] 1.0543908     0 #> [653,] 1.0543100     0 #> [654,] 1.0542293     0"},{"path":"/reference/create_netgeom.html","id":null,"dir":"Reference","previous_headings":"","what":"Create netgeom column in SSN object — create_netgeom","title":"Create netgeom column in SSN object — create_netgeom","text":"Create netgeom column edges, observed sites, /prediction sites Landscape Network (LSN).","code":""},{"path":"/reference/create_netgeom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create netgeom column in SSN object — create_netgeom","text":"","code":"create_netgeom(sf_data, type = NULL, overwrite = FALSE)"},{"path":"/reference/create_netgeom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create netgeom column in SSN object — create_netgeom","text":"sf_data sf object LINESTING POINT geometry created using link{lsn_to_ssn} (see Details). type Character string defining geometry type sf_data. Default = NULL. overwrite Logical indicating whether existing data overwritten present. Default = FALSE.","code":""},{"path":"/reference/create_netgeom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create netgeom column in SSN object — create_netgeom","text":"sf object containing original data sf_data additional column named netgeom.","code":""},{"path":"/reference/create_netgeom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create netgeom column in SSN object — create_netgeom","text":"users need run create_netgeom called internally lsn_to_ssn run SSN imported using link[SSN2]{ssn_import} found SSN2 package. users wish run create_netgeom, sf_data object must represent edges, observed sites, prediction sites SSN object created using link{lsn_to_ssn}. netgeom column contains information character format used describe topology LSN. format content netgeom column differs depending whether sf_data contains LINESTRING (edges) POINT (observed prediction sites) geometry. edges, netgeom format : 'ENETWORK (netID, rid, upDist)' observed prediction sites, netgeom format : 'SNETWORK (netID, rid, upDist, ratio, pid, locID)' rid, ratio, upDist, netID, pid, locID columns must present sf_data netgeom added. overwrite = TRUE column named netgeom present sf_data, data overwritten. Default = FALSE.","code":""},{"path":"/reference/create_netgeom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create netgeom column in SSN object — create_netgeom","text":"","code":"## Create local temporary copy of MiddleFork04.ssn found in ## the SSN2 package. Only necessary for this example. copy_lsn_to_temp()  # Import the SSN object with prediction points, pred1km mf04<- ssn_import(    paste0(tempdir(), \"/MiddleFork04.ssn\"),    predpts = c(\"pred1km\"),    overwrite = TRUE )  # Recalculate the netgeom column for the observed sites sf_obs <- create_netgeom(     mf04$obs,     type = \"POINT\",     overwrite = TRUE )  # Recalculate the netgeom column for the edges sf_edges <- create_netgeom(     mf04$edges,     type = \"LINESTRING\",     overwrite = TRUE )"},{"path":"/reference/deviance.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitted model deviance — deviance.SSN2","title":"Fitted model deviance — deviance.SSN2","text":"Returns deviance fitted model object.","code":""},{"path":"/reference/deviance.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitted model deviance — deviance.SSN2","text":"","code":"# S3 method for class 'ssn_lm' deviance(object, ...)  # S3 method for class 'ssn_glm' deviance(object, ...)"},{"path":"/reference/deviance.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitted model deviance — deviance.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/deviance.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitted model deviance — deviance.SSN2","text":"deviance.","code":""},{"path":"/reference/deviance.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitted model deviance — deviance.SSN2","text":"deviance twice difference log-likelihoods saturated (perfect-fit) model fitted model.","code":""},{"path":"/reference/deviance.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitted model deviance — deviance.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_glm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   family = \"Gamma\",   tailup_type = \"exponential\",   additive = \"afvArea\" ) deviance(ssn_mod) #> [1] 9.832514e-05"},{"path":"/reference/fitted.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract model fitted values — fitted.SSN2","title":"Extract model fitted values — fitted.SSN2","text":"Extract fitted values fitted model objects. fitted.values alias.","code":""},{"path":"/reference/fitted.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract model fitted values — fitted.SSN2","text":"","code":"# S3 method for class 'ssn_lm' fitted(object, type = \"response\", ...)  # S3 method for class 'ssn_lm' fitted.values(object, type = \"response\", ...)  # S3 method for class 'ssn_glm' fitted(object, type = \"response\", ...)  # S3 method for class 'ssn_glm' fitted.values(object, type = \"response\", ...)"},{"path":"/reference/fitted.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract model fitted values — fitted.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). type \"response\" fitted values response, \"tailup\" fitted values tailup random errors, \"taildown\" fitted values taildown random errors, \"euclid\" fitted values Euclidean random errors, \"nugget\" fitted values nugget random errors, \"randcov\" fitted values random effects. ssn_glm(), \"link\" fitted values link scale. default \"response\". ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/fitted.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract model fitted values — fitted.SSN2","text":"fitted values according type.","code":""},{"path":"/reference/fitted.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract model fitted values — fitted.SSN2","text":"type \"response\", fitted values observation standard fitted values \\(X \\hat{\\beta}\\). type \"tailup\", \"taildown\", \"euclid\", \"nugget\" fitted values observation (generally) best linear unbiased predictors respective random error. type \"randcov\", fitted values level random effect (generally) best linear unbiased predictors corresponding random effect. fitted values type \"tailup\", \"taildown\", \"euclid\", \"nugget\", \"randcov\" can generally used check assumptions component fitted model object (e.g., check Gaussian assumption). ssn_glm(), type \"response\", fitted values observation standard fitted values inverse link scale: \\(g^{-1}\\)(\\(X \\hat{\\beta} + \\nu\\)), \\(g(.)\\) link function, \\(\\beta\\) fixed effects, \\(\\nu\\) spatial random effects.","code":""},{"path":"/reference/fitted.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract model fitted values — fitted.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) fitted(ssn_mod) #>         1         2         3         4         5         6         7         8  #> 14.417439 14.246817 14.042070 15.236427 14.929306 14.656310 14.656310 14.485688  #>         9        10        11        12        13        14        15        16  #> 14.383315 14.315066 14.349190 14.315066 14.280941 13.393704 13.154833 12.847712  #>        17        18        19        20        21        22        23        24  #> 12.369969 12.301720 12.199347 11.823977 11.823977 11.721604 13.018335 12.165222  #>        25        26        27        28        29        30        31        32  #> 12.096973 12.096973 11.312110 10.834367 10.356624  9.708258 13.461953 13.223082  #>        33        34        35        36        37        38        39        40  #> 12.642965 12.540592 12.711214 12.404094 11.721604 11.380359 11.039114 10.902616  #>        41        42        43        44        45  #> 10.186001  9.981254 12.062849 11.448608 11.175612  fitted.values(ssn_mod) #>         1         2         3         4         5         6         7         8  #> 14.417439 14.246817 14.042070 15.236427 14.929306 14.656310 14.656310 14.485688  #>         9        10        11        12        13        14        15        16  #> 14.383315 14.315066 14.349190 14.315066 14.280941 13.393704 13.154833 12.847712  #>        17        18        19        20        21        22        23        24  #> 12.369969 12.301720 12.199347 11.823977 11.823977 11.721604 13.018335 12.165222  #>        25        26        27        28        29        30        31        32  #> 12.096973 12.096973 11.312110 10.834367 10.356624  9.708258 13.461953 13.223082  #>        33        34        35        36        37        38        39        40  #> 12.642965 12.540592 12.711214 12.404094 11.721604 11.380359 11.039114 10.902616  #>        41        42        43        44        45  #> 10.186001  9.981254 12.062849 11.448608 11.175612"},{"path":"/reference/formula.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Model formulae — formula.SSN2","title":"Model formulae — formula.SSN2","text":"Return formula used fitted model object.","code":""},{"path":"/reference/formula.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model formulae — formula.SSN2","text":"","code":"# S3 method for class 'ssn_lm' formula(x, ...)  # S3 method for class 'ssn_glm' formula(x, ...)"},{"path":"/reference/formula.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model formulae — formula.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/formula.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model formulae — formula.SSN2","text":"formula used fitted model object.","code":""},{"path":"/reference/formula.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model formulae — formula.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) formula(ssn_mod) #> Summer_mn ~ ELEV_DEM #> <environment: 0x000002369df92a90>"},{"path":"/reference/glance.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance at a fitted model object — glance.SSN2","title":"Glance at a fitted model object — glance.SSN2","text":"Returns row model summaries fitted model object. Glance returns number columns models estimation methods.","code":""},{"path":"/reference/glance.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance at a fitted model object — glance.SSN2","text":"","code":"# S3 method for class 'ssn_lm' glance(x, ...)  # S3 method for class 'ssn_glm' glance(x, ...)"},{"path":"/reference/glance.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance at a fitted model object — glance.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/glance.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance at a fitted model object — glance.SSN2","text":"single-row tibble columns n sample size. p number fixed effects. npar number estimated covariance parameters. value optimized value fitting function AIC AIC. AICc AICc. logLik log-likelihood deviance deviance. pseudo.r.squared pseudo r-squared","code":""},{"path":"/reference/glance.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance at a fitted model object — glance.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) glance(ssn_mod) #> # A tibble: 1 × 9 #>       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1    45     2     3  76.9  82.9  83.5  -38.4     43.0            0.585"},{"path":"/reference/glances.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance at many fitted model objects — glances.SSN2","title":"Glance at many fitted model objects — glances.SSN2","text":"glances() repeatedly calls glance() several fitted model objects binds output together, sorted column interest.","code":""},{"path":"/reference/glances.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance at many fitted model objects — glances.SSN2","text":"","code":"# S3 method for class 'ssn_lm' glances(object, ..., sort_by = \"AICc\", decreasing = FALSE)  # S3 method for class 'ssn_glm' glances(object, ..., sort_by = \"AICc\", decreasing = FALSE)"},{"path":"/reference/glances.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance at many fitted model objects — glances.SSN2","text":"object Fitted model object ssn_lm() ssn_glm(). ... Additional fitted model objects ssn_lm() ssn_glm(). sort_by Sort glance statistic (.e., name column output glance() order model input (sort_by = \"order\"). default \"AICc\". decreasing sort_by decreasing ? default FALSE.","code":""},{"path":"/reference/glances.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance at many fitted model objects — glances.SSN2","text":"tibble row represents output glance() fitted model object.","code":""},{"path":"/reference/glances.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance at many fitted model objects — glances.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  # tailup only ssn_mod1 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) # taildown only ssn_mod2 <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   taildown_type = \"exponential\" ) glances(ssn_mod1, ssn_mod2) #> # A tibble: 2 × 10 #>   model        n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared #>   <chr>    <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl> #> 1 ssn_mod1    45     2     3  76.9  82.9  83.5  -38.4     43.0           0.585  #> 2 ssn_mod2    45     2     3 123.  129.  129.   -61.4     43.4           0.0917"},{"path":"/reference/hatvalues.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute leverage (hat) values — hatvalues.SSN2","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"Compute leverage (hat) value observation fitted model object.","code":""},{"path":"/reference/hatvalues.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"","code":"# S3 method for class 'ssn_lm' hatvalues(model, ...)  # S3 method for class 'ssn_glm' hatvalues(model, ...)"},{"path":"/reference/hatvalues.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"model fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/hatvalues.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"vector leverage (hat) values observation fitted model object.","code":""},{"path":"/reference/hatvalues.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"Leverage values measure far observation's explanatory variables relative average explanatory variables. words, observations high leverage typically considered extreme unusual combination explanatory variables. Leverage values diagonal hat (projection) matrix. larger hat value, larger leverage.","code":""},{"path":[]},{"path":"/reference/hatvalues.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute leverage (hat) values — hatvalues.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) hatvalues(ssn_mod) #>           1           2           3           4           5           6  #> 0.111004780 0.055738061 0.033706428 0.074359366 0.020156027 0.005693127  #>           7           8           9          10          11          12  #> 0.001624725 0.057398164 0.073923742 0.058051176 0.025562432 0.027726546  #>          13          14          15          16          17          18  #> 0.025641370 0.060692330 0.083505795 0.029915738 0.015064681 0.017762234  #>          19          20          21          22          23          24  #> 0.007203224 0.014860920 0.014280350 0.012121476 0.073987730 0.030984461  #>          25          26          27          28          29          30  #> 0.051576193 0.054619566 0.013623967 0.043036889 0.120814555 0.112522931  #>          31          32          33          34          35          36  #> 0.058861422 0.019483321 0.005954595 0.004592189 0.009847750 0.005559533  #>          37          38          39          40          41          42  #> 0.002381727 0.033445107 0.035807368 0.046264890 0.057583822 0.065328710  #>          43          44          45  #> 0.138148339 0.030372326 0.159209917"},{"path":"/reference/influence.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Regression diagnostics — influence.SSN2","title":"Regression diagnostics — influence.SSN2","text":"Provides basic quantities used forming wide variety diagnostics checking quality fitted model objects.","code":""},{"path":"/reference/influence.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regression diagnostics — influence.SSN2","text":"","code":"# S3 method for class 'ssn_lm' influence(model, ...)  # S3 method for class 'ssn_glm' influence(model, ...)"},{"path":"/reference/influence.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Regression diagnostics — influence.SSN2","text":"model fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/influence.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Regression diagnostics — influence.SSN2","text":"tibble residuals (.resid), leverage values (.hat), cook's distance (.cooksd), standardized residuals (.std.resid).","code":""},{"path":"/reference/influence.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Regression diagnostics — influence.SSN2","text":"function calls residuals.SSN2(), hatvalues.SSN2(), cooks.distance.SSN2() puts results tibble. primarily used calling augment.SSN2().","code":""},{"path":[]},{"path":"/reference/influence.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regression diagnostics — influence.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) influence(ssn_mod) #> # A tibble: 45 × 4 #>     .resid    .hat   .cooksd .std.resid #>      <dbl>   <dbl>     <dbl>      <dbl> #>  1  0.503  0.111   0.0165         0.545 #>  2  0.473  0.0557  0.00173        0.249 #>  3  0.568  0.0337  0.00658        0.625 #>  4 -0.0164 0.0744  0.00893        0.490 #>  5 -0.439  0.0202  0.0158        -1.25  #>  6  0.634  0.00569 0.000970       0.584 #>  7  0.414  0.00162 0.0000507     -0.250 #>  8  0.454  0.0574  0.0143        -0.706 #>  9  0.607  0.0739  0.00666        0.425 #> 10  0.705  0.0581  0.0196         0.821 #> # ℹ 35 more rows"},{"path":"/reference/labels.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Find labels from object — labels.SSN2","title":"Find labels from object — labels.SSN2","text":"Find suitable set labels fitted model object.","code":""},{"path":"/reference/labels.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find labels from object — labels.SSN2","text":"","code":"# S3 method for class 'ssn_lm' labels(object, ...)  # S3 method for class 'ssn_glm' labels(object, ...)"},{"path":"/reference/labels.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find labels from object — labels.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/labels.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find labels from object — labels.SSN2","text":"character vector containing terms used fixed effects fitted model object.","code":""},{"path":"/reference/labels.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find labels from object — labels.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) labels(ssn_mod) #> [1] \"ELEV_DEM\""},{"path":"/reference/logLik.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract log-likelihood — logLik.SSN2","title":"Extract log-likelihood — logLik.SSN2","text":"Find log-likelihood fitted model.","code":""},{"path":"/reference/logLik.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract log-likelihood — logLik.SSN2","text":"","code":"# S3 method for class 'ssn_lm' logLik(object, ...)  # S3 method for class 'ssn_glm' logLik(object, ...)"},{"path":"/reference/logLik.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract log-likelihood — logLik.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/logLik.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract log-likelihood — logLik.SSN2","text":"log-likelihood.","code":""},{"path":"/reference/logLik.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract log-likelihood — logLik.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) logLik(ssn_mod) #> [1] -38.4466"},{"path":"/reference/loocv.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform leave-one-out cross validation — loocv.SSN2","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"Perform leave-one-cross validation options computationally efficient approximations big data.","code":""},{"path":"/reference/loocv.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"","code":"# S3 method for class 'ssn_lm' loocv(object, cv_predict = FALSE, se.fit = FALSE, ...)  # S3 method for class 'ssn_glm' loocv(object, cv_predict = FALSE, se.fit = FALSE, ...)"},{"path":"/reference/loocv.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). cv_predict logical indicating whether leave-one-fitted values returned. Defaults FALSE. se.fit logical indicating whether leave-one-prediction standard errors returned. Defaults FALSE. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/loocv.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"cv_predict = FALSE se.fit = FALSE, tibble indicating several leave-one-cross validation error metrics. cv_predict = TRUE se.fit = TRUE, list elements: stats, tibble indicating several leave-one-cross validation metrics; cv_predict, numeric vector leave-one-predictions observation (cv_predict = TRUE); se.fit, numeric vector leave-one-prediction standard errors observation (se.fit = TRUE). ssn_lm object, cross validation error metrics : bias: average difference predicted value true value std.bias: average standardized difference predicted value true value MSPE: average squared difference predicted value true value RMSPE: root average squared difference predicted value true value std.MSPE: average standardized squared difference predicted value true value RAV: root average estimated variance predicted value cor2: squared correlation predicted true values cover.80: Coverage rates 80% prediction intervals built true values cover.90: Coverage rates 90% prediction intervals built true values cover.95: Coverage rates 95% prediction intervals built true values ssn_glm object, cross validation error metrics : bias: average difference predicted value true value MSPE: average squared difference predicted value true value RMSPE: root average squared difference predicted value true value RAV: root average estimated variance predicted value (link scale)","code":""},{"path":"/reference/loocv.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"observation held-data set remaining data used make prediction held-observation. compared true value observation several model-fit statistics computed across observations.","code":""},{"path":"/reference/loocv.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform leave-one-out cross validation — loocv.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) loocv(ssn_mod) #> # A tibble: 1 × 10 #>     bias std.bias  MSPE RMSPE std.MSPE   RAV  cor2 cover.80 cover.90 cover.95 #>    <dbl>    <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>    <dbl>    <dbl>    <dbl> #> 1 0.0449   0.0456 0.273 0.522     1.23 0.418 0.930      0.8    0.889    0.911"},{"path":"/reference/mf04p.html","id":null,"dir":"Reference","previous_headings":"","what":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","title":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","text":"MiddleFork04.ssn data folder contains spatial, attribute, topological information needed construct spatial stream network object using SSN2 package. mf04p created using ssn_import().","code":""},{"path":"/reference/mf04p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","text":"","code":"mf04p"},{"path":"/reference/mf04p.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Imported SSN object from the MiddleFork04.ssn data folder — mf04p","text":"object class SSN length 4.","code":""},{"path":[]},{"path":"/reference/MiddleFork04.ssn.html","id":null,"dir":"Reference","previous_headings":"","what":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","title":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","text":"MiddleFork04.ssn data folder contains spatial, attribute, topological information needed construct SSN object using SSN2 package.","code":""},{"path":"/reference/MiddleFork04.ssn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","text":"edges modified version United States National Hydrography Dataset (http://nhd.usgs.gov/). sites, pred1km CapeHorn unpublished United States Forest Service data.","code":""},{"path":"/reference/MiddleFork04.ssn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn","text":"MiddleFork04.ssn folder contains five geopackages: edges: geopackage LINESTRING geometry representing stream network sites: geopackage POINT geometry representing observed site locations pred1km: geopackage POINT geometry prediction site locations approximately 1km intervals throughout stream network CapeHorn: geopackage POINT geometry representing prediction site locations Cape Horn River MiddleFork04.ssn includes two text files, netID1.txt netID2.txt, contain topological information two stream networks Middle Fork 2004 dataset. distance folder contains four folders store hydrologic distance matrices point datasets (obs, CapeHorn, pred1km). See ssn_create_distmat() detailed description distance matrix file structure. Attribute data also stored within spatial datasets. column names defined follows: edges: rid: Reach identifier COMID: Common identifier NHD feature relationship GNIS_Name: Feature name found Geographic Names Information System REACHCODE: Unique identifier reach. first 8 digits contain identfier HUC8 last 6 digits unique within-HUC8 identifier reach FTYPE: three-digit integer used classify hydrography features NHD define subtypes FCODE: Numeric code contains feature type attributes found NHDFCode lookup table AREAWTMAP: Area weighted mean annual precipitation (mm) lowermost location edge SLOPE: Slope edge (cm/cm) rcaAreaKm2: Reach contributing area (km2) edge feature. RCA represents land area drains directly edge feature h2oAreaKm2: Watershed area (km2) lowermost location edge feature areaPI: Segment proportional influence value, calculated using watershed area (h2oAreaKm2) afvArea: Additive function value, calculated using areaPI upDist: Distance stream outlet (downstream location stream network) uppermost location line segment Length: Length line segment (m) netID: Network identifier sites: rid: Reach identifier edge site resides pid: Point identifier STREAMNAME: Stream name COMID: Common identifier NHD feature relationship AREAWTMAP: Area weighted mean annual precipitation (mm) lowermost location line segment site resides SLOPE: Slope line segment (cm/cm) site resides ELEV_DEM: Elevation site based 30m DEM Source: Source data - relates ID field source table Summer_mn: Overall summer mean termperature (C) deployment MaxOver20: Binary variable: 1 represents maximum summer temperature greater 20C 0 indicates less 20C C16: Number times daily stream temperature exceeded 16C C20: Number times daily stream temperature exceeded 20C C24: Number times daily stream temperature exceeded 24C FlowCMS: Average stream flow (cubic meters per sec) August, year, 1950-2010 across 9 USGS gauges region AirMEANc: Average mean air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain AirMWMTc: Average maximum air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain. MWMT = maximum 7-day moving average maximum daily temperature (.e. maximum 7-day maximums) rcaAreaKm2: Reach contributing area (km2) edge feature site resides . RCA represents land area drains directly edge feature h2oAreaKm2: Watershed area (km2) lowermost location edge feature site resides ratio: Site ratio value; provides proportional distance downstream node edge site location upDist: Distance upstream stream outlet (m) afvArea: Additive function value calculated using waterhsed area (h2oAreaKm2) locID: Location identifier netID: Stream network identifier snapdist: Distance (m) site moved snapped edges pred1km CapeHorn: rid: Reach identifier edge site resides pid: Point identifier COMID: Common identifier NHD feature relationship GNIS_Name: Feature name edge site resides , found Geographic Names Information System AREAWTMAP: Area weighted mean annual precipitation (mm) lowermost location line segment site resides SLOPE: Slope line segment (cm/cm) site resides ELEV_DEM: Elevation site based 30m DEM rcaAreaKm2: Reach contributing area (km2) edge feature site resides . RCA represents land area drains directly edge feature h2oAreaKm2: Watershed area (km2) lowermost location edge feature site resides ratio: Site ratio value; provides proportional distance along edge site location upDist: Distance upstream stream outlet (m) afvArea: Additive function value calculated using watershed area (h2oAreaKm2) locID: Location identifier netID: Stream network identifier snapdist: Distance (m) site moved snapped edges FlowCMS: Average stream flow (cubic meters per sec) August, year, 1950-2010 across 9 USGS gauges region AirMEANc: Average mean air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain AirMWMTc: Average maximum air temperature (C) July 15 - August 31, 1980-2009 across 10 COOP air stations within domain. MWMT = maximum 7-day moving average maximum daily temperature(.e. maximum 7-day maximums)","code":""},{"path":[]},{"path":"/reference/model.frame.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model frame from a fitted model object — model.frame.SSN2","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"Extract model frame fitted model object.","code":""},{"path":"/reference/model.frame.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"","code":"# S3 method for class 'ssn_lm' model.frame(formula, ...)  # S3 method for class 'ssn_glm' model.frame(formula, ...)"},{"path":"/reference/model.frame.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"formula fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/model.frame.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"model frame contains variables used formula fitted model object.","code":""},{"path":[]},{"path":"/reference/model.frame.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model frame from a fitted model object — model.frame.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) model.frame(ssn_mod) #>    Summer_mn ELEV_DEM #> 1      14.92     1947 #> 2      14.72     1952 #> 3      14.61     1958 #> 4      15.22     1923 #> 5      14.49     1932 #> 6      15.29     1940 #> 7      15.07     1940 #> 8      14.94     1945 #> 9      14.99     1948 #> 10     15.02     1950 #> 11     15.11     1949 #> 12     14.70     1950 #> 13     14.58     1951 #> 14     11.38     1977 #> 15     10.72     1984 #> 16     10.43     1993 #> 17     10.13     2007 #> 18     10.12     2009 #> 19      9.81     2012 #> 20      9.76     2023 #> 21      9.77     2023 #> 22      9.53     2026 #> 23     12.57     1988 #> 24     12.42     2013 #> 25     14.10     2015 #> 26     13.70     2015 #> 27     13.22     2038 #> 28     13.08     2052 #> 29     12.19     2066 #> 30     11.32     2085 #> 31     12.11     1975 #> 32     11.82     1982 #> 33     11.91     1999 #> 34     11.41     2002 #> 35     11.71     1997 #> 36     11.54     2006 #> 37     11.14     2026 #> 38     12.06     2036 #> 39     12.09     2046 #> 40     11.96     2050 #> 41     11.02     2071 #> 42     10.21     2077 #> 43     11.16     2016 #> 44      8.75     2034 #> 45      9.02     2042"},{"path":"/reference/model.matrix.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model matrix from a fitted model object — model.matrix.SSN2","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"Extract model matrix (X) fitted model object.","code":""},{"path":"/reference/model.matrix.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"","code":"# S3 method for class 'ssn_lm' model.matrix(object, ...)  # S3 method for class 'ssn_glm' model.matrix(object, ...)"},{"path":"/reference/model.matrix.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/model.matrix.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"model matrix (fixed effects), whose rows represent observations whose columns represent explanatory variables corresponding fixed effect.","code":""},{"path":[]},{"path":"/reference/model.matrix.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model matrix from a fitted model object — model.matrix.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) model.matrix(ssn_mod) #>    (Intercept) ELEV_DEM #> 1            1     1947 #> 2            1     1952 #> 3            1     1958 #> 4            1     1923 #> 5            1     1932 #> 6            1     1940 #> 7            1     1940 #> 8            1     1945 #> 9            1     1948 #> 10           1     1950 #> 11           1     1949 #> 12           1     1950 #> 13           1     1951 #> 14           1     1977 #> 15           1     1984 #> 16           1     1993 #> 17           1     2007 #> 18           1     2009 #> 19           1     2012 #> 20           1     2023 #> 21           1     2023 #> 22           1     2026 #> 23           1     1988 #> 24           1     2013 #> 25           1     2015 #> 26           1     2015 #> 27           1     2038 #> 28           1     2052 #> 29           1     2066 #> 30           1     2085 #> 31           1     1975 #> 32           1     1982 #> 33           1     1999 #> 34           1     2002 #> 35           1     1997 #> 36           1     2006 #> 37           1     2026 #> 38           1     2036 #> 39           1     2046 #> 40           1     2050 #> 41           1     2071 #> 42           1     2077 #> 43           1     2016 #> 44           1     2034 #> 45           1     2042 #> attr(,\"assign\") #> [1] 0 1"},{"path":"/reference/names.SSN.html","id":null,"dir":"Reference","previous_headings":"","what":"names SSN object — names.SSN","title":"names SSN object — names.SSN","text":"Extract print names SSN object","code":""},{"path":"/reference/names.SSN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"names SSN object — names.SSN","text":"","code":"# S3 method for SSN names(x, ...)"},{"path":"/reference/names.SSN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"names SSN object — names.SSN","text":"x SSN object. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/names.SSN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"names SSN object — names.SSN","text":"Print variable names console","code":""},{"path":"/reference/plot.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot fitted model diagnostics — plot.SSN2","title":"Plot fitted model diagnostics — plot.SSN2","text":"Plot fitted model diagnostics residuals vs fitted values, quantile-quantile, scale-location, Cook's distance, residuals vs leverage, Cook's distance vs leverage.","code":""},{"path":"/reference/plot.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot fitted model diagnostics — plot.SSN2","text":"","code":"# S3 method for class 'ssn_lm' plot(x, which, ...)  # S3 method for class 'ssn_glm' plot(x, which, ...)"},{"path":"/reference/plot.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot fitted model diagnostics — plot.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). integer vector taking values 1 6, indicates plots return. Available plots described Details. length greater one, additional plots stepped order using <Return>. default = c(1, 2) ... arguments passed methods.","code":""},{"path":"/reference/plot.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot fitted model diagnostics — plot.SSN2","text":"return value. Function called plotting side effects.","code":""},{"path":"/reference/plot.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot fitted model diagnostics — plot.SSN2","text":"fitted model objects,, values make corresponding plot: 1: Standardized residuals vs fitted values (response) 2: Normal quantile-quantile plot standardized residuals 3: Scale-location plot standardized residuals 4: Cook's distance 5: Standardized residuals vs leverage 6: Cook's distance vs leverage","code":""},{"path":[]},{"path":"/reference/plot.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot fitted model diagnostics — plot.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) plot(ssn_mod, which = 1)"},{"path":"/reference/plot.Torgegram.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Torgegram — plot.Torgegram","title":"Plot Torgegram — plot.Torgegram","text":"Plot Torgegram","code":""},{"path":"/reference/plot.Torgegram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Torgegram — plot.Torgegram","text":"","code":"# S3 method for class 'Torgegram' plot(x, type, separate = FALSE, ...)"},{"path":"/reference/plot.Torgegram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Torgegram — plot.Torgegram","text":"x Torgegram object Torgegram(). type type semivariogram. Can take character values subset objects x. default names(x). separate type length greater one, whether type placed separate plot. default FALSE. ... arguments passed methods.","code":""},{"path":"/reference/plot.Torgegram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Torgegram — plot.Torgegram","text":"return value. Function called plotting side effects.","code":""},{"path":[]},{"path":"/reference/plot.Torgegram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Torgegram — plot.Torgegram","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  tg <- Torgegram(Summer_mn ~ 1, mf04p) plot(tg)"},{"path":"/reference/predict.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Model predictions (Kriging) — predict.SSN2","title":"Model predictions (Kriging) — predict.SSN2","text":"Predicted values intervals based fitted model object.","code":""},{"path":"/reference/predict.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model predictions (Kriging) — predict.SSN2","text":"","code":"# S3 method for class 'ssn_lm' predict(   object,   newdata,   se.fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   level = 0.95,   block = FALSE,   ... )  # S3 method for class 'ssn_glm' predict(   object,   newdata,   type = c(\"link\", \"response\"),   se.fit = FALSE,   interval = c(\"none\", \"confidence\", \"prediction\"),   newdata_size,   level = 0.95,   var_correct = TRUE,   ... )"},{"path":"/reference/predict.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model predictions (Kriging) — predict.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). newdata character vector indicates name prediction data set predictions desired (accessible via object$ssn.object$preds). Note prediction data must original SSN object used fit model. newdata omitted, predictions prediction data sets returned. Note name \".missing\" indicates prediction data set contains missing observations data used fit model. se.fit logical indicating standard errors returned. default FALSE. interval Type interval calculation. default \"none\". options \"confidence\" (confidence intervals) \"prediction\" (prediction intervals). level Tolerance/confidence level. default 0.95. block logical indicating whether block prediction entire region newdata returned. default FALSE, returns point predictions location newdata. Currently available model fit using ssn_lm() models fit using ssn_glm() family \"gaussian\". ... arguments. used (needed generic consistency). type scale (response link) predictions obtained using ssn_glm objects. newdata_size size value observation newdata used predicting binomial family. var_correct logical indicating whether return corrected prediction variances predicting via models fit using ssn_glm. default TRUE.","code":""},{"path":"/reference/predict.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model predictions (Kriging) — predict.SSN2","text":"se.fit FALSE, predict.ssn() returns vector predictions matrix predictions column names fit, lwr, upr interval \"confidence\" \"prediction\". se.fit TRUE, list following components returned: fit: vector matrix se.fit: standard error fit","code":""},{"path":"/reference/predict.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model predictions (Kriging) — predict.SSN2","text":"(empirical) best linear unbiased predictions (.e., Kriging predictions) site returned interval \"none\" \"prediction\" alongside standard errors. Prediction intervals also returned interval \"prediction\". interval \"confidence\", estimated mean returned alongside standard errors confidence intervals mean.","code":""},{"path":"/reference/predict.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model predictions (Kriging) — predict.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"pred1km\", overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) predict(ssn_mod, \"pred1km\") #>          1          2          3          4          5          6          7  #> 14.6563815 14.6963559 14.8045294 14.0808258 14.1858577 14.5667842 15.0145065  #>          8          9         10         11         12         13         14  #> 14.6297345 14.9009338 15.2402931 15.1355108 15.1875988 15.1627073 14.9902752  #>         15         16         17         18         19         20         21  #> 14.7615680 14.6655120 14.2122319 14.0809766 10.5777507 13.0996798 13.7106117  #>         22         23         24         25         26         27         28  #>  9.1798694  3.8625882  6.8631036 13.1910592 13.8704178 11.4421093 12.1914869  #>         29         30         31         32         33         34         35  #> 11.9059944 10.2075486 10.4876498 10.7563715  9.7136328  9.2602313 10.2477095  #>         36         37         38         39         40         41         42  #> 11.1327979 12.4559124 12.4129502 13.7980889 13.5323743 12.9587195 13.2053219  #>         43         44         45         46         47         48         49  #> 12.2647206 12.5451214 10.5334812 10.8185903 11.1379170  7.7356815  8.5300384  #>         50         51         52         53         54         55         56  #>  9.6657132 12.1276515 11.7448118 11.0619591 11.5867648 11.8614135 12.1651404  #>         57         58         59         60         61         62         63  #> 11.1660976  9.7461962  7.6051681  8.4940758  9.5695913  7.4720775 10.3052974  #>         64         65         66         67         68         69         70  #> 10.8934126 10.2716308 11.4632367 11.6943370  9.5595283 10.2041277 10.7122002  #>         71         72         73         74         75         76         77  #> 14.4973491 14.0748939 14.2079722  8.9680246 11.6946555 13.2951523  8.5007739  #>         78         79         80         81         82         83         84  #> 13.6894328 14.6807917 14.8873005 12.2498400 13.2731761 13.9552643 14.2619799  #>         85         86         87         88         89         90         91  #>  8.7029556  7.8742831  8.7757780  8.4003990 11.0899999  9.1269008 10.0760823  #>         92         93         94         95         96         97         98  #> -0.3864082  4.2490631  6.3251551  7.9575862  2.5376779  0.9631321  6.9393960  #>         99        100        101        102        103        104        105  #>  8.5366536  6.4191929 -3.3142339 -0.2880692  2.8745096  5.6957589  7.3225656  #>        106        107        108        109        110        111        112  #>  5.7764329  8.5758955  1.5118142  3.5878501  8.6968251 14.4646670 13.3400039  #>        113        114        115        116        117        118        119  #>  8.0509565 10.7070108 14.8419069 12.8760912 14.2394533  4.4802227  7.2513351  #>        120        121        122        123        124        125        126  #>  9.5447586  5.8262213  8.9258430 10.8651879 10.5039605 13.2731116 14.9161932  #>        127        128        129        130        131        132        133  #> 12.8312424  6.3336288  9.5137927  4.3386584  8.0150939 11.1795926  8.1475599  #>        134        135        136        137        138        139        140  #> 10.5660098 14.1260092 14.3295653  8.9033099 10.0699457  8.1529409 -4.3851596  #>        141        142        143        144        145        146        147  #> -0.1195973  4.3848365  7.4560414 10.8684912 13.2572061  5.5645821  9.1107947  #>        148        149        150        151        152        153        154  #> 11.5308779  3.9613491 10.4769224 -2.9452086 12.1802353  7.5331144 10.8708259  #>        155        156        157        158        159        160        161  #> 10.4096884  5.1723903  8.8549716 10.0791094 10.6195587 10.8187203 11.0860880  #>        162        163        164        165        166        167        168  #>  3.2295281 10.2763951 10.7490625 11.3581890 11.7284046  7.7797240  7.9294044  #>        169        170        171        172        173        174        175  #>  3.9415182 11.1746028  0.6856936  5.8826486  6.5147241 -0.1986592  5.0538075"},{"path":"/reference/print.SSN.html","id":null,"dir":"Reference","previous_headings":"","what":"Print SSN object — print.SSN","title":"Print SSN object — print.SSN","text":"Print information data found SSN object.","code":""},{"path":"/reference/print.SSN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print SSN object — print.SSN","text":"","code":"# S3 method for class 'SSN' print(x, ...)"},{"path":"/reference/print.SSN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print SSN object — print.SSN","text":"x SSN object. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/print.SSN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print SSN object — print.SSN","text":"Print summary console","code":""},{"path":"/reference/print.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Print values — print.SSN2","title":"Print values — print.SSN2","text":"Print fitted model objects summaries.","code":""},{"path":"/reference/print.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print values — print.SSN2","text":"","code":"# S3 method for class 'ssn_lm' print(x, digits = max(3L, getOption(\"digits\") - 3L), ...)  # S3 method for class 'ssn_glm' print(x, digits = max(3L, getOption(\"digits\") - 3L), ...)  # S3 method for class 'summary.ssn_lm' print(   x,   digits = max(3L, getOption(\"digits\") - 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )  # S3 method for class 'summary.ssn_glm' print(   x,   digits = max(3L, getOption(\"digits\") - 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )  # S3 method for class 'anova.ssn_lm' print(   x,   digits = max(getOption(\"digits\") - 2L, 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )  # S3 method for class 'anova.ssn_glm' print(   x,   digits = max(getOption(\"digits\") - 2L, 3L),   signif.stars = getOption(\"show.signif.stars\"),   ... )"},{"path":"/reference/print.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print values — print.SSN2","text":"x fitted model object ssn_lm(), fitted model object ssn_glm(), output summary(x) anova(x). digits number significant digits use printing. ... arguments passed methods. signif.stars Logical. TRUE, significance stars printed coefficient","code":""},{"path":"/reference/print.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print values — print.SSN2","text":"Printed fitted model objects summaries formatting.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a pseudo r-squared — pseudoR2.SSN2","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"Compute pseudo r-squared fitted model object.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"","code":"# S3 method for class 'ssn_lm' pseudoR2(object, adjust = FALSE, ...)  # S3 method for class 'ssn_glm' pseudoR2(object, adjust = FALSE, ...)"},{"path":"/reference/pseudoR2.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). adjust logical indicating whether pseudo r-squared adjusted account number explanatory variables. default FALSE. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"pseudo r-squared numeric vector.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"Several pseudo r-squared statistics exist literature. define pseudo r-squared one minus ratio deviance full model relative deviance null (intercept ) model. pseudo r-squared can viewed generalization classical r-squared definition seen one minus ratio error sums squares full model relative error sums squares null model. adjusted, adjustment analogous classical r-squared adjustment.","code":""},{"path":"/reference/pseudoR2.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute a pseudo r-squared — pseudoR2.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) pseudoR2(ssn_mod) #> [1] 0.5854895"},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics augment, glance, tidy spmodel AICc, covmatrix, dispersion_initial, dispersion_params, glances, loocv, pseudoR2, randcov_initial, randcov_params, varcomp","code":""},{"path":"/reference/residuals.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted model residuals — residuals.SSN2","title":"Extract fitted model residuals — residuals.SSN2","text":"Extract residuals fitted model object. resid alias.","code":""},{"path":"/reference/residuals.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted model residuals — residuals.SSN2","text":"","code":"# S3 method for class 'ssn_lm' residuals(object, type = \"response\", ...)  # S3 method for class 'ssn_lm' resid(object, type = \"response\", ...)  # S3 method for class 'ssn_lm' rstandard(model, ...)  # S3 method for class 'ssn_glm' residuals(object, type = \"deviance\", ...)  # S3 method for class 'ssn_glm' resid(object, type = \"deviance\", ...)  # S3 method for class 'ssn_glm' rstandard(model, ...)"},{"path":"/reference/residuals.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted model residuals — residuals.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). type \"response\" response residuals, \"pearson\" Pearson residuals, \"standardized\" standardized residuals. ssn_lm() fitted model objects, default \"response\". ssn_glm() fitted model objects, deviance residuals also available (\"deviance\") default residual type. ... arguments. used (needed generic consistency). model fitted model object ssn_lm() ssn_glm().","code":""},{"path":"/reference/residuals.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract fitted model residuals — residuals.SSN2","text":"residuals numeric vector.","code":""},{"path":"/reference/residuals.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract fitted model residuals — residuals.SSN2","text":"response residuals taken response minus fitted values response: \\(y - X \\hat{\\beta}\\). Pearson residuals response residuals pre-multiplied inverse square root. standardized residuals Pearson residuals divided square root one minus leverage (hat) value. standardized residuals often used check model assumptions, mean zero variance approximately one. rstandard() alias residuals(model, type = \"standardized\").","code":""},{"path":"/reference/residuals.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted model residuals — residuals.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) residuals(ssn_mod) #>           1           2           3           4           5           6  #>  0.50256099  0.47318349  0.56793047 -0.01642697 -0.43930648  0.63368951  #>           7           8           9          10          11          12  #>  0.41368951  0.45431200  0.60668549  0.70493449  0.76080999  0.38493449  #>          13          14          15          16          17          18  #>  0.29905899 -2.01370406 -2.43483257 -2.41771208 -2.23996911 -2.18172011  #>          19          20          21          22          23          24  #> -2.38934662 -2.06397713 -2.05397713 -2.19160364 -0.44833458  0.25477788  #>          25          26          27          28          29          30  #>  2.00302688  1.60302688  1.90789034  2.24563332  1.83337629  1.61174176  #>          31          32          33          34          35          36  #> -1.35195305 -1.40308157 -0.73296509 -1.13059160 -1.00121409 -0.86409361  #>          37          38          39          40          41          42  #> -0.58160364  0.67964134  1.05088633  1.05738432  0.83399878  0.22874577  #>          43          44          45  #> -0.90284862 -2.69860765 -2.15561167  resid(ssn_mod) #>           1           2           3           4           5           6  #>  0.50256099  0.47318349  0.56793047 -0.01642697 -0.43930648  0.63368951  #>           7           8           9          10          11          12  #>  0.41368951  0.45431200  0.60668549  0.70493449  0.76080999  0.38493449  #>          13          14          15          16          17          18  #>  0.29905899 -2.01370406 -2.43483257 -2.41771208 -2.23996911 -2.18172011  #>          19          20          21          22          23          24  #> -2.38934662 -2.06397713 -2.05397713 -2.19160364 -0.44833458  0.25477788  #>          25          26          27          28          29          30  #>  2.00302688  1.60302688  1.90789034  2.24563332  1.83337629  1.61174176  #>          31          32          33          34          35          36  #> -1.35195305 -1.40308157 -0.73296509 -1.13059160 -1.00121409 -0.86409361  #>          37          38          39          40          41          42  #> -0.58160364  0.67964134  1.05088633  1.05738432  0.83399878  0.22874577  #>          43          44          45  #> -0.90284862 -2.69860765 -2.15561167  rstandard(ssn_mod) #>           1           2           3           4           5           6  #>  0.54482527  0.24902815  0.62481728  0.49000705 -1.25153528  0.58378939  #>           7           8           9          10          11          12  #> -0.24979138 -0.70645636  0.42458717  0.82105356  0.66286002  0.15454106  #>          13          14          15          16          17          18  #> -0.16911790 -1.93108065 -0.94482469 -0.92746033 -0.30105744 -0.08748646  #>          19          20          21          22          23          24  #> -1.03751283 -0.14027557 -0.10922866 -0.52442537 -0.25655729 -1.50792407  #>          25          26          27          28          29          30  #>  1.88733195  0.08022385  0.84150045  2.10218328  0.49121189  0.62967096  #>          31          32          33          34          35          36  #> -0.94695811 -1.16773136  0.66216711 -0.89530470 -0.36015318 -0.34642484  #>          37          38          39          40          41          42  #> -0.34733356  2.77614973  0.15975430  0.24945263  0.41507374 -0.37899919  #>          43          44          45  #>  0.41832097 -1.69498836 -2.69823469"},{"path":"/reference/SSN2-package.html","id":null,"dir":"Reference","previous_headings":"","what":"SSN2: Spatial Modeling on Stream Networks — SSN2-package","title":"SSN2: Spatial Modeling on Stream Networks — SSN2-package","text":"Spatial statistical modeling prediction data stream networks, including models based -stream distance (Ver Hoef, J.M. Peterson, E.E., (2010) doi:10.1198/jasa.2009.ap08248 .) Models created using moving average constructions. Spatial linear models, including explanatory variables, can fit (restricted) maximum likelihood. Mapping graphical functions included.","code":""},{"path":[]},{"path":"/reference/SSN2-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SSN2: Spatial Modeling on Stream Networks — SSN2-package","text":"Maintainer: Michael Dumelle Dumelle.Michael@epa.gov (ORCID) Authors: Jay M. Ver Hoef jay.verhoef@noaa.gov Erin Peterson contributors: Alan Pearse [contributor] Dan Isaak [contributor]","code":""},{"path":"/reference/ssn_create_distmat.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"Creates collection (non-symmetric) matrices containing pairwise downstream hydrologic distances sites SSN object","code":""},{"path":"/reference/ssn_create_distmat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"","code":"ssn_create_distmat(   ssn.object,   predpts = NULL,   overwrite = FALSE,   among_predpts = FALSE,   only_predpts = FALSE )"},{"path":"/reference/ssn_create_distmat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"ssn.object SSN object predpts name prediction points SSN object. vector length greater one, name iterated upon. Default NULL. overwrite Logical. TRUE, overwrite existing distance matrices. Defaults FALSE. among_predpts Logical. TRUE, compute pairwise distances prediction sites. Defaults FALSE. only_predpts Logical. TRUE, compute distances prediction sites. Defaults FALSE.","code":""},{"path":"/reference/ssn_create_distmat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"ssn_create_distmat function creates collection hierarchical directories ssn$path directory, store pairwise distances sites associated SSN object. See details section additional information.","code":""},{"path":"/reference/ssn_create_distmat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"distance matrix contains hydrologic distance two sites SSN object needed fit spatial statistical model using tail-tail-autocovariance functions described Ver Hoef Peterson (2010). models implemented R via ssn_lm ssn_glm theSSN2 package. hydrologic distance information needed model covariance flow-connected (.e. water flows one location ) flow-unconnected (.e. water flow one location , reside network) locations differs. total hydrologic distance directionless measure; represents hydrologic distance two sites, ignoring flow direction. hydrologic distance site common downstream stream junction used creating models flow-unconnected pairs, term downstream hydrologic distance. contrast, total hydrologic distance used modeling flow-connected pairs, term total hydrologic distance. downstream hydrologic distance matrix provides enough information meet data requirements tail-tail-models. two locations flow-connected, downstream hydrologic distance upstream location downstream location greater zero, zero direction. two locations flow-unconnected downstream hydrologic distance greater zero directions. site's downstream hydrologic distance equal zero. format downstream hydrologic distance matrix efficient distance information needed fit tail-tail-models stored . example, matrix containing total hydrologic distance sites easily calculated adding downstream distance matrix transpose. downstream hydrologic distances calculated based binaryIDs stored matrices. matrices stored directory named ‘distance’, created ssn_create_distmat function within .ssn directory. distance directory always contain least one directory named ‘obs’, contains number .RData files, one network observed sites residing . naming convention files based netID number (e.g. dist.net1.RData). matrix ‘obs’ folder contains information form square matrix, contains downstream hydrologic distance pair observed sites network. Direction preserved, columns representing site rows representing site. Row column names correspond pid attribute site. argument predpts specified call function, downstream hydrologic distances observed prediction sites also computed. new directory created within distance directory, name corresponding names attribute preds (e.g. attributes(ssn.object$preds)$names). sequence .RData files created within directory, similar structure observed sites, except two objects stored network contains observed prediction sites. letters b used naming convention distinguish two objects (e.g. dist.net1.dist.net1.b). matrices objects represent necessarily square. matrices type , rows correspond observed locations columns prediction locations. contrast, rows correspond prediction locations columns observed locations matrices type b. Direction also preserved, columns representing site rows representing site object types. , row column names correspond pid attribute site. among_predpts = TRUE, downstream hydrologic distances also computed prediction sites, network. stored within distance directory name corresponding prediction points dataset. naming convention prediction prediction site distance matrices distance matrices stored ‘obs’ directory (e.g. dist.net1.RData). extra distance matrices needed perform block Kriging using predict.ssn_lm. only_predpts = TRUE, downstream hydrologic distances calculated observed sites . Pairwise distances calculated observed prediction locations . Pairwise distances prediction locations also calculated among_predpts = TRUE.","code":""},{"path":"/reference/ssn_create_distmat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Hydrologic Distances for an SSN object — ssn_create_distmat","text":"","code":"## Copy the MiddleForke04.ssn data to a local temporary directory. ## Only needed for this example. copy_lsn_to_temp() ## Import SSN data mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(\"pred1km.gpkg\", \"CapeHorn\"),   overwrite = TRUE )  ## Create distance matrices for observations and one set of prediction sites ## Include hydrologic distance matrices among prediction sites. ssn_create_distmat(mf04p,   predpts = \"pred1km\", overwrite = TRUE,   among_predpts = TRUE )  ## Create distance matrices for an additional set of prediction points. ## Distance matrices for observations and pred1km prediction sites are ## not recalculated. ssn_create_distmat(mf04p,   predpts = \"CapeHorn\", overwrite = TRUE,   among_predpts = TRUE, only_predpts = TRUE )"},{"path":"/reference/ssn_get_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"ssn_get_data function extracts sf data.frame observation prediction data SSN, ssn_lm, ssn_glm object.","code":""},{"path":"/reference/ssn_get_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"","code":"ssn_get_data(x, name = \"obs\")"},{"path":"/reference/ssn_get_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"x object class SSN, ssn_lm, ssn_glm. name internal name dataset object x. observed values, always \"obs\", default.","code":""},{"path":"/reference/ssn_get_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"sf data.frame","code":""},{"path":"/reference/ssn_get_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"internal name observed data objects class SSN \"obs\" default. another name specified, must represent prediction data set SSN, ssn_lm, ssn_glm object. SSN objects, names obtained using call names(x$preds). object classes, names obtained using call names(x$ssn.object$preds).","code":""},{"path":[]},{"path":"/reference/ssn_get_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data","text":"","code":"## Extract observed data from an SSN object # Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, predpts = \"pred1km\", overwrite = TRUE)  obs.df <- ssn_get_data(mf04p) dim(obs.df) #> [1] 45 26  ## Extract prediction data from an SSN object names(mf04p$preds) #> [1] \"pred1km\" pred1km.df <- ssn_get_data(mf04p, name = \"pred1km\") names(pred1km.df) #>  [1] \"rid\"        \"pid\"        \"COMID\"      \"AREAWTMAP\"  \"SLOPE\"      #>  [6] \"ELEV_DEM\"   \"FlowCMS\"    \"AirMEANc\"   \"AirMWMTc\"   \"rcaAreaKm2\" #> [11] \"h2oAreaKm2\" \"ratio\"      \"snapdist\"   \"upDist\"     \"afvArea\"    #> [16] \"locID\"      \"netID\"      \"netgeom\"    \"geom\"        ## extract observed data from an ssn_lm object ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) obs.mod.df <- ssn_get_data(ssn_mod) summary(obs.mod.df) #>       rid              pid      STREAMNAME            COMID          #>  Min.   :  1.00   Min.   : 1   Length:45          Min.   :23519297   #>  1st Qu.: 21.00   1st Qu.:12   Class :character   1st Qu.:23519365   #>  Median : 42.00   Median :23   Mode  :character   Median :23519479   #>  Mean   : 42.27   Mean   :23                      Mean   :23519557   #>  3rd Qu.: 61.00   3rd Qu.:34                      3rd Qu.:23519529   #>  Max.   :110.00   Max.   :45                      Max.   :23522805   #>    AREAWTMAP          SLOPE             ELEV_DEM       Source          #>  Min.   : 786.4   Min.   :0.000000   Min.   :1923   Length:45          #>  1st Qu.: 968.2   1st Qu.:0.002740   1st Qu.:1952   Class :character   #>  Median : 995.2   Median :0.005680   Median :2006   Mode  :character   #>  Mean   : 998.6   Mean   :0.006743   Mean   :1999                      #>  3rd Qu.:1032.7   3rd Qu.:0.008430   3rd Qu.:2026                      #>  Max.   :1130.4   Max.   :0.044260   Max.   :2085                      #>    Summer_mn       MaxOver20           C16            C20              C24    #>  Min.   : 8.75   Min.   :0.0000   Min.   : 0.0   Min.   : 0.000   Min.   :0   #>  1st Qu.:11.02   1st Qu.:0.0000   1st Qu.:17.0   1st Qu.: 0.000   1st Qu.:0   #>  Median :12.06   Median :0.0000   Median :32.0   Median : 0.000   Median :0   #>  Mean   :12.35   Mean   :0.3111   Mean   :26.4   Mean   : 2.867   Mean   :0   #>  3rd Qu.:14.58   3rd Qu.:1.0000   3rd Qu.:39.0   3rd Qu.: 3.000   3rd Qu.:0   #>  Max.   :15.29   Max.   :1.0000   Max.   :41.0   Max.   :19.000   Max.   :0   #>     FlowCMS         AirMEANc        AirMWMTc      rcaAreaKm2     #>  Min.   :28.67   Min.   :21.12   Min.   :35.1   Min.   :0.0234   #>  1st Qu.:28.67   1st Qu.:21.12   1st Qu.:35.1   1st Qu.:0.8613   #>  Median :28.67   Median :21.12   Median :35.1   Median :2.1780   #>  Mean   :28.67   Mean   :21.12   Mean   :35.1   Mean   :2.2958   #>  3rd Qu.:28.67   3rd Qu.:21.12   3rd Qu.:35.1   3rd Qu.:3.3993   #>  Max.   :28.67   Max.   :21.12   Max.   :35.1   Max.   :5.0175   #>    h2oAreaKm2          ratio           snapdist             upDist        #>  Min.   :  3.399   Min.   :0.0143   Min.   :0.000e+00   Min.   :  909.9   #>  1st Qu.: 16.790   1st Qu.:0.1967   1st Qu.:0.000e+00   1st Qu.: 6281.2   #>  Median : 29.092   Median :0.4720   Median :0.000e+00   Median :10020.0   #>  Mean   : 36.063   Mean   :0.4565   Mean   :5.174e-12   Mean   :10176.9   #>  3rd Qu.: 46.260   3rd Qu.:0.6936   3rd Qu.:0.000e+00   3rd Qu.:14295.2   #>  Max.   :124.250   Max.   :0.9739   Max.   :1.164e-10   Max.   :19566.2   #>     afvArea            locID        netID         netgeom          #>  Min.   :0.05144   Min.   : 1   Min.   :1.000   Length:45          #>  1st Qu.:0.15212   1st Qu.:12   1st Qu.:1.000   Class :character   #>  Median :0.17089   Median :23   Median :2.000   Mode  :character   #>  Mean   :0.24583   Mean   :23   Mean   :1.711                      #>  3rd Qu.:0.29944   3rd Qu.:34   3rd Qu.:2.000                      #>  Max.   :1.00000   Max.   :45   Max.   :2.000                      #>           geometry  #>  POINT        :45   #>  epsg:NA      : 0   #>  +proj=aea ...: 0   #>                     #>                     #>"},{"path":"/reference/ssn_get_netgeom.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract netgeom column — ssn_get_netgeom","title":"Extract netgeom column — ssn_get_netgeom","text":"Extract topological information netgeom column","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract netgeom column — ssn_get_netgeom","text":"","code":"ssn_get_netgeom(x, netvars = \"all\", reformat = FALSE)"},{"path":"/reference/ssn_get_netgeom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract netgeom column — ssn_get_netgeom","text":"x sf data.frame found SSN object netgeom column vector netvars Network coordinate variables return. Default \"\". edges, valid column names include: \"NetworkID\", \"SegmentID\", \"DistanceUpstream\". point datasets, valid column names include \"NetworkID\", \"SegmentID\", \"DistanceUpstream\", \"ratio\", \"pid\", \"locID\". reformat Convert network coordinate variables character numeric.","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract netgeom column — ssn_get_netgeom","text":"one column requested using netvars, function returns data.frame (default). one column requested, result vector.","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract netgeom column — ssn_get_netgeom","text":"SSN object generated using importSSN function, text column named \"netgeom\" added edges, observed sites, prediction sites (exist) data.frames. netgeom column contains data used describe edge site features relate one another topological space. edges, netgeom values contain \"ENETWORK\" prefix, 3 space delimited values parentheses: \"ENETWORK (NetworkID SegmentID DistanceUpstream)\". point datasets (observed prediction sites), values contain \"SNETWORK\" prefix, followed 6 space delimited values parentheses: \"SNETWORK (NetworkID SegmentID DistanceUpstream ratio pid locID)\". ssn_get_netgeom function extracts converts values text numeric, returning either data.frame (default) vector containing variables requested via netvars.","code":""},{"path":"/reference/ssn_get_netgeom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract netgeom column — ssn_get_netgeom","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_get_netgeom(mf04p$obs) #>    NetworkID SegmentID DistanceUpstream              ratio pid locID #> 1          1         1  14295.196345228 0.0143033570193725   1     1 #> 2          1         1 16258.1867133034  0.626023498621516   2     2 #> 3          1         1 16794.9565779269  0.793295301344197   3     3 #> 4          1         5 909.937835040871  0.231819253798763   4     4 #> 5          1         5 1692.25764134473  0.515501826191856   5     5 #> 6          1        10 8046.69959654784   0.28093206947971   6     6 #> 7          1        13 10443.8838684547  0.613552417711556   7     7 #> 8          1        15 13865.3815621499  0.907439930466464   8     8 #> 9          1        17 14323.7279440717  0.182376231507493   9     9 #> 10         1        17 14580.7850080222  0.812239349337638  10    10 #> 11         1        19 15652.4273971773  0.549770203960467  11    11 #> 12         1        21 18642.8232807059  0.157200895942302  12    12 #> 13         1        21 19566.2045370585  0.639335640749524  13    13 #> 14         2        33 3194.82758834595  0.321185040919459  14    14 #> 15         2        34 3896.10674038296 0.0690361937952187  15    15 #> 16         2        34 4857.50889815636  0.370693074274211  16    16 #> 17         2        34 5886.68489225268  0.693615191031243  17    17 #> 18         2        34 6281.22686215765  0.817409691487961  18    18 #> 19         2        36 7300.18896771736  0.472020290982274  19    19 #> 20         2        37 8386.87883528115  0.467609736277533  20    20 #> 21         2        37 8626.98482357545  0.595431065021748  21    21 #> 22         2        39 10019.9727397032  0.481795562544374  22    22 #> 23         2        42 4049.94559903902  0.241892607277062  23    23 #> 24         2        48  8914.2984583901  0.356571475118011  24    24 #> 25         2        49 9295.32664671222  0.223896901080731  25    25 #> 26         2        49 9711.54632884272  0.751115282023418  26    26 #> 27         2        52 12658.4900937506  0.170490508605362  27    27 #> 28         2        52 14103.2031153509  0.799102017170274  28    28 #> 29         2        53 16019.2025746132  0.601882501113124  29    29 #> 30         2        55 18447.5789987791  0.132889204229652  30    30 #> 31         2        58 3405.72135312965  0.609185232315584  31    31 #> 32         2        58 4046.04417334624  0.973908806043524  32    32 #> 33         2        60 5365.42224130655  0.845110196771577  33    33 #> 34         2        61 6489.59759943874  0.885802548015722  34    34 #> 35         2        61 5794.51481160791  0.258397975972908  35    35 #> 36         2        63 7547.78605502196  0.128723349502858  36    36 #> 37         2        64 10022.9334714274  0.703550475006261  37    37 #> 38         2        66 10879.7081769225  0.078434677103365  38    38 #> 39         2        67 11985.9719106275 0.0893958570488306  39    39 #> 40         2        67 12357.0088970759  0.242744463683594  40    40 #> 41         2        68 15080.7113188755  0.665890884639618  41    41 #> 42         2        69 15672.8306470736 0.0751878474972271  42    42 #> 43         2        71 9921.48731634449  0.881800162965071  43    43 #> 44         2        95  10871.508857035  0.196749884784435  44    44 #> 45         2       110 11795.0008892232 0.0160819902932108  45    45 ssn_get_netgeom(mf04p$edges, \"DistanceUpstream\") #>     DistanceUpstream #> 1   17458.2653443092 #> 2   18357.2013473613 #> 3   20793.1520843239 #> 4   270.643033814759 #> 5   3028.37256123817 #> 6   4402.46334120276 #> 7   5256.11048818444 #> 8   6616.51936213131 #> 9   7886.30445015111 #> 10  8457.24370494599 #> 11  9024.57776816167 #> 12   9536.5135324752 #> 13  11015.3934289375 #> 14  12325.9762679366 #> 15  14022.4029321479 #> 16  14249.2973300564 #> 17  14657.4130883438 #> 18  14790.6182826554 #> 19   16358.198897817 #> 20  18341.7531704135 #> 21  20256.9465189487 #> 22  8056.91207732279 #> 23  9607.96481229633 #> 24  13105.9441561817 #> 25  14112.2130888775 #> 26   16613.992659694 #> 27  5611.09385125239 #> 28  7959.14006422809 #> 29  1556.81337220539 #> 30  1920.27673834595 #> 31  2336.21218484763 #> 32  2967.11873356165 #> 33  3676.08342921632 #> 34  6863.15529791302 #> 35  7113.95880148187 #> 36  7508.49727746126 #> 37  9386.94738162795 #> 38  9805.94712673543 #> 39  10250.1720835564 #> 40  10289.8733396204 #> 41  13963.1651296954 #> 42  5221.65430891865 #> 43  5793.50030354748 #> 44  6280.45578933733 #> 45  7277.66171418537 #> 46  7449.52533785191 #> 47   8801.0974364933 #> 48  9118.56820966533 #> 49  9908.03173592987 #> 50  11074.7483484508 #> 51  12266.6584863342 #> 52  14564.9190207277 #> 53  16981.1440334103 #> 54  17918.0916672167 #> 55  21902.5186538316 #> 56  25336.5841329242 #> 57  26164.3010725867 #> 58  4091.85088314091 #> 59  4586.16095740494 #> 60  5508.24341112338 #> 61  6616.11354596741 #> 62  7197.21170561536 #> 63  9920.68301645799 #> 64  10066.0179406772 #> 65  10803.9630452421 #> 66  11769.6727858538 #> 67  14189.2382099604 #> 68  15528.0057610398 #> 69  17454.1798816533 #> 70  20006.2582618593 #> 71  10029.1136375311 #> 72  10892.8779616433 #> 73  11270.8487206139 #> 74   11844.159173113 #> 75  13763.1476372381 #> 76  12715.7735594781 #> 77  14546.8345236079 #> 78  17838.6435897391 #> 79  8887.67517891411 #> 80  8891.97355540552 #> 81  12839.2250328338 #> 82  5433.90934000716 #> 83   6124.9118981488 #> 84  6267.87682697666 #> 85  6677.35714098017 #> 86  8073.26914055922 #> 87  11585.4944923386 #> 88  12057.2455602907 #> 89  13058.8169909537 #> 90  19026.6198060218 #> 91  18924.7499077214 #> 92   19525.005382963 #> 93  19880.9175088897 #> 94  21486.7688274084 #> 95  13246.0911694392 #> 96  14399.5534168745 #> 97  10396.7058430995 #> 98  12938.8411905711 #> 99  17726.9196395517 #> 100 15870.5572203595 #> 101 20989.1229380097 #> 102 15125.2684792853 #> 103 15389.8725132931 #> 104 14793.2194915557 #> 105 14808.0000676813 #> 106 18347.8931324167 #> 107 19583.0855759017 #> 108 14729.3812837901 #> 109 6772.63827190942 #> 110 13344.6086555785 #> 111 12092.1446817101 #> 112 9641.34969827759 #> 113 7320.38318896619 #> 114 12444.8239211523 #> 115 8982.07724984958 #> 116  10769.239683546 #> 117  8257.2511028149 #> 118 14762.1705229917 #> 119 24929.5055970415 #> 120 13100.4871772562 #> 121 19498.8928168494 #> 122 7406.49129825225 #> 123 17122.0987313141 #> 124 6939.68851956591 #> 125 7564.94695586144 #> 126 10026.1216247482 #> 127 7873.39351216316 #> 128 13530.4857971852 #> 129 20684.2844080521 #> 130 3786.82459872068 #> 131 4527.88648086542 #> 132 18932.7126207706 #> 133 8241.47433250851 #> 134 9033.46264256387 #> 135 5684.90317017307 #> 136 9288.41653485173 #> 137 14064.8357297009 #> 138 13639.3482775505 #> 139 9878.97609862289 #> 140 6900.36121898015 #> 141 6948.21909422703 #> 142 9589.88284428016 #> 143 14957.8325228195 #> 144 8207.03249122928 #> 145 12218.7636229605 #> 146 14966.1256740333 #> 147  10353.691753873 #> 148 10436.3448078734 #> 149 16900.7317617924 #> 150  11569.327853944 #> 151 13332.8551098625 #> 152 11680.4722375554 #> 153 12568.0861312944 #> 154 13081.1159536959 #> 155 12724.5995838964 #> 156 17091.6681515776 #> 157 15598.4603054857 #> 158 14400.3070941811 #> 159 15886.9084558951 #> 160 10139.4205086529 #> 161 7406.60614162612 #> 162 6346.02608978609 #> 163 7466.07885573737"},{"path":"/reference/ssn_get_netgeometry.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract netgeometry column — ssn_get_netgeometry","title":"Extract netgeometry column — ssn_get_netgeometry","text":"Extract topological information netgeom column","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract netgeometry column — ssn_get_netgeometry","text":"","code":"ssn_get_netgeom(x, netvars = \"all\", reformat = FALSE)"},{"path":"/reference/ssn_get_netgeometry.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract netgeometry column — ssn_get_netgeometry","text":"x sf data.frame found SSN object netgeom column vector netvars Network coordinate variables return. Default \"\". edges, valid column names include: \"NetworkID\", \"SegmentID\", \"DistanceUpstream\". point datasets, valid column names include \"NetworkID\", \"SegmentID\", \"DistanceUpstream\", \"ratio\", \"pid\", \"locID\". reformat Convert network coordinate variables character numeric.","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract netgeometry column — ssn_get_netgeometry","text":"one column requested using netvars, function returns data.frame (default). one column requested, result vector.","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract netgeometry column — ssn_get_netgeometry","text":"SSN object generated using importSSN function, text column named \"netgeom\" added edges, observed sites, prediction sites (exist) data.frames. netgeom column contains data used describe edge site features relate one another topological space. edges, netgeom values contain \"ENETWORK\" prefix, 3 space delimited values parentheses: \"ENETWORK (NetworkID SegmentID DistanceUpstream)\". point datasets (observed prediction sites), values contain \"SNETWORK\" prefix, followed 6 space delimited values parentheses: \"SNETWORK (NetworkID SegmentID DistanceUpstream ratio pid locID)\". ssn_get_netgeom function extracts converts values text numeric, returning either data.frame (default) vector containing variables requested via netvars.","code":""},{"path":"/reference/ssn_get_netgeometry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract netgeometry column — ssn_get_netgeometry","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_get_netgeom(mf04p$obs) #>    NetworkID SegmentID DistanceUpstream           ratio pid locID #> 1          2        32    3194.82758834  0.321185040918   1     1 #> 2          2        33    3896.10674037 0.0690361937938   2     2 #> 3          2        33    4857.50889816  0.370693074276   3     3 #> 4          2        33    5886.68489225  0.693615191033   4     4 #> 5          2        33    6281.22686216   0.81740969149   5     5 #> 6          2        35    7300.18896771  0.472020290969   6     6 #> 7          2        36    8386.87883527  0.467609736278   7     7 #> 8          2        36    8626.98482356  0.595431065021   8     8 #> 9          2        38    10019.9727397  0.481795562545   9     9 #> 10         2        41    4049.94559903  0.241892607278  10    10 #> 11         2        47    8914.29845837  0.356571475119  11    11 #> 12         2        48     9295.3266467  0.223896901083  12    12 #> 13         2        51    12659.9947331  0.171145194729  13    13 #> 14         2        51    14103.2031154  0.799102017173  14    14 #> 15         2        52    16019.2025746  0.601882501114  15    15 #> 16         2        54    18447.5789988  0.132889204232  16    16 #> 17         2        57    3405.72135314  0.609185232313  17    17 #> 18         2        57    4046.04417336  0.973908806041  18    18 #> 19         2        59    5365.42224132  0.845110196768  19    19 #> 20         2        60    6489.59766227  0.885802604708  20    20 #> 21         2        60    5794.51481162  0.258397975972  21    21 #> 22         2        62    7547.78605505  0.128723349502  22    22 #> 23         2        63    10022.9334715   0.70355047499  23    23 #> 24         2        65     10879.708177  0.078434677104  24    24 #> 25         2        66    11985.9719107 0.0893958570497  25    25 #> 26         2        66    12357.0088971  0.242744463686  26    26 #> 27         2        67    15080.7113189  0.665890884643  27    27 #> 28         2        70    9921.48731634  0.881800162966  28    28 #> 29         2        94     10871.508857  0.196749884783  29    29 #> 30         2       109    11795.0008893 0.0160819902947  30    30 #> 31         1         0    16786.5620802   0.79067935202  31    31 #> 32         1         0    14295.1963452 0.0143033570212  32    32 #> 33         1         0    16258.1596546  0.626015066404  33    33 #> 34         1         4    909.868496941  0.231794110614  34    34 #> 35         1         4    1692.25764135  0.515501826192  35    35 #> 36         1         9    8046.69965981  0.280932180239  36    36 #> 37         1        12    10443.8838685  0.613552417712  37    37 #> 38         1        14    13865.3815622  0.907439930469  38    38 #> 39         1        16    14323.7279441  0.182376231574  39    39 #> 40         1        16     14580.785008  0.812239349337  40    40 #> 41         1        18    15652.4273972   0.54977020396  41    41 #> 42         1        20    18642.8233007  0.157200906378  42    42 #> 43         1        20    19566.2045371  0.639335640746  43    43 #> 44         2        48    9711.54632884  0.751115282016  44    44 #> 45         2        68    15672.8306471 0.0751878474962  45    45 ssn_get_netgeom(mf04p$edges, \"DistanceUpstream\") #>     DistanceUpstream #> 1      17458.2653443 #> 2      18357.2013474 #> 3      20793.1520844 #> 4      270.643033817 #> 5      3028.37256124 #> 6      4402.46334121 #> 7       5256.1104882 #> 8      6616.51936215 #> 9      7886.30445018 #> 10     8457.24370497 #> 11     9024.57776818 #> 12     9536.51353251 #> 13      11015.393429 #> 14      12325.976268 #> 15     14022.4029322 #> 16     14249.2973301 #> 17     14657.4130884 #> 18     14790.6182827 #> 19     16358.1988978 #> 20     18341.7531704 #> 21      20256.946519 #> 22     8056.91207735 #> 23     9607.96481233 #> 24     13105.9441562 #> 25     14112.2130889 #> 26     16613.9926597 #> 27     5611.09385126 #> 28     7959.14006425 #> 29     1556.81337221 #> 30     1920.27673835 #> 31     2336.21218485 #> 32     2967.11873356 #> 33     3676.08342921 #> 34     6863.15529791 #> 35     7113.95880148 #> 36     7508.49727746 #> 37     9386.94738161 #> 38     9805.94712673 #> 39     10250.1720836 #> 40     10289.8733396 #> 41     13963.1651297 #> 42      5221.6543089 #> 43     5793.50030353 #> 44     6280.45578932 #> 45     7277.66171417 #> 46     7449.52533783 #> 47     8801.09743647 #> 48     9118.56820965 #> 49     9908.03173594 #> 50     11074.7483485 #> 51     12266.6584864 #> 52     14564.9190207 #> 53     16981.1440334 #> 54     17918.0916672 #> 55     21902.5186538 #> 56      25336.584133 #> 57     26164.3010726 #> 58     4091.85088316 #> 59     4586.16095742 #> 60     5508.24341114 #> 61     6616.11354599 #> 62     7197.21170564 #> 63     9920.68301648 #> 64     10066.0179407 #> 65     10803.9630453 #> 66     11769.6727859 #> 67       14189.23821 #> 68     15528.0057611 #> 69     17454.1798817 #> 70     20006.2582619 #> 71     10029.1136375 #> 72     10892.8779616 #> 73     11270.8487206 #> 74     11844.1591731 #> 75     13763.1476372 #> 76     12715.7735595 #> 77     14546.8345236 #> 78     17838.6435898 #> 79     8887.67517889 #> 80     8891.97355539 #> 81     12839.2250328 #> 82     5433.90934003 #> 83     6124.91189817 #> 84       6267.876827 #> 85     6677.35714099 #> 86     8073.26914058 #> 87     11585.4944924 #> 88     12057.2455603 #> 89      13058.816991 #> 90      19026.619806 #> 91     18924.7499077 #> 92      19525.005383 #> 93     19880.9175089 #> 94     21486.7688274 #> 95     13246.0911694 #> 96     14399.5534169 #> 97     10396.7058431 #> 98     12938.8411906 #> 99     17726.9196396 #> 100    15870.5572204 #> 101     20989.122938 #> 102    15125.2684793 #> 103    15389.8725133 #> 104    14793.2194916 #> 105    14808.0000677 #> 106    18347.8931324 #> 107    19583.0855759 #> 108    14729.3812838 #> 109    6772.63827193 #> 110    13344.6086556 #> 111    12092.1446817 #> 112     9641.3496983 #> 113    7320.38318899 #> 114    12444.8239212 #> 115    8982.07724987 #> 116    10769.2396836 #> 117    8257.25110283 #> 118     14762.170523 #> 119     24929.505597 #> 120    13100.4871773 #> 121    19498.8928168 #> 122    7406.49129826 #> 123    17122.0987313 #> 124    6939.68851959 #> 125    7564.94695587 #> 126    10026.1216248 #> 127    7873.39351219 #> 128    13530.4857972 #> 129    20684.2844081 #> 130    3786.82459871 #> 131    4527.88648086 #> 132    18932.7126208 #> 133     8241.4743325 #> 134    9033.46264257 #> 135    5684.90317016 #> 136    9288.41653484 #> 137    14064.8357297 #> 138    13639.3482775 #> 139    9878.97609863 #> 140    6900.36121897 #> 141    6948.21909422 #> 142    9589.88284429 #> 143    14957.8325229 #> 144    8207.03249122 #> 145    12218.7636229 #> 146     14966.125674 #> 147    10353.6917539 #> 148    10436.3448079 #> 149    16900.7317618 #> 150    11569.3278539 #> 151    13332.8551099 #> 152    11680.4722376 #> 153    12568.0861313 #> 154    13081.1159537 #> 155    12724.5995839 #> 156    17091.6681516 #> 157    15598.4603055 #> 158    14400.3070942 #> 159    15886.9084559 #> 160    10139.4205086 #> 161    7406.60614164 #> 162    6346.02608977 #> 163    7466.07885572"},{"path":"/reference/ssn_get_stream_distmat.html","id":null,"dir":"Reference","previous_headings":"","what":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"Extracts stream network distance matrices observation prediction data SSN object.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"","code":"ssn_get_stream_distmat(x, name = \"obs\")"},{"path":"/reference/ssn_get_stream_distmat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"x SSN object name Internal name dataset object x. observed values, always \"obs\", default.  get stream network distance matrix prediction data set, name dataset must given, quotes.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"list asymmetric downstream stream distance matrices, network.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"internal name observed data objects class SSN \"obs\" default. another name specified, must represent prediction data set SSN object. SSN objects, names obtained using call names(x$preds). Note traditional symmetric distance matrices. First, distances SSN object represent stream distance, hydrologic distance, distance two locations movement restricted branching stream network. Another important difference distance matrices SSN objects contain downstream stream distance two locations, making asymmetric. asymmetry provides way store two types spatial relationships based stream distance: Flow-connected: Water flows upstream site downstream site. Flow-unconnected: Two sites reside stream network, share flow. example, two sites flow-connected downstream distance upstream site downstream site > 0, downstream distance downstream site upstream site = 0. flow-unconnected sites, downstream distance represents distance site closest downstream junction > 0 directions. Direction preserved, columns representing site rows representing site. Row column names correspond unique point identifier \"pid\" site. matrix, also possible get total stream distance (downstream + upstream) two sites network (see examples additional details). Stream distances calculated within network asymmetric matrices also stored network.  observation data, single square matrix distances returned network, names based netID value (e.g. \"dist.net1\", \"dist.net2\", etc.). However, two distance matrices (\"\" \"b\") required store downstream distance observed prediction sites. label \"\" represents downstream stream distance prediction sites observation sites, label \"b\" represents distance observation sites predictions sites.  Thus, list prediction matrices labeled \"dist.net1.\" downstream distance prediction sites columns, observation sites rows, first network. prediction matrix labeled \"dist.net1.b\" contains downstream distances observation sites columns prediction sites rows, first network. downstream distance matrices observations predictions rectangular, unless number observation prediction locations equal.  argument amongPreds = TRUE used function ssn_create_distmat, distance prediction sites also returned, using labelling convention among observation sites. , matrices network labeled \"dist.net1\", \"dist.net2\", etc., first second network, etc.","code":""},{"path":"/reference/ssn_get_stream_distmat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks. Journal American Statistical Association, 105(489), 22–24","code":""},{"path":[]},{"path":"/reference/ssn_get_stream_distmat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get stream distance matrices from an SSN object — ssn_get_stream_distmat","text":"","code":"## For this example only, copy MiddleFork04.ssn directory to R's ## temporary directory copy_lsn_to_temp() ## Create an SSN object with prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = \"pred1km\", overwrite = TRUE )  ## Create distance matrices for obs x obs, obs x preds, and preds x ## preds if (FALSE) { # \\dontrun{ ssn_create_distmat(mf04p,   predpts = \"pred1km\", among_predpts = TRUE,   overwrite = TRUE ) } # }  ## Check names of prediction datasets names(mf04p$preds) #> [1] \"pred1km\"  ## Get list of stream distance matrices for observations dist_obs <- ssn_get_stream_distmat(mf04p) ## Display structure of list and names of the matrices str(dist_obs) #> List of 2 #>  $ dist.net1: num [1:13, 1:13] 0 0 0 13385 12603 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:13] \"1\" \"2\" \"3\" \"4\" ... #>   .. ..$ : chr [1:13] \"1\" \"2\" \"3\" \"4\" ... #>  $ dist.net2: num [1:32, 1:32] 0 0 0 0 0 0 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:32] \"14\" \"15\" \"16\" \"17\" ... #>   .. ..$ : chr [1:32] \"14\" \"15\" \"16\" \"17\" ... names(dist_obs) #> [1] \"dist.net1\" \"dist.net2\" ## Look at first 5 rows and columns in asymmetric ## downstream only distance matrix for netID == 1 dist_obs$dist.net1[1:5, 1:5] #>          1        2          3 4        5 #> 1     0.00  1962.99  2499.7602 0   0.0000 #> 2     0.00     0.00   536.7699 0   0.0000 #> 3     0.00     0.00     0.0000 0   0.0000 #> 4 13385.26 15348.25 15885.0187 0 782.3198 #> 5 12602.94 14565.93 15102.6989 0   0.0000  ## Create symmetric total stream distance matrix between ## observations strdist_2 <- dist_obs$dist.net2 + t(dist_obs$dist.net2) strdist_2[5:10, 5:10] #>          18       19       20       21       22       23 #> 18    0.000 1018.962 2105.652 2345.758 3738.746 2979.006 #> 19 1018.962    0.000 1086.690 1326.796 2719.784 3997.968 #> 20 2105.652 1086.690    0.000  240.106 1633.094 5084.658 #> 21 2345.758 1326.796  240.106    0.000 1392.988 5324.764 #> 22 3738.746 2719.784 1633.094 1392.988    0.000 6717.751 #> 23 2979.006 3997.968 5084.658 5324.764 6717.751    0.000  ## Get maximum downstream only distance between ## observations on netID == 2 a.mat <- pmax(dist_obs$dist.net2, t(dist_obs$dist.net2)) a.mat[5:10, 5:10] #>          18       19       20       21       22       23 #> 18    0.000 1018.962 2105.652 2345.758 3738.746 2605.143 #> 19 1018.962    0.000 1086.690 1326.796 2719.784 3624.106 #> 20 2105.652 1086.690    0.000  240.106 1633.094 4710.795 #> 21 2345.758 1326.796  240.106    0.000 1392.988 4950.901 #> 22 3738.746 2719.784 1633.094 1392.988    0.000 6343.889 #> 23 2605.143 3624.106 4710.795 4950.901 6343.889    0.000  ## Get minimum downstream only distance between observations. If ## minimum distance == 0, sites are flow-connected b.mat <- pmin(dist_obs$dist.net2, t(dist_obs$dist.net2)) b.mat[5:10, 5:10] #>          18       19       20       21       22       23 #> 18   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 19   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 20   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 21   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 22   0.0000   0.0000   0.0000   0.0000   0.0000 373.8622 #> 23 373.8622 373.8622 373.8622 373.8622 373.8622   0.0000  ## Get distance matrices for pred1km dist_pred1km <- ssn_get_stream_distmat(mf04p, name = \"pred1km\") str(dist_pred1km) #> List of 6 #>  $ dist.net1  : num [1:57, 1:57] 0 1000 2000 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>  $ dist.net1.a: num [1:13, 1:57] 2163 200 0 15548 14766 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:13] \"1\" \"2\" \"3\" \"4\" ... #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>  $ dist.net1.b: num [1:57, 1:13] 0 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:57] \"46\" \"47\" \"48\" \"49\" ... #>   .. ..$ : chr [1:13] \"1\" \"2\" \"3\" \"4\" ... #>  $ dist.net2  : num [1:118, 1:118] 0 0 0 0 0 0 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>  $ dist.net2.a: num [1:32, 1:118] 0 0 0 0 0 0 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:32] \"14\" \"15\" \"16\" \"17\" ... #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>  $ dist.net2.b: num [1:118, 1:32] 2638 0 0 0 0 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : chr [1:118] \"74\" \"75\" \"76\" \"77\" ... #>   .. ..$ : chr [1:32] \"14\" \"15\" \"16\" \"17\" ... names(dist_pred1km) #> [1] \"dist.net1\"   \"dist.net1.a\" \"dist.net1.b\" \"dist.net2\"   \"dist.net2.a\" #> [6] \"dist.net2.b\" ## Look at first 5 rows and columns of downstream only distances ## FROM prediction sites TO observed sites on netID == 1 dist_pred1km$dist.net1.a[1:5, 1:5] #>           46        47        48        49        50 #> 1  2163.0690  1163.069   163.069  5497.956  4497.956 #> 2   200.0786     0.000     0.000  3534.965  2534.965 #> 3     0.0000     0.000     0.000  2998.196  1998.196 #> 4 15548.3275 14548.328 13548.328 18883.214 17883.214 #> 5 14766.0077 13766.008 12766.008 18100.894 17100.894  ## Look at downstream only stream distances among prediction ## sites in pred1km on netID == 1. This is useful for block ## prediction dist_pred1km$dist.net1[1:5, 1:5] #>      46   47 48       49       50 #> 46    0    0  0 3334.887 2334.887 #> 47 1000    0  0 4334.887 3334.887 #> 48 2000 1000  0 5334.887 4334.887 #> 49    0    0  0    0.000    0.000 #> 50    0    0  0 1000.000    0.000"},{"path":"/reference/ssn_glm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"function works spatial stream network objects fit generalized linear models spatially autocorrelated errors using likelihood methods, allowing non-spatial random effects, anisotropy, partition factors, big data methods, . spatial formulation described Ver Hoef Peterson (2010) Peterson Ver Hoef (2010).","code":""},{"path":"/reference/ssn_glm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"","code":"ssn_glm(   formula,   ssn.object,   family,   tailup_type = \"none\",   taildown_type = \"none\",   euclid_type = \"none\",   nugget_type = \"nugget\",   tailup_initial,   taildown_initial,   euclid_initial,   nugget_initial,   dispersion_initial,   additive,   estmethod = \"reml\",   anisotropy = FALSE,   random,   randcov_initial,   partition_factor,   ... )"},{"path":"/reference/ssn_glm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"formula two-sided linear formula describing fixed effect structure model, response left ~ operator terms right, separated + operators. ssn.object spatial stream network object class SSN. family generalized linear model family use ssn_glm(). Available options include \"Gaussian\", \"poisson\", \"nbinomial\" (negative binomial), \"binomial\", \"beta\", \"Gamma\", \"invgauss\". family \"Gaussian\", arguments passed evaluated ssn_lm(). Can quoted unquoted. Note family argument takes single value, rather list structure used stats::glm. See Details . tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". Parameterizations described Details. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". Parameterizations described Details. tailup_initial object tailup_initial() specifying initial /known values tailup covariance parameters. taildown_initial object taildown_initial() specifying initial /known values taildown covariance parameters. euclid_initial object euclid_initial() specifying initial /known values euclidean covariance parameters. nugget_initial object nugget_initial() specifying initial /known values nugget covariance parameters. dispersion_initial object dispersion_initial() specifying initial /known values tailup covariance parameters. additive name variable ssn.object used define spatial weights. Can quoted unquoted. tailup covariance functions, additive weights used branching. Technical details describe role additive variable tailup covariance function available Ver Hoef Peterson (2010). estmethod estimation method. Available options include \"reml\" restricted maximum likelihood \"ml\" maximum likelihood. default \"reml\". anisotropy logical indicating whether (geometric) anisotropy modeled. required spcov_initial provided 1) rotate assumed unknown assumed known non-zero 2) scale assumed unknown assumed known less one. anisotropy TRUE, computational times can significantly increase. default FALSE. random one-sided linear formula describing random effect structure model. Terms specified right ~ operator. term structure x1 + ... + xn | g1/.../gm, x1 + ... + xn specifies model random effects g1/.../gm grouping structure. Separate terms separated + must generally wrapped parentheses. Random intercepts added model implicitly least  one variable defined. random intercept desired, must explicitly defined (e.g., x1 + ... + xn - 1 | g1/.../gm). random intercept desired grouping structure, random intercept must specified 1 | g1/.../gm. Note g1/.../gm shorthand (1 | g1/.../gm). random intercepts desired shorthand notation used, parentheses can omitted. randcov_initial optional object specifying initial /known values random effect variances. See spmodel::randcov_initial(). partition_factor one-sided linear formula single term specifying partition factor.  partition factor assumes observations different levels partition factor uncorrelated. ... arguments stats::optim().","code":""},{"path":"/reference/ssn_glm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"list many elements store information fitted model object class ssn_glm. Many generic functions summarize model fit available ssn_glm objects, including AIC, AICc, anova, augment, coef, cooks.distance, covmatrix, deviance, fitted, formula, glance, glances, hatvalues, influence, labels, logLik, loocv, model.frame, model.matrix, plot, predict, print, pseudoR2, summary, terms, tidy, update, varcomp, vcov. fitted model list contains following elements: additive: name additive function value column. anisotropy: Whether euclidean anisotropy modeled. call: function call. coefficients: Model coefficients. contrasts: user-supplied contrasts. cooks_distance: Cook's distance values. crs: geographic coordinate reference system. deviance: model deviance. diagtol: tolerance value may added diagonal  ovariance matrices encourage decomposition stability. estmethod: estimation method. euclid_max: maximum euclidean distance. family: generalized linear model family fitted: Fitted values. formula: model formula. hatvalues: hat (leverage) values. is_known: object identifies parameters known. local_index: index identifier used internally sorting. missing_index: rows \"obs\" object missing responses. n: sample size. npar: number estimated covariance parameters. observed_index: rows \"obs\" object observed responses. optim: optimization output. p: number fixed effects. partition_factor: partition factor formula. pseudoR2: pseudo R-squared. random: random effect formula. residuals: residuals. sf_column_name: name geometry columns ssn.object size: size binomial trials relevant. ssn.object: updated ssn.object. tail_max: maximum stream distance. terms: model terms. vcov: Variance-covariance matrices xlevels: levels factors model matrix. y: response. list elements meant used various generic functions (e.g., residuals() operate model object. possible access elements fitted model list directly, strongly advise generic available return element interest. example, strongly recommend using residuals() obtain model residuals instead accessing fitted model list directly via object$residuals.","code":""},{"path":"/reference/ssn_glm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"generalized linear model spatial stream networks can written \\(g(\\mu) = \\eta = X \\beta + zu + zd + ze + n\\), \\(\\mu\\) expectation response given random errors, \\(y\\), \\(g()\\) function links mean \\(\\eta\\) (called link function), X fixed effects design matrix, \\(\\beta\\) fixed effects, \\(zu\\) tailup random error, \\(zd\\) taildown random error, \\(ze\\) Euclidean random error, \\(n\\) nugget random error. six generalized linear model families available: poisson assumes \\(y\\) Poisson random variable nbinomial assumes \\(y\\) negative binomial random variable, binomial assumes \\(y\\) binomial random variable, beta assumes \\(y\\) beta random variable, Gamma assumes \\(y\\) gamma random variable, inverse.gaussian assumes \\(y\\) inverse Gaussian random variable. supports \\(y\\) family given : family: support \\(y\\) Gaussian: \\(-\\infty < y < \\infty\\) poisson: \\(0 \\le y\\); \\(y\\) integer nbinomial: \\(0 \\le y\\); \\(y\\) integer binomial: \\(0 \\le y\\); \\(y\\) integer beta: \\(0 < y < 1\\) Gamma: \\(0 < y\\) inverse.gaussian: \\(0 < y\\) generalized linear model families parameterizations link functions given : family: link function Gaussian: \\(g(\\mu) = \\eta\\) (identity link) poisson: \\(g(\\mu) = log(\\eta)\\) (log link) nbinomial: \\(g(\\mu) = log(\\eta)\\) (log link) binomial: \\(g(\\mu) = log(\\eta / (1 - \\eta))\\) (logit link) beta: \\(g(\\mu) = log(\\eta / (1 - \\eta))\\) (logit link) Gamma: \\(g(\\mu) = log(\\eta)\\) (log link) inverse.gaussian: \\(g(\\mu) = log(\\eta)\\) (log link) variance function individual \\(y\\) (given \\(\\mu\\)) generalized linear model family given : family: \\(Var(y)\\) Gaussian: \\(\\sigma^2\\) poisson: \\(\\mu \\phi\\) nbinomial: \\(\\mu + \\mu^2 / \\phi\\) binomial: \\(n \\mu (1 - \\mu) \\phi\\) beta: \\(\\mu (1 - \\mu) / (1 + \\phi)\\) Gamma: \\(\\mu^2 / \\phi\\) inverse.gaussian: \\(\\mu^2 / \\phi\\) parameter \\(\\phi\\) dispersion parameter influences \\(Var(y)\\). poisson binomial families, \\(\\phi\\) always one. Note inverse Gaussian parameterization different standard inverse Gaussian parameterization, variance \\(\\mu^3 / \\lambda\\). Setting \\(\\phi = \\lambda / \\mu\\) yields parameterization, preferred computational stability. Also note dispersion parameter often defined literature \\(V(\\mu) \\phi\\), \\(V(\\mu)\\) variance function mean. use parameterization, important recognize interpreting dispersion estimates. generalized linear model constructions, see McCullagh Nelder (1989). generalized linear model context, tailup, taildown, Euclidean, nugget covariance affect modeled mean observation (conditional effects). link scale, tailup random errors capture spatial covariance moving downstream (depend downstream distance), taildown random errors capture spatial covariance moving upstream (depend upstream) distance, Euclidean random errors capture spatial covariance depends Euclidean distance, nugget random errors captures variability independent spatial locations. \\(\\eta\\) modeled using spatial covariance function expressed \\(de(zu) * R(zu) + de(zd) * R(zd) + de(ze) * R(ze) + nugget * \\). \\(de(zu)\\), \\(de(zu)\\), \\(de(zd)\\) represent tailup, taildown, Euclidean variances, respectively. \\(R(zu)\\), \\(R(zd)\\), \\(R(ze)\\) represent tailup, taildown, Euclidean correlation matrices, respectively. correlation matrix depends range parameter controls distance-decay behavior correlation. \\(nugget\\) represents nugget variance \\(\\) represents identity matrix. tailup_type Details: Let \\(D\\) matrix hydrologic distances, \\(W\\) diagonal matrix weights additive, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zu)\\) given : linear: \\((1 - r) * (r <= 1) * W\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1) * W\\) exponential: \\(exp(-r) * W\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0) * W\\) epa: \\((D - range)^2 * F * (r <= 1) * W / 16range^5\\) none: \\(\\) * W Details describing F matrix epa covariance given Garreta et al. (2010). Flow-unconnected elements \\(R(zu)\\) assumed uncorrelated. Observations different networks also assumed uncorrelated. taildown_type Details: Let \\(D\\) matrix hydrologic distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zd)\\) given : linear: \\((1 - r) * (r <= 1)\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) exponential: \\(exp(-r)\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0)\\) epa: \\((D - range)^2 * F1 * (r <= 1) / 16range^5\\) none: \\(\\) Now let \\(\\) matrix contains shorter two distances two sites common downstream junction, \\(r1 = / range\\), \\(B\\) matrix contains longer two distances two sites common downstream junction, \\(r2 = B / range\\),  \\(\\) identity matrix. parametric forms flow-unconnected elements \\(R(zd)\\) given : linear: \\((1 - r2) * (r2 <= 1)\\) spherical: \\((1 - 1.5r1 + 0.5r2) * (1 - r2)^2 * (r2 <= 1)\\) exponential: \\(exp(-(r1 + r2))\\) mariah: \\((log(90r1 + 1) - log(90r2 + 1)) / (90r1 - 90r2) * (=/ B) + (1 / (90r1 + 1)) * (= B)\\) epa: \\((B - range)^2 * F2 * (r2 <= 1) / 16range^5\\) none: \\(\\) Details describing F1 F2 matrices epa covariance given Garreta et al. (2010). Observations different networks assumed uncorrelated. euclid_type Details: Let \\(D\\) matrix Euclidean distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms elements \\(R(ze)\\) given : exponential: \\(exp(- r )\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) gaussian: \\(exp(- r^2 )\\) cubic: \\((1 - 7r^2 + 8.75r^3 - 3.5r^5 + 0.75r^7) * (r <= 1)\\) pentaspherical: \\((1 - 1.875r + 1.25r^3 - 0.375r^5) * (r <= 1)\\) cosine: \\(cos(r)\\) wave: \\(sin(r) * (h > 0) / r + (h = 0)\\) jbessel: \\(Bj(h * range)\\), Bj Bessel-J function gravity: \\((1 + r^2)^{-0.5}\\) rquad: \\((1 + r^2)^{-1}\\) magnetic: \\((1 + r^2)^{-1.5}\\) none: \\(\\) nugget_type Details: Let \\(\\) identity matrix \\(0\\) zero matrix. parametric forms elements nugget variance given : nugget: \\(\\) none: \\(0\\) short, nugget effect modeled nugget_type \"nugget\" omitted nugget_type \"none\". estmethod Details: various estimation methods reml: Maximize restricted log-likelihood. ml: Maximize log-likelihood. anisotropy Details: default, Euclidean covariance parameters except rotate scale assumed unknown, requiring estimation. either rotate scale given initial values 0 1 (respectively) assumed unknown euclid_initial(), anisotropy implicitly set TRUE. (Geometric) Anisotropy modeled transforming Euclidean covariance function decays differently different directions one decays equally directions via rotation scaling original Euclidean coordinates. rotation controlled rotate parameter \\([0, \\pi]\\) radians. scaling controlled scale parameter \\([0, 1]\\). anisotropy correction involves first rotation coordinates clockwise rotate scaling coordinates' minor axis reciprocal scale. Euclidean covariance computed using transformed coordinates. random Details: random effects used (estimation method must \"reml\" \"ml\"), model can written \\(g(\\mu) = \\eta = X \\beta + W1\\gamma 1 + ... Wj\\gamma j + zu + zd + ze + n\\), Z random effects design matrix u random effect. partition_factor Details: partition factor can represented matrix form \\(P\\), elements \\(P\\) equal one observations level partition factor zero otherwise. covariance matrix involving spatial random effects components multiplied element-wise (Hadmard product) \\(P\\), yielding final covariance matrix. Details: Observations NA response values removed model fitting, values can predicted afterwards running predict(object).","code":""},{"path":"/reference/ssn_glm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"function perform internal scaling. optimization stable due large extremely large variances, scale relevant variables variance 1 optimization.","code":""},{"path":"/reference/ssn_glm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"Garreta, V., Monestiez, P. Ver Hoef, J.M. (2010) Spatial modelling prediction river networks: model, model, hybrid? Environmetrics 21(5), 439–456. McCullagh P. Nelder, J. . (1989) Generalized Linear Models. London: Chapman Hall. Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644–651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6–18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.","code":""},{"path":"/reference/ssn_glm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_gmod <- ssn_glm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   family = \"Gamma\",   tailup_type = \"exponential\",   additive = \"afvArea\" ) summary(ssn_gmod) #>  #> Call: #> ssn_glm(formula = Summer_mn ~ ELEV_DEM, ssn.object = mf04p, family = \"Gamma\",  #>     tailup_type = \"exponential\", additive = \"afvArea\") #>  #> Deviance Residuals: #>        Min         1Q     Median         3Q        Max  #> -3.296e-03 -9.345e-04  4.352e-05  4.850e-04  5.457e-03  #>  #> Coefficients (fixed): #>               Estimate Std. Error z value Pr(>|z|)     #> (Intercept)  8.4238415  0.7860602  10.717  < 2e-16 *** #> ELEV_DEM    -0.0029545  0.0003907  -7.563 3.95e-14 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.606 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  1.058e-02 #>   tailup exponential         range  9.184e+04 #>               nugget        nugget  2.898e-04 #>           dispersion    dispersion  2.328e+04 #>"},{"path":"/reference/ssn_import.html","id":null,"dir":"Reference","previous_headings":"","what":"Import SSN object — ssn_import","title":"Import SSN object — ssn_import","text":"function reads spatial data .ssn folder creates SSN object.","code":""},{"path":"/reference/ssn_import.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import SSN object — ssn_import","text":"","code":"ssn_import(path, include_obs = TRUE, predpts = NULL, overwrite = FALSE)"},{"path":"/reference/ssn_import.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import SSN object — ssn_import","text":"path Filepath .ssn directory. See details. include_obs default = TRUE. Logical indicating whether observed sites included SSN object. predpts Vector prediction site dataset names found within .ssn folder. See details. overwrite default = FALSE. TRUE, overwrite existing binaryID.db files netgeom column(s) exists edges, observed sites (include_obs = TRUE), prediction site datasets (exist).","code":""},{"path":"/reference/ssn_import.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import SSN object — ssn_import","text":"ssn_import returns object class SSN, list four elements containing: edges: sf data.frame containing stream network, additional 'netgeom' column. obs: sf data.frame containing observed site locations, additional 'netgeom' column. NA include_obs =     FALSE. preds: list sf data.frames containing prediction site locations. empty list returned predpts provided. path: local filepath .ssn directory associated SSN object.","code":""},{"path":"/reference/ssn_import.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import SSN object — ssn_import","text":"ssn_import function imports spatial data (shapefile GeoPackage format) .ssn folder generated using SSNbler package function SSNbler::lsn_to_ssn. .ssn folder contains spatial, topological attribute data needed fit spatial statistical stream network model streams data.  includes: edges dataset LINESTRING geometry representing stream network. sites dataset POINT geometry observed data collected stream network. Prediction sites dataset(s) representing locations predictions made. netID.dat file(s) distinct network, stores topological relationships line features edges. detailed description .ssn directory contents provided Peterson Ver Hoef (2014). ssn_import imports edges, observed sites (optional), prediction sites (optional) sf data.frame objects. new column named 'netgeom' created store important data representing topological relationships spatial stream network model. data stored character format, less likely inadvertantly changed users. See create_netgeom detailed description format contents 'netgeom'. information contained netID text files imported SQLite database, binaryID.db, stored .ssn directory. information used internally ssn_create_distmat, ssn_lm ssn_glm calculate data necessary fit spatial statistical model stream network data. overwrite = TRUE (overwrite = FALSE default) binaryID.db file already exists within .ssn directory, overwriten SSN object created. minimum, SSN object must always contain streams, referred edges. SSN object also typically contain set observed sites, measurements collected. one observed dataset permitted SSN object. include_obs=FALSE, SSN object created without observations. option provides flexibility users like simulate data set artifical sites existing stream network. Note observation sites must included SSN object order fit models using ssn_lm ssn_glm. SSN object may contain multiple sets prediction points (none), stored separate datasets .ssn directory. predpts named vector, names preds list SSN object correspond vector names. Otherwise, set basename prediction site dataset file(s) specified predpts. ssn_import_predpts function allows users import additional sets prediction sites existing SSN object.","code":""},{"path":"/reference/ssn_import.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Import SSN object — ssn_import","text":"Peterson, E., Ver Hoef, J.M. (2014) STARS: ArcGIS toolset used calculate spatial information needed fit spatial statistical stream network models stream network data. Journal Statistical Software 56(2), 1–17.","code":""},{"path":"/reference/ssn_import.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import SSN object — ssn_import","text":"","code":"## Create local temporary copy of MiddleFork04.ssn found in # SSN2/lsndata folder. Only necessary for this example. copy_lsn_to_temp()  ## Import SSN object with no prediction sites mf04 <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   overwrite = TRUE )  ## Import SSN object with 3 sets of prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(     \"pred1km\",     \"CapeHorn\"   ),   overwrite = TRUE )"},{"path":"/reference/ssn_import_predpts.html","id":null,"dir":"Reference","previous_headings":"","what":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"shapefile prediction points found .ssn directory imported existing object class SSN, ssn_lm, ssn_glm.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"","code":"ssn_import_predpts(x, predpts)"},{"path":"/reference/ssn_import_predpts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"x object classSSN, ssn_lm, ssn_glm. predpts Name prediction point dataset import character format. See details.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"object class SSN, ssn_lm, ssn_glm contains new prediction dataset.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"ssn_import_predpts imports one set prediction points residing .ssn directory existing SSN, ssn_lm, ssn_glm object. prediction dataset must shapefile geopackage format (.shp .gpkg, respectively) reside ssn.object$path directory. path SSN object can updated using ssn_update_path() prior importing prediction datasets. argument predpts accepts name prediction point dataset, without file extension. passed named vector (length 1), name provided used prediction dataset name SSN object prediction sites list (e.g. names(ssn.obj$preds)). Otherwise, file basename used names attribute. See ssn_import detailed description prediction dataset format within SSN class object. prediction dataset imported, new column named netgeom created. column already exists overwritten. Please see create_netgeom detailed description netgeom column information contains. prediction dataset specified predpts must contain spatial, topological attribute information needed make predictions using ssn_lm ssn_glm object. information generated using SSNbler package, makes use functionality found sf igraph packages process streams data vector format.","code":""},{"path":"/reference/ssn_import_predpts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts","text":"","code":"## Create local temporary copy of MiddleFork04.ssn found in # SSN2/lsndata folder. Only necessary for this example. copy_lsn_to_temp()  ## Import SSN object with no prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   overwrite = TRUE )  ## Import pred1km prediction dataset into SSN object and assign the ## name preds1 mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),     overwrite = TRUE) mf04p <- ssn_import_predpts(mf04p, predpts = c(preds1 = \"pred1km\")) names(mf04p$preds) #> [1] \"preds1\"  ## Import CapeHorn prediction dataset into a ssn_glm object, using ## the default file basename as the name ssn_gmod <- ssn_glm(Summer_mn ~ netID, mf04p,   family = \"Gamma\",   tailup_type = \"exponential\", additive = \"afvArea\" ) ssn_gmod <- ssn_import_predpts(ssn_gmod, predpts = \"CapeHorn\") names(ssn_gmod$ssn.object$preds) #> [1] \"preds1\"   \"CapeHorn\""},{"path":"/reference/ssn_initial.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a covariance parameter initial object — ssn_initial","title":"Create a covariance parameter initial object — ssn_initial","text":"Create covariance parameter initial object specifies initial /known values use estimating specific covariance parameters ssn_lm() ssn_glm().  See spmodel::randcov_initial() documentation regarding random effect covariance parameter initial objects.","code":""},{"path":"/reference/ssn_initial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a covariance parameter initial object — ssn_initial","text":"","code":"tailup_initial(tailup_type, de, range, known)  taildown_initial(taildown_type, de, range, known)  euclid_initial(euclid_type, de, range, rotate, scale, known)  nugget_initial(nugget_type, nugget, known)"},{"path":"/reference/ssn_initial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a covariance parameter initial object — ssn_initial","text":"tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". de spatially dependent (correlated) random error variance. Commonly referred partial sill. range correlation parameter. known character vector indicating covariance parameters assumed known. value \"given\" shorthand assuming covariance parameters given *_initial() assumed known. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". rotate Anisotropy rotation parameter (0 \\(\\pi\\) radians) euclidean portion covariance. value 0 (default) implies rotation. scale Anisotropy scale parameter (0 1) euclidean portion covariance. value 1 (default) implies scaling. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". nugget spatially independent (correlated) random error variance. Commonly referred nugget.","code":""},{"path":"/reference/ssn_initial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a covariance parameter initial object — ssn_initial","text":"list two elements: initial is_known. initial named numeric vector indicating spatial covariance parameters specified initial /known values. is_known named numeric vector indicating whether spatial covariance parameters initial known . class list matches relevant spatial covariance type.","code":""},{"path":"/reference/ssn_initial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a covariance parameter initial object — ssn_initial","text":"Create initial object use ssn_lm() ssn_glm(). NA values can given ie, rotate, scale, lets functions find initial values parameters sometimes otherwise assumed known (e.g., rotate scale ssn_lm() ssn_glm(). Parametric forms spatial covariance type presented . tailup_type Details: Let \\(D\\) matrix hydrologic distances, \\(W\\) diagonal matrix weights additive, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zu)\\) given : linear: \\((1 - r) * (r <= 1) * W\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1) * W\\) exponential: \\(exp(-r) * W\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0) * W\\) epa: \\((D - range)^2 * F * (r <= 1) * W / 16range^5\\) none: \\(\\) * W Details describing F matrix epa covariance given Garreta et al. (2010). Flow-unconnected elements \\(R(zu)\\) assumed uncorrelated. Observations different networks also assumed uncorrelated. taildown_type Details: Let \\(D\\) matrix hydrologic distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zd)\\) given : linear: \\((1 - r) * (r <= 1)\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) exponential: \\(exp(-r)\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0)\\) epa: \\((D - range)^2 * F1 * (r <= 1) / 16range^5\\) none: \\(\\) Now let \\(\\) matrix contains shorter two distances two sites common downstream junction, \\(r1 = / range\\), \\(B\\) matrix contains longer two distances two sites common downstream junction, \\(r2 = B / range\\),  \\(\\) identity matrix. parametric forms flow-unconnected elements \\(R(zd)\\) given : linear: \\((1 - r2) * (r2 <= 1)\\) spherical: \\((1 - 1.5r1 + 0.5r2) * (1 - r2)^2 * (r2 <= 1)\\) exponential: \\(exp(-(r1 + r2))\\) mariah: \\((log(90r1 + 1) - log(90r2 + 1)) / (90r1 - 90r2) * (=/ B) + (1 / (90r1 + 1)) * (= B)\\) epa: \\((B - range)^2 * F2 * (r2 <= 1) / 16range^5\\) none: \\(\\) Details describing F1 F2 matrices epa covariance given Garreta et al. (2010). Observations different networks assumed uncorrelated. euclid_type Details: Let \\(D\\) matrix Euclidean distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms elements \\(R(ze)\\) given : exponential: \\(exp(- r )\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) gaussian: \\(exp(- r^2 )\\) cubic: \\((1 - 7r^2 + 8.75r^3 - 3.5r^5 + 0.75r^7) * (r <= 1)\\) pentaspherical: \\((1 - 1.875r + 1.25r^3 - 0.375r^5) * (r <= 1)\\) cosine: \\(cos(r)\\) wave: \\(sin(r) * (h > 0) / r + (h = 0)\\) jbessel: \\(Bj(h * range)\\), Bj Bessel-J function gravity: \\((1 + r^2)^{-0.5}\\) rquad: \\((1 + r^2)^{-1}\\) magnetic: \\((1 + r^2)^{-1.5}\\) none: \\(\\) nugget_type Details: Let \\(\\) identity matrix \\(0\\) zero matrix. parametric forms elements nugget variance given : nugget: \\(\\) none: \\(0\\) short, nugget effect modeled nugget_type \"nugget\" omitted nugget_type \"none\". Dispersion random effect initial objects specified via spmodel::dispersion_initial() spmodel::randcov_initial(), respectively.","code":""},{"path":"/reference/ssn_initial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create a covariance parameter initial object — ssn_initial","text":"Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644–651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6–18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.","code":""},{"path":"/reference/ssn_initial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a covariance parameter initial object — ssn_initial","text":"","code":"tailup_initial(\"exponential\", de = 1, range = 20, known = \"range\") #> $initial #>    de range  #>     1    20  #>  #> $is_known #>    de range  #> FALSE  TRUE  #>  #> attr(,\"class\") #> [1] \"tailup_exponential\" tailup_initial(\"exponential\", de = 1, range = 20, known = \"given\") #> $initial #>    de range  #>     1    20  #>  #> $is_known #>    de range  #>  TRUE  TRUE  #>  #> attr(,\"class\") #> [1] \"tailup_exponential\" euclid_initial(\"spherical\", de = 2, range = 4, scale = 0.8, known = c(\"range\", \"scale\")) #> $initial #>    de range scale  #>   2.0   4.0   0.8  #>  #> $is_known #>    de range scale  #> FALSE  TRUE  TRUE  #>  #> attr(,\"class\") #> [1] \"euclid_spherical\" dispersion_initial(\"nbinomial\", dispersion = 5) #> $initial #> dispersion  #>          5  #>  #> $is_known #> dispersion  #>      FALSE  #>  #> attr(,\"class\") #> [1] \"nbinomial\""},{"path":"/reference/ssn_lm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"function works spatial stream network objects fit linear models spatially autocorrelated errors using likelihood methods, allowing non-spatial random effects, anisotropy, partition factors, big data methods, . spatial formulation described Ver Hoef Peterson (2010) Peterson Ver Hoef (2010).","code":""},{"path":"/reference/ssn_lm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"","code":"ssn_lm(   formula,   ssn.object,   tailup_type = \"none\",   taildown_type = \"none\",   euclid_type = \"none\",   nugget_type = \"nugget\",   tailup_initial,   taildown_initial,   euclid_initial,   nugget_initial,   additive,   estmethod = \"reml\",   anisotropy = FALSE,   random,   randcov_initial,   partition_factor,   ... )"},{"path":"/reference/ssn_lm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"formula two-sided linear formula describing fixed effect structure model, response left ~ operator terms right, separated + operators. ssn.object spatial stream network object class SSN. tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". Parameterizations described Details. euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". Parameterizations described Details. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". Parameterizations described Details. tailup_initial object tailup_initial() specifying initial /known values tailup covariance parameters. taildown_initial object taildown_initial() specifying initial /known values taildown covariance parameters. euclid_initial object euclid_initial() specifying initial /known values euclidean covariance parameters. nugget_initial object nugget_initial() specifying initial /known values nugget covariance parameters. additive name variable ssn.object used define spatial weights. Can quoted unquoted. tailup covariance functions, additive weights used branching. Technical details describe role additive variable tailup covariance function available Ver Hoef Peterson (2010). estmethod estimation method. Available options include \"reml\" restricted maximum likelihood \"ml\" maximum likelihood. default \"reml\". anisotropy logical indicating whether (geometric) anisotropy modeled. required spcov_initial provided 1) rotate assumed unknown assumed known non-zero 2) scale assumed unknown assumed known less one. anisotropy TRUE, computational times can significantly increase. default FALSE. random one-sided linear formula describing random effect structure model. Terms specified right ~ operator. term structure x1 + ... + xn | g1/.../gm, x1 + ... + xn specifies model random effects g1/.../gm grouping structure. Separate terms separated + must generally wrapped parentheses. Random intercepts added model implicitly least  one variable defined. random intercept desired, must explicitly defined (e.g., x1 + ... + xn - 1 | g1/.../gm). random intercept desired grouping structure, random intercept must specified 1 | g1/.../gm. Note g1/.../gm shorthand (1 | g1/.../gm). random intercepts desired shorthand notation used, parentheses can omitted. randcov_initial optional object specifying initial /known values random effect variances. See spmodel::randcov_initial(). partition_factor one-sided linear formula single term specifying partition factor.  partition factor assumes observations different levels partition factor uncorrelated. ... arguments stats::optim().","code":""},{"path":"/reference/ssn_lm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"list many elements store information fitted model object class ssn_lm. Many generic functions summarize model fit available ssn_lm objects, including AIC, AICc, anova, augment, coef, cooks.distance, covmatrix, deviance, fitted, formula, glance, glances, hatvalues, influence, labels, logLik, loocv, model.frame, model.matrix, plot, predict, print, pseudoR2, summary, terms, tidy, update, varcomp, vcov. fitted model list contains following elements: additive: name additive function value column. anisotropy: Whether euclidean anisotropy modeled. call: function call. coefficients: Model coefficients. contrasts: user-supplied contrasts. cooks_distance: Cook's distance values. crs: geographic coordinate reference system. deviance: model deviance. diagtol: tolerance value may added diagonal  ovariance matrices encourage decomposition stability. estmethod: estimation method. euclid_max: maximum euclidean distance. fitted: Fitted values. formula: model formula. hatvalues: hat (leverage) values. is_known: object identifies parameters known. local_index: index identifier used internally sorting. missing_index: rows \"obs\" object missing responses. n: sample size. npar: number estimated covariance parameters. observed_index: rows \"obs\" object observed responses. optim: optimization output. p: number fixed effects. partition_factor: partition factor formula. pseudoR2: pseudo R-squared. random: random effect formula. residuals: residuals. sf_column_name: name geometry columns ssn.object ssn.object: updated ssn.object. tail_max: maximum stream distance. terms: model terms. vcov: Variance-covariance matrices xlevels: levels factors model matrix. list elements meant used various generic functions (e.g., residuals() operate model object. possible access elements fitted model list directly, strongly advise generic available return element interest. example, strongly recommend using residuals() obtain model residuals instead accessing fitted model list directly via object$residuals.","code":""},{"path":"/reference/ssn_lm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"linear model spatial stream networks can written \\(y = X \\beta + zu + zd + ze + n\\), \\(X\\) fixed effects design matrix, \\(\\beta\\) fixed effects, \\(zu\\) tailup random error, \\(zd\\) taildown random error, \\(ze\\) Euclidean random error, \\(n\\) nugget random error. tailup random errors capture spatial covariance moving downstream (depend downstream distance), taildown random errors capture spatial covariance moving upstream (depend upstream) distance, Euclidean random errors capture spatial covariance depends Euclidean distance, nugget random errors captures variability independent spatial locations. response \\(y\\) modeled using spatial covariance function expressed \\(de(zu) * R(zu) + de(zd) * R(zd) + de(ze) * R(ze) + nugget * \\). \\(de(zu)\\), \\(de(zu)\\), \\(de(zd)\\) represent tailup, taildown, Euclidean variances, respectively. \\(R(zu)\\), \\(R(zd)\\), \\(R(ze)\\) represent tailup, taildown, Euclidean correlation matrices, respectively. correlation matrix depends range parameter controls distance-decay behavior correlation. \\(nugget\\) represents nugget variance \\(\\) represents identity matrix. tailup_type Details: Let \\(D\\) matrix hydrologic distances, \\(W\\) diagonal matrix weights additive, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zu)\\) given : linear: \\((1 - r) * (r <= 1) * W\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1) * W\\) exponential: \\(exp(-r) * W\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0) * W\\) epa: \\((D - range)^2 * F * (r <= 1) * W / 16range^5\\) none: \\(\\) * W Details describing F matrix epa covariance given Garreta et al. (2010). Flow-unconnected elements \\(R(zu)\\) assumed uncorrelated. Observations different networks also assumed uncorrelated. taildown_type Details: Let \\(D\\) matrix hydrologic distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms flow-connected elements \\(R(zd)\\) given : linear: \\((1 - r) * (r <= 1)\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) exponential: \\(exp(-r)\\) mariah: \\(log(90r + 1) / 90r * (D > 0) + 1 * (D = 0)\\) epa: \\((D - range)^2 * F1 * (r <= 1) / 16range^5\\) none: \\(\\) Now let \\(\\) matrix contains shorter two distances two sites common downstream junction, \\(r1 = / range\\), \\(B\\) matrix contains longer two distances two sites common downstream junction, \\(r2 = B / range\\),  \\(\\) identity matrix. parametric forms flow-unconnected elements \\(R(zd)\\) given : linear: \\((1 - r2) * (r2 <= 1)\\) spherical: \\((1 - 1.5r1 + 0.5r2) * (1 - r2)^2 * (r2 <= 1)\\) exponential: \\(exp(-(r1 + r2))\\) mariah: \\((log(90r1 + 1) - log(90r2 + 1)) / (90r1 - 90r2) * (=/ B) + (1 / (90r1 + 1)) * (= B)\\) epa: \\((B - range)^2 * F2 * (r2 <= 1) / 16range^5\\) none: \\(\\) Details describing F1 F2 matrices epa covariance given Garreta et al. (2010). Observations different networks assumed uncorrelated. euclid_type Details: Let \\(D\\) matrix Euclidean distances, \\(r = D / range\\), \\(\\) identity matrix. parametric forms elements \\(R(ze)\\) given : exponential: \\(exp(- r )\\) spherical: \\((1 - 1.5r + 0.5r^3) * (r <= 1)\\) gaussian: \\(exp(- r^2 )\\) cubic: \\((1 - 7r^2 + 8.75r^3 - 3.5r^5 + 0.75r^7) * (r <= 1)\\) pentaspherical: \\((1 - 1.875r + 1.25r^3 - 0.375r^5) * (r <= 1)\\) cosine: \\(cos(r)\\) wave: \\(sin(r) * (h > 0) / r + (h = 0)\\) jbessel: \\(Bj(h * range)\\), Bj Bessel-J function gravity: \\((1 + r^2)^{-0.5}\\) rquad: \\((1 + r^2)^{-1}\\) magnetic: \\((1 + r^2)^{-1.5}\\) none: \\(\\) nugget_type Details: Let \\(\\) identity matrix \\(0\\) zero matrix. parametric forms elements nugget variance given : nugget: \\(\\) none: \\(0\\) short, nugget effect modeled nugget_type \"nugget\" omitted nugget_type \"none\". estmethod Details: various estimation methods reml: Maximize restricted log-likelihood. ml: Maximize log-likelihood. anisotropy Details: default, Euclidean covariance parameters except rotate scale assumed unknown, requiring estimation. either rotate scale given initial values 0 1 (respectively) assumed unknown euclid_initial(), anisotropy implicitly set TRUE. (Geometric) Anisotropy modeled transforming Euclidean covariance function decays differently different directions one decays equally directions via rotation scaling original Euclidean coordinates. rotation controlled rotate parameter \\([0, \\pi]\\) radians. scaling controlled scale parameter \\([0, 1]\\). anisotropy correction involves first rotation coordinates clockwise rotate scaling coordinates' minor axis reciprocal scale. Euclidean covariance computed using transformed coordinates. random Details: random effects used, model can written \\(y = X \\beta + W1\\gamma 1 + ... Wj\\gamma j + zu + zd + ze + n\\), Z random effects design matrix u random effect. partition_factor Details: partition factor can represented matrix form \\(P\\), elements \\(P\\) equal one observations level partition factor zero otherwise. covariance matrix involving spatial random effects components multiplied element-wise (Hadmard product) \\(P\\), yielding final covariance matrix. Details: Observations NA response values removed model fitting, values can predicted afterwards running predict(object).","code":""},{"path":"/reference/ssn_lm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"function perform internal scaling. optimization stable due large extremely large variances, scale relevant variables variance 1 optimization.","code":""},{"path":"/reference/ssn_lm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"Garreta, V., Monestiez, P. Ver Hoef, J.M. (2010) Spatial modelling prediction river networks: model, model, hybrid? Environmetrics 21(5), 439–456. Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644–651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6–18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.","code":""},{"path":"/reference/ssn_lm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting Linear Models for Spatial Stream Networks — ssn_lm","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) summary(ssn_mod) #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM, ssn.object = mf04p, tailup_type = \"exponential\",  #>     additive = \"afvArea\") #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -2.6986 -1.4031  0.2287  0.6796  2.2456  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 80.857837   8.814114   9.174  < 2e-16 *** #> ELEV_DEM    -0.034124   0.004376  -7.797 6.22e-15 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.5855 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  1.390e+00 #>   tailup exponential         range  1.306e+05 #>               nugget        nugget  5.415e-02 #>"},{"path":"/reference/ssn_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Return names of data in an SSN object — ssn_names","title":"Return names of data in an SSN object — ssn_names","text":"Extract print names edges, sites preds elements SSN object.","code":""},{"path":"/reference/ssn_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return names of data in an SSN object — ssn_names","text":"","code":"ssn_names(ssn.object)"},{"path":"/reference/ssn_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return names of data in an SSN object — ssn_names","text":"ssn.object SSN object.","code":""},{"path":"/reference/ssn_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return names of data in an SSN object — ssn_names","text":"Print variable names console","code":""},{"path":"/reference/ssn_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create covariance parameter objects. — ssn_params","title":"Create covariance parameter objects. — ssn_params","text":"Create covariance parameter object us functions. See spmodel::randcov_params() documentation regarding random effect covariance parameter objects.","code":""},{"path":"/reference/ssn_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create covariance parameter objects. — ssn_params","text":"","code":"tailup_params(tailup_type, de, range)  taildown_params(taildown_type, de, range)  euclid_params(euclid_type, de, range, rotate, scale)  nugget_params(nugget_type, nugget)"},{"path":"/reference/ssn_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create covariance parameter objects. — ssn_params","text":"tailup_type tailup covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". de spatially dependent (correlated) random error variance. Commonly referred partial sill. range correlation parameter. taildown_type taildown covariance function type. Available options include \"linear\", \"spherical\", \"exponential\", \"mariah\", \"epa\", \"none\". euclid_type euclidean covariance function type. Available options include \"spherical\", \"exponential\", \"gaussian\", \"cosine\", \"cubic\", \"pentaspherical\", \"wave\", \"jbessel\", \"gravity\", \"rquad\", \"magnetic\", \"none\". rotate Anisotropy rotation parameter (0 \\(\\pi\\) radians) euclidean portion covariance. value 0 (default) implies rotation. scale Anisotropy scale parameter (0 1) euclidean portion covariance. value 1 (default) implies scaling. nugget_type nugget covariance function type. Available options include \"nugget\" \"none\". nugget spatially independent (correlated) random error variance. Commonly referred nugget.","code":""},{"path":"/reference/ssn_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create covariance parameter objects. — ssn_params","text":"parameter object class matches relevant type argument.","code":""},{"path":"/reference/ssn_params.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create covariance parameter objects. — ssn_params","text":"Peterson, E.E. Ver Hoef, J.M. (2010) mixed-model moving-average approach geostatistical modeling stream networks. Ecology 91(3), 644–651. Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6–18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.","code":""},{"path":"/reference/ssn_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create covariance parameter objects. — ssn_params","text":"","code":"tailup_params(\"exponential\", de = 1, range = 20) #>    de range  #>     1    20  #> attr(,\"class\") #> [1] \"tailup_exponential\" taildown_params(\"exponential\", de = 1, range = 20) #>    de range  #>     1    20  #> attr(,\"class\") #> [1] \"taildown_exponential\" euclid_params(\"exponential\", de = 1, range = 20, rotate = 0, scale = 1) #>     de  range rotate  scale  #>      1     20      0      1  #> attr(,\"class\") #> [1] \"euclid_exponential\" nugget_params(\"nugget\", nugget = 1) #> nugget  #>      1  #> attr(,\"class\") #> [1] \"nugget_nugget\""},{"path":"/reference/ssn_put_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Put an sf data.frame in an SSN object — ssn_put_data","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"ssn_put_data function puts sf data.frame representing observation prediction data SSN, ssn_lm, ssn_glm object.","code":""},{"path":"/reference/ssn_put_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"","code":"ssn_put_data(data, x, name = \"obs\", resize_data = FALSE)"},{"path":"/reference/ssn_put_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"data sf data.frame point geometry. x object class SSN, ssn_lm, ssn_glm. name internal name data set object x. observed data, always \"obs\", default. resize_data Logical. Indicates whether sf_df can different number features current data.frame object. Default FALSE.","code":""},{"path":"/reference/ssn_put_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"Returns object class x, contains sf data.frame sf_data.","code":""},{"path":"/reference/ssn_put_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"internal name observed data objects class SSN, ssn_lm, ssn_glm \"obs\" default. another name specified, must represent prediction dataset object. SSN objects, names obtained using call names(x$preds). object classes, names obtained using call names(x$ssn.object$preds). resize_sf_data argument specifies whether sf_data can different number features (.e., rows) sf data.frame replacing. Care taken resize_df set TRUE, especially new sf_data features existing sf data.frame. cases, user responsible ensuring additional features correct spatial, topological, attribute data accurately represent spatial relationships SSN object.","code":""},{"path":[]},{"path":"/reference/ssn_put_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Put an sf data.frame in an SSN object — ssn_put_data","text":"","code":"data(mf04p) ## Extract observation data.frame from SSN object obs.df <- ssn_get_data(mf04p) ## Create a new column for summer mean temperature and set Value in obs.df$Value <- obs.df$Summer_mn obs.df$Value[1] <- NA  ## Put the modified sf data.frame into the SSN object mf04p <- ssn_put_data(obs.df, mf04p) head(ssn_get_data(mf04p)[, c(\"Summer_mn\", \"Value\")]) #> Simple feature collection with 6 features and 2 fields #> Geometry type: POINT #> Dimension:     XY #> Bounding box:  xmin: -1528485 ymin: 927846.1 xmax: -1519790 ymax: 930808.7 #> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic #>   Summer_mn Value                  geometry #> 1     14.92    NA POINT (-1528194 929550.4) #> 2     14.72 14.72 POINT (-1528222 928237.7) #> 3     14.61 14.61 POINT (-1528485 927846.1) #> 4     15.22 15.22 POINT (-1519790 930112.1) #> 5     14.49 14.49   POINT (-1520336 929772) #> 6     15.29 15.29 POINT (-1524599 930808.7)"},{"path":"/reference/ssn_simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate random variables on a stream network — ssn_simulate","title":"Simulate random variables on a stream network — ssn_simulate","text":"Simulate random variables stream network specific mean covariance structure. Designed use ssn_simulate(), individual simulation functions resposne distribution also exist.","code":""},{"path":"/reference/ssn_simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate random variables on a stream network — ssn_simulate","text":"","code":"ssn_simulate(   family = \"Gaussian\",   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   additive,   mean = 0,   samples = 1,   dispersion = 1,   size = 1,   randcov_params,   partition_factor,   ... )  ssn_rbeta(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rbinom(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   mean = 0,   size = 1,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rgamma(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rinvgauss(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rnbinom(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   dispersion = 1,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rnorm(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )  ssn_rpois(   ssn.object,   network = \"obs\",   tailup_params,   taildown_params,   euclid_params,   nugget_params,   mean = 0,   samples = 1,   additive,   randcov_params,   partition_factor,   ... )"},{"path":"/reference/ssn_simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate random variables on a stream network — ssn_simulate","text":"family response distribution family. default \"Gaussian\". ssn.object spatial stream network object class SSN. Random variables simulated row ssn.object$obs. network spatial stream network simulate . Currently allowed \"obs\" ssn.object$obs object. tailup_params object tailup_params() specifying tailup covariance parameters. taildown_params object taildown_params() specifying taildown covariance parameters. euclid_params object euclid_params() specifying Euclidean covariance parameters. nugget_params object nugget_params() specifying nugget covariance parameters. additive name variable ssn.object used define spatial weights. Can quoted unquoted. tailup covariance functions, additive weights used branching. Technical details describe role additive variable tailup covariance function available Ver Hoef Peterson (2010). mean numeric vector representing mean. mean must length 1 (case recycled) length equal number rows data. default 0. samples number independent samples generate. default 1. dispersion dispersion value (relevant). size numeric vector representing sample size binomial trial. default 1, corresponds Bernoulli trial observation. randcov_params spmodel::randcov_params() object. partition_factor formula indicating partition factor. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/ssn_simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate random variables on a stream network — ssn_simulate","text":"samples 1, vector random variables row ssn.object$obs returned. samples greater one, matrix random variables returned, rows correspond row ssn.object$obs columns correspond independent samples.","code":""},{"path":"/reference/ssn_simulate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate random variables on a stream network — ssn_simulate","text":"Random variables simulated via product covariance matrix's square (Cholesky) root independent standard normal random variables link scale, used simulate relevant variable response scale according family. Computing square root significant computational burden likely unfeasible sample sizes much past 10,000. square root needs computed , however, nearly sample computational cost call ssn_rnorm() value samples. using ssn_simulate(), individual simulation functions response distribution exist: ssn_rnorm(): Simulate Gaussian distribution ssn_rpois(): Simulate Poisson distribution ssn_rnbinom(): Simulate negative binomial distribution ssn_rbinom(): Simulate binomial distribution ssn_rbeta(): Simulate beta distribution ssn_rgamma(): Simulate gamma distribution ssn_rinvgauss(): Simulate inverse Gaussian distribution","code":""},{"path":"/reference/ssn_simulate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate random variables on a stream network — ssn_simulate","text":"Ver Hoef, J.M. Peterson, E.E. (2010) moving average approach spatial statistical models stream networks (discussion). Journal American Statistical Association 105, 6–18. DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.","code":""},{"path":"/reference/ssn_simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate random variables on a stream network — ssn_simulate","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  tailup <- tailup_params(\"exponential\", de = 0.1, range = 200) taildown <- taildown_params(\"exponential\", de = 0.4, range = 300) euclid <- euclid_params(\"spherical\", de = 0.2, range = 1000, rotate = 0, scale = 1) nugget <- nugget_params(\"nugget\", nugget = 0.1) ssn_simulate(\"gaussian\", mf04p, \"obs\", tailup, taildown, euclid, nugget, additive = \"afvArea\") #>  [1]  1.45785676  0.76604939 -0.12910867 -0.35397583  0.19671514 -0.07588754 #>  [7]  0.57765723 -0.81538653  1.75224086  0.72687527 -0.65778712 -1.73063057 #> [13] -0.11226686  0.46230814  1.30434670  0.39980221  0.34100800  1.58922294 #> [19] -0.56814769 -1.01022346 -0.83452440 -0.41155550 -0.59774668 -0.31112518 #> [25]  0.09688149 -0.48593585  1.22120747  1.56687390 -0.15460358  0.89744186 #> [31] -0.43396443 -0.53227176 -0.12537756  0.97001333 -0.24502012  1.62890851 #> [37] -0.15335130 -0.78883608 -0.80805800 -1.18710291 -0.35885264  0.13201272 #> [43]  0.56219793  0.73955602 -0.88882700"},{"path":"/reference/ssn_split_predpts.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a prediction dataset in an SSN object — ssn_split_predpts","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"splitPrediction function used split prediction sets SSN object smaller prediction sets. returns SSN object additional prediction sets based equal interval splits, factor, integer, character logical column stored within prediction set, logical expression.","code":""},{"path":"/reference/ssn_split_predpts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"","code":"ssn_split_predpts(   ssn,   predpts,   size_predpts,   by,   subset,   id_predpts,   keep = TRUE,   drop_levels = FALSE,   overwrite = FALSE )"},{"path":"/reference/ssn_split_predpts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"ssn SSN object. predpts character string representing name prediction dataset. size_predpts numeric value representing size new prediction sets. existing prediction set split equally produce multiple prediction sets size character string representing column name type factor, integer, character logical split based subset logical expression indicating elements rows keep; missing values taken FALSE id_predpts character string representing new prediction dataset name. value specified subset method used keep logical value indicating whether original prediction dataset retained SSN object. Default TRUE drop_levels logical value indicating whether empty factor levels dropped column new prediction dataset(s) created. Default FALSE overwrite logical indicating whether new prediction dataset geopackage deleted .ssn directory already exists. Default = FALSE","code":""},{"path":"/reference/ssn_split_predpts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"returns SSN specified ssn, one new prediction sets. Geopackages new prediction sets written .ssn directory designated ssn$path.","code":""},{"path":"/reference/ssn_split_predpts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"Three methods provided split prediction sets: size, , subset. size method used split existing prediction set multiple equally-sized prediction sets using size_predpts argument. Note final prediction set may smaller size others total number predictions evenly divisible size_predpts. method used prediction set split multiple new prediction sets based existing column type factor, integer, character, logical specified using argument . subset method used create one new prediction set based logical expression defined subset. one prediction dataset created prediction dataset names appended hyphen prediction dataset number one prediction dataset created. example, \"preds\" split using size_predpts, new names \"preds-1\", \"preds-2\", forth. keep=FALSE, prediction dataset removed SSN object stored memory, deleted .ssn directory specified ssn$path. Note , one method may specified ssn_split_predpts function called. distance matrices new prediction datasets must created using ssn_create_distmat predictions can made.","code":""},{"path":"/reference/ssn_split_predpts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a prediction dataset in an SSN object — ssn_split_predpts","text":"","code":"## Import SSN object copy_lsn_to_temp() ## Only needed for this example ssn <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = c(\"pred1km\", \"CapeHorn\"),   overwrite = TRUE )  ## Split predictions based on 'size' method ssn1 <- ssn_split_predpts(ssn, \"CapeHorn\",   size_predpts = 200,   keep = FALSE, overwrite = TRUE ) names(ssn1$preds) #> [1] \"pred1km\"    \"CapeHorn-1\" \"CapeHorn-2\" \"CapeHorn-3\" \"CapeHorn-4\" nrow(ssn1$preds[[\"CapeHorn-1\"]]) #> [1] 200  ## Split predictions using 'by' method ssn$preds$pred1km$net.fac <- as.factor(ssn$preds$pred1km$netID) ssn2 <- ssn_split_predpts(ssn, \"pred1km\",   by = \"net.fac\",   overwrite = TRUE ) names(ssn2$preds) #> [1] \"pred1km\"           \"CapeHorn\"          \"pred1km-net.fac-1\" #> [4] \"pred1km-net.fac-2\"  ## Split predictions using 'subset' method ssn3 <- ssn_split_predpts(ssn, \"pred1km\",   subset = ratio > 0.5,   id_predpts = \"RATIO_05\", overwrite = TRUE ) names(ssn3$preds) #> [1] \"pred1km\"  \"CapeHorn\" \"RATIO_05\""},{"path":"/reference/ssn_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset an SSN object — ssn_subset","title":"Subset an SSN object — ssn_subset","text":"Returns SSN object subset based logical expression.","code":""},{"path":"/reference/ssn_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset an SSN object — ssn_subset","text":"","code":"ssn_subset(ssn, path, subset, clip = FALSE, overwrite = FALSE)"},{"path":"/reference/ssn_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset an SSN object — ssn_subset","text":"ssn SSN object. path filepath .ssn folder, string format, subset SSN saved. subset logical expression indicating features keep. clip TRUE, create subset edges prediction sites, based logical expression used subset observed sites.  Default = FALSE. overwrite TRUE, overwrite folder specified path exists. Default = FALSE.","code":""},{"path":"/reference/ssn_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset an SSN object — ssn_subset","text":"object class SSN, stored locally .ssn directory specified path. also creates stores SQLite database, binaryID.db, within .ssn directory.","code":""},{"path":"/reference/ssn_subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subset an SSN object — ssn_subset","text":"function creates subset original SSN object based logical expression defined subset argument. subset argument treated expression within ssn_subset() full argument string; although values factor character format still require quotes (see examples). clip = TRUE, columns referred subset must present edges prediction datasets (present SSN object). Note features missing values subset expression treated false included subset SSN object. subset SSN object written local directory, re-imported using ssn_import. process, binaryID.db recreated. distance matrices exist original SSN object, copied recalculated new SSN object. Users need run ssn_create_distmat create distance matrices fitting models data subset SSN.","code":""},{"path":"/reference/ssn_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset an SSN object — ssn_subset","text":"","code":"## Import SSN object copy_lsn_to_temp() ## Only needed for this example mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = \"pred1km\",   overwrite = TRUE )  ## Subset SSN observations, edges, and prediction sites on network 1 ssn.sub1 <- ssn_subset(mf04p,   path = paste0(tempdir(), \"/subset1.ssn\"),   subset = netID == 1, clip = TRUE,   overwrite = TRUE )  ## Subset SSN observations, removing two sites ssn.sub2 <- ssn_subset(mf04p,   path = paste0(tempdir(), \"/subset2.ssn\"),   subset = !COMID %in% c(\"23519461\", \"23519365\"),   overwrite = TRUE )"},{"path":"/reference/SSN_to_SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"Convert S4 SpatialStreamNetwork object created SSN package S3 SSN object used SSN2 package.","code":""},{"path":"/reference/SSN_to_SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"","code":"SSN_to_SSN2(object)"},{"path":"/reference/SSN_to_SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"object SpatialStreamNetwork object","code":""},{"path":"/reference/SSN_to_SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"S3 SSN class object.","code":""},{"path":"/reference/SSN_to_SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2","text":"SSN_to_SSN2() made available help users migrate SSN package updated SSN2 package. used convert existing S4 SpatialStreamNetwork objects stored saved workspaces S3 SSN class object used SSN2 package. Note ssn_import used create S3 SSN object data stored locally .ssn directory.","code":""},{"path":"/reference/ssn_update_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Update path in an SSN object — ssn_update_path","title":"Update path in an SSN object — ssn_update_path","text":"Update local path existing SSN object based user defined file.","code":""},{"path":"/reference/ssn_update_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update path in an SSN object — ssn_update_path","text":"","code":"ssn_update_path(x, path, verbose = FALSE)"},{"path":"/reference/ssn_update_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update path in an SSN object — ssn_update_path","text":"x SSN, ssn_lm ssn_glm object. path Filepath .ssn folder associated SSN object. verbose logical indicates new path printed console.","code":""},{"path":"/reference/ssn_update_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update path in an SSN object — ssn_update_path","text":"SSN object new path list element.","code":""},{"path":"/reference/ssn_update_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Update path in an SSN object — ssn_update_path","text":"times, may necessary move .ssn directory, linked SSN object R workspace. .ssn directory moved, path must updated using ssn_glmssn function functions read/write .ssn directory. ssn_update_path helper function serves purpose.","code":""},{"path":"/reference/ssn_update_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update path in an SSN object — ssn_update_path","text":"","code":"## Use mf04p SSN object provided in SSN2 data(mf04p)  ## For examples only, make sure mf04p has the correct path ## If you use ssn_import(), the path will be correct newpath <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_update_path(mf04p, newpath)"},{"path":"/reference/ssn_write.html","id":null,"dir":"Reference","previous_headings":"","what":"write an SSN object — ssn_write","title":"write an SSN object — ssn_write","text":"function writes SSN object local .ssn directory","code":""},{"path":"/reference/ssn_write.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"write an SSN object — ssn_write","text":"","code":"ssn_write(ssn, path, overwrite = FALSE, copy_dist = FALSE, import = FALSE)"},{"path":"/reference/ssn_write.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"write an SSN object — ssn_write","text":"ssn SSN object. path filepath local .ssn directory write . overwrite TRUE, overwrite existing files file (exists). Defaults FALSE. copy_dist TRUE, copy distance matrices file (exist). Defaults FALSE. import TRUE, import return SSN object writing file. Defaults FALSE.","code":""},{"path":"/reference/ssn_write.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"write an SSN object — ssn_write","text":"ssn_write creates .ssn directory contains spatial, topological, attribute information stored original SSN object. Spatial datasets found SSN object (e.g. edges, obs, prediction sites) saved GeoPackage format. import = TRUE, SSN object imported returned.","code":""},{"path":"/reference/ssn_write.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"write an SSN object — ssn_write","text":"","code":"## For examples only, copy MiddleFork04.ssn directory to R's # temporary directory copy_lsn_to_temp() ## Import SSN object with prediction sites mf04p <- ssn_import(paste0(tempdir(), \"/MiddleFork04.ssn\"),   predpts = \"pred1km\",   overwrite = TRUE )  ## Write SSN to new .ssn directory ssn_write(mf04p,   path = paste0(tempdir(), \"/tempSSN.ssn\"),   overwrite = TRUE )  ## Write SSN to .ssn directory and return SSN object tempSSN <- ssn_write(mf04p, path = paste0(   tempdir(),   \"/tempSSN.ssn\" ), overwrite = TRUE, import = TRUE)"},{"path":"/reference/summary.SSN.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize an SSN object — summary.SSN","title":"Summarize an SSN object — summary.SSN","text":"Summarize data found SSN object.","code":""},{"path":"/reference/summary.SSN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize an SSN object — summary.SSN","text":"","code":"# S3 method for class 'SSN' summary(object, ...)"},{"path":"/reference/summary.SSN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize an SSN object — summary.SSN","text":"object SSN object. ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/summary.SSN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize an SSN object — summary.SSN","text":"list several fitted model quantities used create informative summaries printing.","code":""},{"path":"/reference/summary.SSN.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summarize an SSN object — summary.SSN","text":"summary.SSN() creates summary SSN object intended printed using print(). summary contains information number observed prediction locations, well column names found respective sf data.frames.","code":""},{"path":"/reference/summary.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a fitted model object — summary.SSN2","title":"Summarize a fitted model object — summary.SSN2","text":"Summarize fitted model object.","code":""},{"path":"/reference/summary.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a fitted model object — summary.SSN2","text":"","code":"# S3 method for class 'ssn_lm' summary(object, ...)  # S3 method for class 'ssn_glm' summary(object, ...)"},{"path":"/reference/summary.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a fitted model object — summary.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/summary.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a fitted model object — summary.SSN2","text":"list several fitted model quantities used create informative summaries printing.","code":""},{"path":"/reference/summary.SSN2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summarize a fitted model object — summary.SSN2","text":"summary.ssn() creates summary fitted model object intended printed using print(). summary contains useful information like original function call, residuals, coefficients table, pseudo r-squared, estimated covariance parameters.","code":""},{"path":[]},{"path":"/reference/summary.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize a fitted model object — summary.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) summary(ssn_mod) #>  #> Call: #> ssn_lm(formula = Summer_mn ~ ELEV_DEM, ssn.object = mf04p, tailup_type = \"exponential\",  #>     additive = \"afvArea\") #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -2.6986 -1.4031  0.2287  0.6796  2.2456  #>  #> Coefficients (fixed): #>              Estimate Std. Error z value Pr(>|z|)     #> (Intercept) 80.857837   8.814114   9.174  < 2e-16 *** #> ELEV_DEM    -0.034124   0.004376  -7.797 6.22e-15 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Pseudo R-squared: 0.5855 #>  #> Coefficients (covariance): #>               Effect     Parameter   Estimate #>   tailup exponential  de (parsill)  1.390e+00 #>   tailup exponential         range  1.306e+05 #>               nugget        nugget  5.415e-02 #>"},{"path":"/reference/tidy.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy a fitted model object — tidy.SSN2","title":"Tidy a fitted model object — tidy.SSN2","text":"Tidy fitted model object summarized tibble.","code":""},{"path":"/reference/tidy.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy a fitted model object — tidy.SSN2","text":"","code":"# S3 method for class 'ssn_lm' tidy(x, conf.int = FALSE, conf.level = 0.95, effects = \"fixed\", ...)  # S3 method for class 'ssn_glm' tidy(x, conf.int = FALSE, conf.level = 0.95, effects = \"fixed\", ...)"},{"path":"/reference/tidy.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy a fitted model object — tidy.SSN2","text":"x fitted model object ssn_lm() ssn_glm(). conf.int Logical indicating whether include confidence interval tidied output. default FALSE. conf.level confidence level use confidence interval conf.int TRUE. Must strictly greater 0 less 1. default 0.95, corresponds 95 percent confidence interval. effects type effects tidy. Available options \"fixed\" (fixed effects), \"tailup\" (tailup covariance parameters), \"taildown\" (taildown covariance parameters), \"euclid\" (Euclidean covariance parameters), \"nugget\" (nugget covariance parameter), \"dispersion\" (dispersion parameter relevant), \"ssn\" \"tailup\", \"taildown\", \"euclid\", \"nugget\", \"dispersion\", \"randcov\" (random effect variances). default \"fixed\". ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/tidy.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy a fitted model object — tidy.SSN2","text":"tidy tibble summary information effects.","code":""},{"path":[]},{"path":"/reference/tidy.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy a fitted model object — tidy.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) tidy(ssn_mod) #> # A tibble: 2 × 5 #>   term        estimate std.error statistic  p.value #>   <chr>          <dbl>     <dbl>     <dbl>    <dbl> #> 1 (Intercept)  80.9      8.81         9.17 0        #> 2 ELEV_DEM     -0.0341   0.00438     -7.80 6.22e-15"},{"path":"/reference/Torgegram.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the empirical semivariogram — Torgegram","title":"Compute the empirical semivariogram — Torgegram","text":"Compute empirical semivariogram varying bin sizes cutoff values.","code":""},{"path":"/reference/Torgegram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the empirical semivariogram — Torgegram","text":"","code":"Torgegram(   formula,   ssn.object,   type = c(\"flowcon\", \"flowuncon\"),   bins = 15,   cutoff,   partition_factor )"},{"path":"/reference/Torgegram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the empirical semivariogram — Torgegram","text":"formula formula describing fixed effect structure. ssn.object spatial stream network object class SSN. type Torgegram type. vector possible values \"flowcon\" flow-connected distances, \"flowuncon\" flow-unconnected distances, \"euclid\" Euclidean distances. default show flow-connected flow-unconnected distances. bins number equally spaced bins. default 15. cutoff maximum distance considered. default half diagonal bounding box coordinates. partition_factor optional formula specifying partition factor. specified, semivariances computed observations sharing level partition factor.","code":""},{"path":"/reference/Torgegram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the empirical semivariogram — Torgegram","text":"list elements correspond type. element data frame distance bins (bins),  average distance (dist), semivariance (gamma), number (unique) pairs (np) respective type.","code":""},{"path":"/reference/Torgegram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute the empirical semivariogram — Torgegram","text":"Torgegram empirical semivariogram tool used visualize model spatial dependence estimating semivariance process varying distances separately flow-connected, flow-unconnected, Euclidean distances. constant-mean process, semivariance distance \\(h\\) denoted \\(\\gamma(h)\\) defined \\(0.5 * Var(z1  - z2)\\). second-order stationarity, \\(\\gamma(h) = Cov(0) - Cov(h)\\), \\(Cov(h)\\) covariance function distance h. Typically residuals ordinary least squares fit defined formula second-order stationary mean zero. residuals used compute empirical semivariogram. distance h, empirical semivariance \\(1/N(h) \\sum (r1 - r2)^2\\), \\(N(h)\\) number (unique) pairs set observations whose distance separation h r1 r2 residuals corresponding observations whose distance separation h. spmodel, distance bins actually contain observations whose distance separation h +- c, c constant determined implicitly bins. Typically, observations whose distance separation cutoff used compute empirical semivariogram (cutoff determined cutoff).","code":""},{"path":"/reference/Torgegram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute the empirical semivariogram — Torgegram","text":"Zimmerman, D. L., & Ver Hoef, J. M. (2017). Torgegram fluvial variography: characterizing spatial dependence stream networks. Journal Computational Graphical Statistics, 26(2), 253–264.","code":""},{"path":[]},{"path":"/reference/Torgegram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the empirical semivariogram — Torgegram","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  tg <- Torgegram(Summer_mn ~ 1, mf04p) plot(tg)"},{"path":"/reference/varcomp.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Variability component comparison — varcomp.SSN2","title":"Variability component comparison — varcomp.SSN2","text":"Compare proportion total variability explained fixed effects variance parameter.","code":""},{"path":"/reference/varcomp.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variability component comparison — varcomp.SSN2","text":"","code":"# S3 method for class 'ssn_lm' varcomp(object, ...)  # S3 method for class 'ssn_glm' varcomp(object, ...)"},{"path":"/reference/varcomp.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variability component comparison — varcomp.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency).","code":""},{"path":"/reference/varcomp.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variability component comparison — varcomp.SSN2","text":"tibble partitions total variability fixed effects variance parameter. proportion variability explained fixed effects pseudo R-squared obtained psuedoR2(). remaining proportion spread accordingly among variance parameter: \"tailup_de\", \"taildown_de\", \"euclid_de\", \"nugget\", random effects used, named random effect. ssn_glm(), models, variances link scale considered (.e., variance function response omitted).","code":""},{"path":"/reference/varcomp.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variability component comparison — varcomp.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) varcomp(ssn_mod) #> # A tibble: 5 × 2 #>   varcomp            proportion #>   <chr>                   <dbl> #> 1 Covariates (PR-sq)     0.585  #> 2 tailup_de              0.399  #> 3 taildown_de            0      #> 4 euclid_de              0      #> 5 nugget                 0.0155"},{"path":"/reference/vcov.SSN2.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"Calculate variance-covariance matrix fitted model object.","code":""},{"path":"/reference/vcov.SSN2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"","code":"# S3 method for class 'ssn_lm' vcov(object, ...)  # S3 method for class 'ssn_glm' vcov(object, var_correct = TRUE, ...)"},{"path":"/reference/vcov.SSN2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"object fitted model object ssn_lm() ssn_glm(). ... arguments. used (needed generic consistency). var_correct logical indicating whether return corrected variance-covariance matrix models fit using ssn_glm() (family different \"Gaussian\"). default TRUE.","code":""},{"path":"/reference/vcov.SSN2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"variance-covariance matrix coefficients obtained via coef(). Currently, variance-covariance matrix fixed effects supported.","code":""},{"path":"/reference/vcov.SSN2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate variance-covariance matrix for a fitted model object — vcov.SSN2","text":"","code":"# Copy the mf04p .ssn data to a local directory and read it into R # When modeling with your .ssn object, you will load it using the relevant # path to the .ssn data on your machine copy_lsn_to_temp() temp_path <- paste0(tempdir(), \"/MiddleFork04.ssn\") mf04p <- ssn_import(temp_path, overwrite = TRUE)  ssn_mod <- ssn_lm(   formula = Summer_mn ~ ELEV_DEM,   ssn.object = mf04p,   tailup_type = \"exponential\",   additive = \"afvArea\" ) vcov(ssn_mod) #>             (Intercept)      ELEV_DEM #> (Intercept) 77.68860270 -3.855169e-02 #> ELEV_DEM    -0.03855169  1.915303e-05"},{"path":"/news/index.html","id":"ssn2-020","dir":"Changelog","previous_headings":"","what":"SSN2 0.2.0","title":"SSN2 0.2.0","text":"CRAN release: 2024-07-26","code":""},{"path":"/news/index.html","id":"major-updates-0-2-0","dir":"Changelog","previous_headings":"","what":"Major Updates","title":"SSN2 0.2.0","text":"Significant testing, documentation, auxiliary (e.g., README.md) updates part submission Journal Open Source Software. Relevant issues associated review available #11, #12, #13, #14, #15, #16, #17, #20, #21. review linked . Added support geopackage file formats .ssn folder accessed importing SSN objects via ssn_import().","code":""},{"path":"/news/index.html","id":"minor-updates-0-2-0","dir":"Changelog","previous_headings":"","what":"Minor Updates","title":"SSN2 0.2.0","text":"Added ssn_names() return column names edges, obs, preds elements SSN object. Changed Matrix::rankMatrix(X, method = \"tolNorm2\") Matrix::rankMatrix(X, method = \"qr\") enhance stability determining linear independence X, design matrix explanatory variables. Replaced error message warning message X perfect collinearities (.e., full rank). Removed format_additive argument ssn_import() transition geopackage support, eliminates need convert additive function values text. Added create_netgeom() function create network geometry column edges, obs, preds elements SSN object. Minor vignette updates. Minor documentation updates.","code":""},{"path":"/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"SSN2 0.2.0","text":"Fixed bug SSN_to_SSN2() caused error using ssn_write() prediction sites. Replaced names.SSN() ssn_names(), names.SSN() prevented proper naming elements SSN object.","code":""},{"path":"/news/index.html","id":"ssn2-011","dir":"Changelog","previous_headings":"","what":"SSN2 0.1.1","title":"SSN2 0.1.1","text":"CRAN release: 2024-01-15","code":""},{"path":"/news/index.html","id":"minor-updates-0-1-1","dir":"Changelog","previous_headings":"","what":"Minor Updates","title":"SSN2 0.1.1","text":"Changed network geometry name netgeometry netgeom avoid exceeding 10 character limit column/field names writing shapefiles (#2). Added error message family missing ssn_glm() (#8). Added deprecation warning SSN_to_SSN2(). Minor stability updates. Minor error message updates. Minor documentation updates.","code":""},{"path":"/news/index.html","id":"bug-fixes-0-1-1","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"SSN2 0.1.1","text":"Fixed bug Torgegram() prevented intended computation cutoff specified. Fixed bug plot.Torgegram() occasionally prevented proper spacing legend. Fixed bug prevented proper printing dispersion parameter ssn_glm() model objects (summaries) covariance parameters known. Fixed bug prevented simulation euclid_type \"none\". Fixed bug cause improper prediction behavior taildown_type \"spherical\". Fixed bug printed response residuals instead deviance residuals ssn_glm() objects.","code":""},{"path":"/news/index.html","id":"ssn2-010","dir":"Changelog","previous_headings":"","what":"SSN2 0.1.0","title":"SSN2 0.1.0","text":"CRAN release: 2023-11-08 Initial CRAN submission.","code":""}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index e6b42c4..90937ed 100644
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diff --git a/inst/CITATION b/inst/CITATION
index 2ace81a..1f8bc02 100644
--- a/inst/CITATION
+++ b/inst/CITATION
@@ -14,11 +14,11 @@ bibentry(
                           family = "Isaak")
                           ),
   year         = "2023",
-  note         = "{R} package version 0.1.0",
+  note         = "{R} package version 0.2.0",
 
   header       = "To cite SSN2 in publications use:",
   textVersion  =
   paste("Dumelle M, Peterson, E, Ver Hoef JM, Pearse A, Isaak D (2023).",
         "SSN2: Spatial Modeling on Stream Networks in R.",
-        "R package version 0.1.0")
+        "R package version 0.2.0")
 )
diff --git a/inst/joss/apa.csl b/inst/joss/apa.csl
new file mode 100644
index 0000000..32be69e
--- /dev/null
+++ b/inst/joss/apa.csl
@@ -0,0 +1,443 @@
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+    <updated>2010-01-27T20:08:03+00:00</updated>
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diff --git a/inst/joss/paper.Rmd b/inst/joss/paper.Rmd
new file mode 100644
index 0000000..68d2300
--- /dev/null
+++ b/inst/joss/paper.Rmd
@@ -0,0 +1,168 @@
+---
+title: 'SSN2: The next generation of spatial stream network modeling in R'
+tags:
+  - R
+  - Spatial stream network
+  - Spatial correlation
+  - Spatial prediction (Kriging)
+  - Geostatistics
+  - Generalized linear model
+  - Torgegram
+authors:
+  - name: Michael Dumelle
+    orcid: 0000-0002-3393-5529
+    affiliation: "1" # (Multiple affiliations must be quoted)
+  - name: Erin E. Peterson
+    orcid: 0000-0003-2992-0372
+    affiliation: 2
+  - name: Jay M. Ver Hoef
+    orcid: 0000-0003-4302-6895
+    affiliation: 3
+  - name: Alan Pearse
+    orcid: 0000-0002-4133-8548 
+    affiliation: 4
+  - name: Daniel J. Isaak
+    affiliation: 5
+affiliations:
+ - name: Pacific Ecological Systems Division, United States Environmental Protection Agency, Corvallis, OR, USA
+   index: 1
+ - name: EP Consulting and Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
+   index: 2
+ - name: NMFS Alaska Fisheries Science Center, United States National Oceanic and Atmospheric Administration, Seattle, WA, USA
+   index: 3
+ - name: NIASRA, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia
+   index: 4
+ - name: Rocky Mountain Research Station, United States Forest Service, Boise, ID, USA
+   index: 5
+citation_author: Dumelle et. al.
+date: 07 July 2024
+year: 2024
+bibliography: paper.bib
+output: rticles::joss_article
+csl: apa.csl
+journal: JOSS
+editor_options: 
+  chunk_output_type: console
+---
+
+# Summary
+
+The `SSN2` **R** package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. `SSN2` is the successor to the `SSN` **R** package [@ver2014ssn], which was archived alongside broader changes in the **R**-spatial ecosystem [@nowosad2023] that included 1) the retirement of `rgdal` [@bivand2021rgdal], `rgeos` [@bivand2020rgeos], and `maptools` [@bivand2021maptools] and 2) the lack of active development of `sp` [@bivand2013applied]. `SSN2` maintains compatibility with the input data file structures used by the `SSN` **R** package but leverages modern **R**-spatial tools like `sf` [@pebesma2018sf]. `SSN2` also provides many useful features that were not available in the `SSN` **R** package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other **R** generic functions.
+
+# Statement of Need
+
+Streams provide vital aquatic services that sustain wildlife, provide drinking and irrigation water, and support recreational and cultural activities.  Data are often collected at various locations on a stream network and used to characterize spatial patterns in stream phenomena. For example, a manager may need to know how the amount of a hazardous chemical changes throughout a stream network to inform mitigation efforts. Comprehensive formulations of spatial stream network (SSN) models are provided by @ver2010moving, @peterson2010mixed, and @ver2014ssn. The `SSN2` **R** package is designed to help users fit SSN models to their stream network data.
+
+SSN models use a spatial statistical modeling framework [e.g., @cressie1993statistics] to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume. These SSN models relate a continuous or discrete response variable to one or more explanatory variables, a spatially independent random error term, and up to three spatially dependent random error terms: tail-up random errors, tail-down random errors, and Euclidean random errors. Tail-up random errors restrict spatial dependence to flow-connected sites (i.e., water flows from an upstream to a downstream site) and incorporate spatial weights through an additive function to describe the branching network between sites. Tail-down random errors describe spatial dependence between both flow-connected and flow-unconnected sites (i.e., sites that share a common downstream junction but not flow), but spatial weights are not required. Euclidean random errors describe spatial dependence between sites based on straight-line distance and are governed by factors not confined to the stream network, such as regional geology. The variances and the length-scales of spatial dependence in the tail-up, tail-down, and Euclidean random errors are controlled by separate variance (i.e., partial sill) and range parameters, respectively, while the spatially independent variance (i.e., nugget) is controlled by another separate variance parameter. In this paper, we show how to use the `SSN2` **R** package to fit SSN models, inspect SSN models, and use SSN models to make predictions at unobserved locations on a stream network. 
+
+# Package Overview
+
+The streams, observation, and prediction datasets must be pre-processed prior to fitting SSN models and making predictions at unobserved locations using `SSN2`. Previously, the STARS toolset for ArcGIS Desktop versions 9.3x - 10.8x [@peterson2014stars] or the `openSTARS` **R** package [@kattwinkel2020preparing] were used to generate spatial information required for model fitting and prediction. However, both software packages have recently been retired and are replaced by the `SSNbler` **R** package [@peterson2024SSNbler], which is a new, **R**-based version of the STARS tools. `SSNbler` is currently available on GitHub, will soon be available on CRAN, and contains several useful resources that guide users through these pre-processing steps. Pre-processing using either `SSNbler`, STARS, or `openSTARS` ends with the creation of a `.ssn` folder, which is non-proprietary. Files residing in the `.ssn` folder are read into R using `ssn_import()` from `SSN2` and placed into a list structure called an SSN object, which contains all the spatial, topological, and attribute information needed to leverage the modeling tools in `SSN2`.
+
+`SSN2` is first installed from CRAN:
+```{r, eval = FALSE}
+install.packages("SSN2")
+```
+
+Then, `SSN2` is loaded into an **R** session:
+```{r, warning = FALSE}
+library(SSN2)
+```
+
+The `SSN2` package comes with an example `.ssn` folder called `MiddleFork04.ssn` that represents water temperatures recorded from a stream network in the Middle Fork of the Salmon River in Idaho, USA during 2004. 
+
+Several functions in `SSN2` for reading and writing data directly manipulate the `.ssn` folder. To avoid directly manipulating the `MiddleFork04.ssn` data installed alongside `SSN2`, `MiddleFork04.ssn` is instead copied into a temporary directory and the relevant path to this directory stored:
+```{r}
+copy_lsn_to_temp()
+path <- file.path(tempdir(), "MiddleFork04.ssn")
+```
+
+The `copy_lsn_to_temp()` function is only used when working with `MiddleFork04.ssn` and generally, `path` should indicate a permanent directory on your computer that points towards your `.ssn` object. After specifying `path`, the stream reaches, observed sites, and prediction sites (`pred1km`) are imported and then visualized (Figure$~$\ref{fig:steam-network}):
+```{r, message=FALSE, results='hide'}
+mf04p <- ssn_import(path, predpts = "pred1km")
+```
+
+```{r steam-network, fig.cap="Middle Fork 2004 stream networks. Observed sites are represented by brown, closed circles at various locations throughout the stream network. Prediction sites are represented by blue, closed triangles and are spaced one kilometer apart.", out.width="85%", fig.align="center", warning=FALSE}
+library(ggplot2)
+ggplot() +
+  geom_sf(data = mf04p$edges) +
+  geom_sf(data = mf04p$preds$pred1km, pch = 17, color = "blue") +
+  geom_sf(data = mf04p$obs, color = "brown", size = 2) +
+  theme_bw()
+```
+
+Prior to statistical modeling, hydrologic distance matrices are created [@ver2010moving]:
+```{r}
+ssn_create_distmat(mf04p, predpts = "pred1km", overwrite = TRUE)
+```
+
+Of particular interest here is summer mean stream temperature (`Summer_mn`) in degrees Celsius, 
+which will be modeled as a function of elevation (`ELEV_DEM`) and watershed-averaged precipitation (`AREAWTMAP`) with exponential, spherical, and Gaussian structures for the tail-up, tail-down, and Euclidean errors, respectively, and a nugget effect (by default). Using `ssn_lm()`, the model is fit:
+```{r}
+ssn_mod <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  tailup_type = "exponential",
+  taildown_type = "spherical",
+  euclid_type = "gaussian",
+  additive = "afvArea"
+)
+```
+
+The `additive` argument represents an "additive function value (AFV)" variable that captures branching in the stream network and is required when modeling the tail-up covariance. Cumulative watershed area is commonly used to derive the additive function value (here, `afvArea` represents cumulative watershed area), but other variables like flow can be used (if every line feature in the `edges` dataset contains a non-null value). @ver2010moving provide further details regarding additive function values.
+
+The `ssn_lm()` function is designed to be similar in syntax and structure to the `lm()` function in base **R** for fitting nonspatial linear models. Additionally, `SSN2` accommodates various S3 methods for commonly-used **R** generic functions that operate on model objects. For example, the generic function `summary()` is used to summarize the fitted model:
+```{r}
+summary(ssn_mod)
+```
+
+`SSN2` methods for the `tidy()`, `glance()`, and `augment()` generic functions from the `broom` **R** package [@robinson2021broom] are used to inspect the fitted model and provide diagnostics:
+```{r}
+tidy(ssn_mod, conf.int = TRUE)
+glance(ssn_mod)
+aug_mod <- augment(ssn_mod)
+subset(aug_mod, select = c(Summer_mn, .fitted, .resid, .hat, .cooksd))
+```
+
+Specific generic helper functions (e.g., `coef()`, `AIC()`, `residuals()`) can be used to obtain the same quantities returned by `tidy()`, `glance()`, and `augment()`:
+```{r}
+coef(ssn_mod)
+AIC(ssn_mod)
+head(residuals(ssn_mod))
+```
+
+Spatial prediction (i.e., Kriging) at the unobserved sites is performed using the generic functions `predict()` or `augment()`:
+```{r}
+aug_pred <- augment(ssn_mod, newdata = "pred1km", interval = "prediction")
+subset(aug_pred, select = c(.fitted, .lower, .upper))
+```
+
+Here, `.fitted` are the predictions, `.lower` are the lower bounds of 95% prediction intervals, and `.upper` are the upper bounds of 95% prediction intervals. Utilizing `augment()` makes the prediction output straightforward to visualize:
+
+```{r steam-preds, fig.cap="Predicted Middle Fork 2004 mean summer temperatures (Celsius) spaced one kilometer apart. As expected, temperature is predicted to be lower in areas of higher elevation.", out.width="85%", fig.align="center", warning=FALSE}
+ggplot() +
+    geom_sf(data = mf04p$edges) +
+    geom_sf(data = aug_pred, aes(color = .fitted), size = 2) +
+    scale_color_viridis_c(name = "Pred.", option = "H") +
+    theme_bw()
+```
+
+Spatial generalized linear models for binary, count, proportion, and skewed data [@ver2024marginal] are applied to stream networks via the `ssn_glm()` function. `ssn_lm()` and `ssn_glm()` also accommodate several advanced features, which include nonspatial random effects as in `lme4` [@bates2015lme4] and `nlme` [@pinheiro2006mixed] Euclidean anisotropy [@zimmerman2024spatial], and more. In addition to modeling, simulating data on a stream network is performed via `ssn_simulate()`. 
+
+# Discussion
+
+SSN models are valuable tools for statistical analysis of data collected on stream networks and help improve inference about vital stream ecosystems. These models have been employed to better understand and manage water quality [@scown2017improving; @mcmanus2020variation], ecosystem metabolism [@rodriguez2019estimating], and climate change impacts on freshwater ecosystems [@ruesch2012projected; @isaak2017norwest], as well as generate aquatic population estimates [@isaak2017scalable], inform conservation planning [@rodriguez2019spatial; @sharma2021dendritic], and assess restoration activities [@fuller2022riparian], among other applications. The breadth and applicability of SSN models are further enhanced by data aggregation tools like the National Hydrography Dataset [@mckay2012nhdplus], National Stream Internet Project [@nagel2015national], and StreamCat [@hill2016stream].
+
+There are several spatial modeling packages in **R**, including `geoR` [@ribiero2022geoR], `gstat` [@pebesma2004gstat], `FRK` [@sainsbury2024modeling], `fields` [@nychka2021fields], `R-INLA` [@lindgren2015bayesian], and `spmodel` [@dumelle2023spmodel], among others. However, these aforementioned spatial modeling packages do not account for the unique spatial relationships found in data collected on stream networks. The `rtop` [@skoien2014rtop], `VAST` [@charsley2023catchment], and `SSN2` **R** packages can be used to describe spatial stream network data in **R**, but `SSN2` is unique. It not only provides representations of stream network data in **R** but also provides an extensive suite of functions for model fitting, diagnostics, and spatial prediction that integrate with the popular "tidy" framework [@wickham2019welcome; @kuhn2022tidy]. To learn more about `SSN2`, visit the CRAN webpage at [https://CRAN.R-project.org/package=SSN2](https://CRAN.R-project.org/package=SSN2).
+
+# Acknowledgements
+
+Figures were created using `ggplot2` [@wickham2016ggplot2] and the `viridis` color palettes [@garnier2024viridis].
+
+<!-- Or, can use \nocite{wickham2016ggplot2, garnier2024viridis}. -->
+
+We would like to sincerely thank the editor and reviewers for all of their helpful feedback which greatly improved both the software and the manuscript.
+
+The views expressed in this manuscript are those of the authors and do not necessarily represent the views or policies of USEPA, NOAA, or USFS. Any mention of trade names, products, or services does not imply an endorsement by the U.S. government, USEPA, NOAA, or USFS. USEPA, NOAA, or USFS do not endorse any commercial products, services or enterprises.
+
+# References
diff --git a/inst/joss/paper.bib b/inst/joss/paper.bib
new file mode 100644
index 0000000..8355cd5
--- /dev/null
+++ b/inst/joss/paper.bib
@@ -0,0 +1,468 @@
+@Book{bivand2013applied,
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+}
+
+@article{bates2015lme4,
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+  volume = {67},
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+  doi = {10.18637/jss.v067.i01},
+}
+
+@Manual{bivand2020rgeos,
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+  author = {Roger Bivand and Colin Rundel},
+  year = {2020},
+  note = {R package version 0.5-5},
+  url = {https://CRAN.R-project.org/package=rgeos}
+}
+
+@Manual{bivand2021maptools,
+  title = {{maptools}: Tools for Handling Spatial Objects},
+  author = {Roger Bivand and Nicholas Lewin-Koh},
+  year = {2021},
+  note = {R package version 1.1-2},
+  url = {https://CRAN.R-project.org/package=maptools}
+}
+
+@Manual{bivand2021rgdal,
+  title = {{rgdal}: Bindings for the 'Geospatial' Data Abstraction Library},
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+  year = {2021},
+  note = {R package version 1.5-27},
+  url = {https://CRAN.R-project.org/package=rgdal}
+}
+
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+  doi={10.1016/j.fishres.2022.106583}
+}
+
+@book{cressie1993statistics,
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+}
+
+@article{detenbeck2016spatial,
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+}
+
+@Article{dumelle2023spmodel,
+  title = {{spmodel}: Spatial statistical modeling and prediction in {R}},
+  author = {Michael Dumelle and Matt Higham and Jay M. {Ver Hoef}},
+  journal = {PLOS ONE},
+  year = {2023},
+  volume = {18},
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+  doi = {10.1371/journal.pone.0282524}
+}
+
+
+@article{fuller2021integrating,
+  title={Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams},
+  author={Fuller, Matthew R and Ebersole, Joseph L and Detenbeck, Naomi E and Labiosa, Rochelle and Leinenbach, Peter and Torgersen, Christian E},
+  journal={Journal of Thermal Biology},
+  volume={100},
+  pages={103028},
+  year={2021},
+  publisher={Elsevier},
+  doi={10.1016/j.jtherbio.2021.103028}
+}
+
+@article{fuller2022riparian,
+  title={Riparian vegetation shade restoration and loss effects on recent and future stream temperatures},
+  author={Fuller, Matthew R and Leinenbach, Peter and Detenbeck, Naomi E and Labiosa, Rochelle and Isaak, Daniel J},
+  journal={Restoration Ecology},
+  volume={30},
+  number={7},
+  pages={e13626},
+  year={2022},
+  publisher={Wiley Online Library},
+  doi={10.1111/rec.13626}
+}
+
+@manual{garnier2024viridis,
+  title = {{viridis(Lite)} - Colorblind-Friendly Color Maps for {R}},
+  author = {{Garnier} and {Simon} and {Ross} and {Noam} and {Rudis} and {Robert} and {Camargo} and Antônio Pedro and {Sciaini} and {Marco} and {Scherer} and {Cédric}},
+  year = {2024},
+  note = {viridis package version 0.6.5},
+  url = {https://sjmgarnier.github.io/viridis/},
+  doi = {10.5281/zenodo.4679423},
+}
+
+@article{hill2016stream,
+  title={The Stream-Catchment (StreamCat) Dataset: A database of watershed metrics for the conterminous {United States}},
+  author={Hill, Ryan A and Weber, Marc H and Leibowitz, Scott G and Olsen, Anthony R and Thornbrugh, Darren J},
+  journal={JAWRA Journal of the American Water Resources Association},
+  volume={52},
+  number={1},
+  pages={120--128},
+  year={2016},
+  publisher={Wiley Online Library},
+  doi={10.1111/1752-1688.12372}
+}
+
+@article{isaak2014applications,
+  title={Applications of spatial statistical network models to stream data},
+  author={Isaak, Daniel J and Peterson, Erin E and {Ver Hoef}, Jay M and Wenger, Seth J and Falke, Jeffrey A and Torgersen, Christian E and Sowder, Colin and Steel, E Ashley and Fortin, Marie-Josee and Jordan, Chris E and others},
+  journal={Wiley Interdisciplinary Reviews: Water},
+  volume={1},
+  number={3},
+  pages={277--294},
+  year={2014},
+  publisher={Wiley Online Library},
+  doi={10.1002/wat2.1023}
+}
+
+@article{isaak2017norwest,
+  title={The NorWeST summer stream temperature model and scenarios for the western US: A crowd-sourced database and new geospatial tools foster a user community and predict broad climate warming of rivers and streams},
+  author={Isaak, Daniel J and Wenger, Seth J and Peterson, Erin E and {Ver Hoef}, Jay M and Nagel, David E and Luce, Charles H and Hostetler, Steven W and Dunham, Jason B and Roper, Brett B and Wollrab, Sherry P and others},
+  journal={Water Resources Research},
+  volume={53},
+  number={11},
+  pages={9181--9205},
+  year={2017},
+  publisher={Wiley Online Library},
+  doi={10.1002/2017WR020969}
+}
+
+@article{isaak2017scalable,
+  title={Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams},
+  author={Isaak, Daniel J and {Ver Hoef}, Jay M and Peterson, Erin E and Horan, Dona L and Nagel, David E},
+  journal={Canadian Journal of Fisheries and Aquatic Sciences},
+  volume={74},
+  number={2},
+  pages={147--156},
+  year={2017},
+  publisher={NRC Research Press},
+  doi={10.1139/cjfas-2016-0247}
+}
+
+@article{kattwinkel2020preparing,
+  title={Preparing GIS data for analysis of stream monitoring data: The {R} package openSTARS},
+  author={Kattwinkel, Mira and Sz{\"o}cs, Eduard and Peterson, Erin E and Sch{\"a}fer, Ralf B},
+  journal={PLOS ONE},
+  volume={15},
+  number={9},
+  pages={e0239237},
+  year={2020},
+  publisher={Public Library of Science San Francisco, CA USA},
+  doi={10.1371/journal.pone.0239237}
+}
+
+@book{kuhn2022tidy,
+  title={Tidy modeling with {R}},
+  author={Kuhn, Max and Silge, Julia},
+  year={2022},
+  publisher={O'Reilly Media, Inc.}
+}
+
+
+@article{lindgren2015bayesian,
+  title={Bayesian spatial modelling with {R-INLA}},
+  author={Lindgren, Finn and Rue, H{\aa}vard},
+  journal={Journal of Statistical Software},
+  volume={63},
+  number={19},
+  year={2015},
+  publisher={University of California at Los Angeles},
+  doi={10.18637/jss.v063.i19}
+}
+
+@manual{mckay2012nhdplus,
+  title = {NHDPlus Version 2: User Guide},
+  author={McKay, L. and Bondelid, T. and Dewald, T. and Johnston, J. and Moore, R. and Reah, A.},
+  year={2012},
+  url={http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php}
+}
+
+@article{mcmanus2020variation,
+  title={Variation in stream network relationships and geospatial predictions of watershed conductivity},
+  author={McManus, Michael G and D’Amico, Ellen and Smith, Elizabeth M and Polinsky, Robyn and Ackerman, Jerry and Tyler, Kip},
+  journal={Freshwater Science},
+  volume={39},
+  number={4},
+  pages={704--721},
+  year={2020},
+  publisher={The University of Chicago Press Chicago, IL},
+  doi={10.1086/710340}
+}
+
+@article{nagel2015national,
+  title={National Stream Internet protocol and user guide},
+  author={Nagel, David and Peterson, Erin E and Isaak, Daniel J and {Ver Hoef}, Jay M and Horan, Dona},
+  journal={US Forest Service, Rocky Mountain Research Station Air, Water, and Aquatic Environments Program},
+  year={2015},
+  url={https://research.fs.usda.gov/sites/default/files/2023-03/rmrs-nationalstreaminternetprotocolanduserguide.pdf}
+}
+
+@article{neill2018using,
+  title={Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment},
+  author={Neill, Aaron James and Tetzlaff, Doerthe and Strachan, Norval James Colin and Hough, Rupert Lloyd and Avery, Lisa Marie and Watson, Helen and Soulsby, Chris},
+  journal={Science of the Total Environment},
+  volume={612},
+  pages={840--852},
+  year={2018},
+  publisher={Elsevier},
+  doi={10.1016/j.scitotenv.2017.08.151}
+}
+
+@online{nowosad2023,
+  author = {Nowosad, Jakub},
+  title = {Upcoming Changes to Popular {R} Packages for Spatial Data:
+    What You Need to Do},
+  date = {2023-06-04},
+  url = {https://geocompx.org//post/2023/rgdal-retirement},
+  langid = {en}
+}
+
+@Misc{nychka2021fields,
+  title = {{fields}: Tools for spatial data},
+  author = {Nychka, Douglas and Furrer, Reinhard and Paige, John and Sain, Stephan},
+  note = {R package version 15.2},
+  organization = {University Corporation for Atmospheric Research},
+  address = {Boulder, CO, USA},
+  year = {2021},
+  url = {https://github.com/dnychka/fieldsRPackage},
+  doi = {10.32614/CRAN.package.fields}
+}
+
+@Article{pebesma2004gstat,
+  title = {Multivariable geostatistics in {S}: the gstat package},
+  author = {Edzer Pebesma},
+  journal = {Computers & Geosciences},
+  year = {2004},
+  volume = {30},
+  pages = {683-691},
+  doi = {10.1016/j.cageo.2004.03.012}
+}
+
+@article{pebesma2018sf,
+  author = {Edzer Pebesma},
+  title = {{Simple Features for {R}: Standardized Support for Spatial Vector Data}},
+  year = {2018},
+  journal = {{The R Journal}},
+  doi = {10.32614/RJ-2018-009},
+  url = {https://doi.org/10.32614/RJ-2018-009},
+  pages = {439--446},
+  volume = {10},
+  number = {1},
+}
+
+@article{peterson2010mixed,
+  title={A mixed-model moving-average approach to geostatistical modeling in stream networks},
+  author={Peterson, Erin E and {Ver Hoef}, Jay M},
+  journal={Ecology},
+  volume={91},
+  number={3},
+  pages={644--651},
+  year={2010},
+  publisher={Wiley Online Library},
+  doi={10.1890/08-1668.1}
+}
+
+@article{peterson2014stars,
+  title={{STARS}: An {ArcGIS} toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data},
+  author={Peterson, Erin E and {Ver Hoef}, Jay M},
+  journal={Journal of Statistical Software},
+  volume={56},
+  pages={1--17},
+  year={2014},
+  doi={10.18637/jss.v056.i02}
+}
+
+@manual{peterson2024SSNbler,
+  title = {{SSNbler}: Assemble SSN objects in {R}},
+  author = {Erin E Peterson and Michael Dumelle and Alan Pearse and Dan Teleki and Jay M {Ver Hoef}},
+  year = {2024},
+  note = {{R} package version 0.0.1},
+  url = {https://github.com/pet221/SSNbler}
+}
+
+@book{pinheiro2006mixed,
+  title={Mixed-effects models in {S} and {S-PLUS}},
+  author={Pinheiro, Jos{\'e} and Bates, Douglas},
+  year={2006},
+  publisher={Springer Science \& Business Media: New York, NY}
+}
+
+@article{ruesch2012projected,
+  title={Projected climate-induced habitat loss for salmonids in the {J}ohn {D}ay {R}iver {N}etwork, {O}regon, USA},
+  author={Ruesch, Aaron S and Torgersen, Christian E and Lawler, Joshua J and Olden, Julian D and Peterson, Erin E and Volk, Carol J and Lawrence, David J},
+  journal={Conservation Biology},
+  volume={26},
+  number={5},
+  pages={873--882},
+  year={2012},
+  publisher={Wiley Online Library},
+  doi={10.1111/j.1523-1739.2012.01897.x}
+}
+
+@Manual{ribiero2022geoR,
+  title = {{geoR}: Analysis of Geostatistical Data},
+  author = {Paulo Justiniano {Ribeiro Jr} and Peter Diggle and Ole Christensen and Martin Schlather and Roger Bivand and Brian Ripley},
+  year = {2022},
+  note = {R package version 1.9-2},
+  url = {https://CRAN.R-project.org/package=geoR},
+  doi = {10.32614/CRAN.package.geoR}
+}
+
+@article{rodriguez2019spatial,
+  title={A spatial stream-network approach assists in managing the remnant genetic diversity of riparian forests},
+  author={Rodr{\'\i}guez-Gonz{\'a}lez, Patricia Mar{\'\i}a and Garc{\'\i}a, Cristina and Albuquerque, Ant{\'o}nio and Monteiro-Henriques, Tiago and Faria, Carla and Guimar{\~a}es, Joana B and Mendon{\c{c}}a, Diogo and Sim{\~o}es, Fernanda and Ferreira, Maria Teresa and Mendes, Ana and others},
+  journal={Scientific Reports},
+  volume={9},
+  number={1},
+  pages={6741},
+  year={2019},
+  publisher={Nature Publishing Group UK London},
+  doi={10.1038/s41598-019-43132-7}
+}
+
+@article{rodriguez2019estimating,
+  title={Estimating ecosystem metabolism to entire river networks},
+  author={Rodr{\'\i}guez-Castillo, Tamara and Est{\'e}vez, Edurne and Gonz{\'a}lez-Ferreras, Alexia Mar{\'\i}a and Barqu{\'\i}n, Jos{\'e}},
+  journal={Ecosystems},
+  volume={22},
+  pages={892--911},
+  year={2019},
+  publisher={Springer},
+  doi={10.1007/s10021-018-0311-8}
+}
+
+@Manual{robinson2021broom,
+  title = {{broom}: Convert Statistical Objects into Tidy Tibbles},
+  author = {Robinson, David and Hayes, Alex and Couch, Simon},
+  year = {2021},
+  note = {R package version 0.7.6},
+  url = {https://CRAN.R-project.org/package=broom},
+  doi = {10.32614/CRAN.package.broom}
+
+}
+
+@article{sainsbury2024modeling,
+  title={Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK},
+  author={Sainsbury-Dale, Matthew and Zammit-Mangion, Andrew and Cressie, Noel},
+  journal={Journal of Statistical Software},
+  volume={108},
+  pages={1--39},
+  year={2024},
+  doi={10.18637/jss.v108.i10}
+}
+
+@article{scown2017improving,
+  title={Improving predictive models of in-stream phosphorus concentration based on nationally-available spatial data coverages},
+  author={Scown, Murray W and McManus, Michael G and Carson Jr, John H and Nietch, Christopher T},
+  journal={Journal of the American Water Resources Association},
+  volume={53},
+  number={4},
+  pages={944--960},
+  year={2017},
+  publisher={Wiley Online Library},
+  doi={10.1111/1752-1688.12543}
+}
+
+@article{sharma2021dendritic,
+  title={Dendritic prioritization through spatial stream network modeling informs targeted management of {H}imalayan riverscapes under brown trout invasion},
+  author={Sharma, Aashna and Dubey, Vineet K and Johnson, Jeyaraj A and Rawal, Yogesh K and Sivakumar, Kuppusamy},
+  journal={Journal of Applied Ecology},
+  volume={58},
+  number={11},
+  pages={2415--2426},
+  year={2021},
+  publisher={Wiley Online Library},
+  doi={10.1111/1365-2664.13997}
+}
+
+@Article{skoien2014rtop,
+  title = {{rtop}: An {R} package for interpolation of data with a variable spatial support, with an example from river networks},
+  author = {J. O. Skoien and {G. Bloschl} and {G. Laaha} and {E. Pebesma} and {J. Parajka} and {A. Viglione}},
+  journal = {Computers \& Geosciences},
+  year = {2014},
+  doi = {10.1016/j.cageo.2014.02.009}
+}
+
+@article{ver2010moving,
+  title={A moving average approach for spatial statistical models of stream networks},
+  author={{Ver Hoef}, Jay M and Peterson, Erin E},
+  journal={Journal of the American Statistical Association},
+  volume={105},
+  number={489},
+  pages={6--18},
+  year={2010},
+  publisher={Taylor \& Francis},
+  doi={10.1198/jasa.2009.ap08248}
+}
+
+@article{ver2014ssn,
+  title={{SSN}: An {R} package for spatial statistical modeling on stream networks},
+  author={{Ver Hoef}, Jay M and Peterson, Erin E and Clifford, David and Shah, Rohan},
+  journal={Journal of Statistical Software},
+  volume={56},
+  pages={1--45},
+  year={2014},
+  doi={10.18637/jss.v056.i03}
+}
+
+@article{ver2024marginal,
+  title={Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation},
+  author={{Ver Hoef}, Jay M and Blagg, Eryn and Dumelle, Michael and Dixon, Phillip M and Zimmerman, Dale L and Conn, Paul B},
+  journal={Environmetrics},
+  year={2024},
+  doi={10.1002/env.2872}
+}
+
+@Book{wickham2016ggplot2,
+  author = {Hadley Wickham},
+  title = {{ggplot2}: Elegant Graphics for Data Analysis},
+  publisher = {Springer-Verlag New York},
+  year = {2016},
+  isbn = {978-3-319-24277-4},
+  url = {https://ggplot2.tidyverse.org},
+  doi={10.1007/978-0-387-98141-3}
+}
+
+@article{wickham2019welcome,
+  title={Welcome to the {T}idyverse},
+  author={Wickham, Hadley and Averick, Mara and Bryan, Jennifer and Chang, Winston and McGowan, Lucy D'Agostino and Fran{\c{c}}ois, Romain and Grolemund, Garrett and Hayes, Alex and Henry, Lionel and Hester, Jim and others},
+  journal={Journal of Open Source Software},
+  volume={4},
+  number={43},
+  pages={1686},
+  year={2019},
+  doi={10.21105/joss.01686}
+}
+
+@book{zimmerman2024spatial,
+  title={Spatial Linear Models for Environmental Data},
+  author={Zimmerman, Dale L and {Ver Hoef}, Jay M},
+  year={2024},
+  publisher={CRC Press}
+}
diff --git a/inst/joss/paper.log b/inst/joss/paper.log
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@@ -0,0 +1,1128 @@
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+entering extended mode
+ restricted \write18 enabled.
+ %&-line parsing enabled.
+**paper.tex
+(./paper.tex
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diff --git a/inst/joss/paper.md b/inst/joss/paper.md
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+---
+title: 'SSN2: The next generation of spatial stream network modeling in R'
+tags:
+  - R
+  - Spatial stream network
+  - Spatial correlation
+  - Spatial prediction (Kriging)
+  - Geostatistics
+  - Generalized linear model
+  - Torgegram
+authors:
+  - name: Michael Dumelle
+    orcid: 0000-0002-3393-5529
+    affiliation: "1" # (Multiple affiliations must be quoted)
+  - name: Erin E. Peterson
+    orcid: 0000-0003-2992-0372
+    affiliation: 2
+  - name: Jay M. Ver Hoef
+    orcid: 0000-0003-4302-6895
+    affiliation: 3
+  - name: Alan Pearse
+    orcid: 0000-0002-4133-8548 
+    affiliation: 4
+  - name: Daniel J. Isaak
+    affiliation: 5
+affiliations:
+ - name: Pacific Ecological Systems Division, United States Environmental Protection Agency, Corvallis, OR, USA
+   index: 1
+ - name: EP Consulting and Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
+   index: 2
+ - name: NMFS Alaska Fisheries Science Center, United States National Oceanic and Atmospheric Administration, Seattle, WA, USA
+   index: 3
+ - name: NIASRA, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia
+   index: 4
+ - name: Rocky Mountain Research Station, United States Forest Service, Boise, ID, USA
+   index: 5
+citation_author: Dumelle et. al.
+date: 07 July 2024
+year: 2024
+bibliography: paper.bib
+output: rticles::joss_article
+csl: apa.csl
+journal: JOSS
+editor_options: 
+  chunk_output_type: console
+---
+
+# Summary
+
+The `SSN2` **R** package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. `SSN2` is the successor to the `SSN` **R** package [@ver2014ssn], which was archived alongside broader changes in the **R**-spatial ecosystem [@nowosad2023] that included 1) the retirement of `rgdal` [@bivand2021rgdal], `rgeos` [@bivand2020rgeos], and `maptools` [@bivand2021maptools] and 2) the lack of active development of `sp` [@bivand2013applied]. `SSN2` maintains compatibility with the input data file structures used by the `SSN` **R** package but leverages modern **R**-spatial tools like `sf` [@pebesma2018sf]. `SSN2` also provides many useful features that were not available in the `SSN` **R** package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other **R** generic functions.
+
+# Statement of Need
+
+Streams provide vital aquatic services that sustain wildlife, provide drinking and irrigation water, and support recreational and cultural activities.  Data are often collected at various locations on a stream network and used to characterize spatial patterns in stream phenomena. For example, a manager may need to know how the amount of a hazardous chemical changes throughout a stream network to inform mitigation efforts. Comprehensive formulations of spatial stream network (SSN) models are provided by @ver2010moving, @peterson2010mixed, and @ver2014ssn. The `SSN2` **R** package is designed to help users fit SSN models to their stream network data.
+
+SSN models use a spatial statistical modeling framework [e.g., @cressie1993statistics] to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume. These SSN models relate a continuous or discrete response variable to one or more explanatory variables, a spatially independent random error term, and up to three spatially dependent random error terms: tail-up random errors, tail-down random errors, and Euclidean random errors. Tail-up random errors restrict spatial dependence to flow-connected sites (i.e., water flows from an upstream to a downstream site) and incorporate spatial weights through an additive function to describe the branching network between sites. Tail-down random errors describe spatial dependence between both flow-connected and flow-unconnected sites (i.e., sites that share a common downstream junction but not flow), but spatial weights are not required. Euclidean random errors describe spatial dependence between sites based on straight-line distance and are governed by factors not confined to the stream network, such as regional geology. The variances and the length-scales of spatial dependence in the tail-up, tail-down, and Euclidean random errors are controlled by separate variance (i.e., partial sill) and range parameters, respectively, while the spatially independent variance (i.e., nugget) is controlled by another separate variance parameter. In this paper, we show how to use the `SSN2` **R** package to fit SSN models, inspect SSN models, and use SSN models to make predictions at unobserved locations on a stream network. 
+
+# Package Overview
+
+The streams, observation, and prediction datasets must be pre-processed prior to fitting SSN models and making predictions at unobserved locations using `SSN2`. Previously, the STARS toolset for ArcGIS Desktop versions 9.3x - 10.8x [@peterson2014stars] or the `openSTARS` **R** package [@kattwinkel2020preparing] were used to generate spatial information required for model fitting and prediction. However, both software packages have recently been retired and are replaced by the `SSNbler` **R** package [@peterson2024SSNbler], which is a new, **R**-based version of the STARS tools. `SSNbler` is currently available on GitHub, will soon be available on CRAN, and contains several useful resources that guide users through these pre-processing steps. Pre-processing using either `SSNbler`, STARS, or `openSTARS` ends with the creation of a `.ssn` folder, which is non-proprietary. Files residing in the `.ssn` folder are read into R using `ssn_import()` from `SSN2` and placed into a list structure called an SSN object, which contains all the spatial, topological, and attribute information needed to leverage the modeling tools in `SSN2`.
+
+`SSN2` is first installed from CRAN:
+
+``` r
+install.packages("SSN2")
+```
+
+Then, `SSN2` is loaded into an **R** session:
+
+``` r
+library(SSN2)
+```
+
+The `SSN2` package comes with an example `.ssn` folder called `MiddleFork04.ssn` that represents water temperatures recorded from a stream network in the Middle Fork of the Salmon River in Idaho, USA during 2004. 
+
+Several functions in `SSN2` for reading and writing data directly manipulate the `.ssn` folder. To avoid directly manipulating the `MiddleFork04.ssn` data installed alongside `SSN2`, `MiddleFork04.ssn` is instead copied into a temporary directory and the relevant path to this directory stored:
+
+``` r
+copy_lsn_to_temp()
+path <- file.path(tempdir(), "MiddleFork04.ssn")
+```
+
+The `copy_lsn_to_temp()` function is only used when working with `MiddleFork04.ssn` and generally, `path` should indicate a permanent directory on your computer that points towards your `.ssn` object. After specifying `path`, the stream reaches, observed sites, and prediction sites (`pred1km`) are imported and then visualized (Figure$~$\ref{fig:steam-network}):
+
+``` r
+mf04p <- ssn_import(path, predpts = "pred1km")
+```
+
+
+``` r
+library(ggplot2)
+ggplot() +
+  geom_sf(data = mf04p$edges) +
+  geom_sf(data = mf04p$preds$pred1km, pch = 17, color = "blue") +
+  geom_sf(data = mf04p$obs, color = "brown", size = 2) +
+  theme_bw()
+```
+
+\begin{figure}
+
+{\centering \includegraphics[width=0.85\linewidth]{paper_files/figure-latex/steam-network-1} 
+
+}
+
+\caption{Middle Fork 2004 stream networks. Observed sites are represented by brown, closed circles at various locations throughout the stream network. Prediction sites are represented by blue, closed triangles and are spaced one kilometer apart.}\label{fig:steam-network}
+\end{figure}
+
+Prior to statistical modeling, hydrologic distance matrices are created [@ver2010moving]:
+
+``` r
+ssn_create_distmat(mf04p, predpts = "pred1km", overwrite = TRUE)
+```
+
+Of particular interest here is summer mean stream temperature (`Summer_mn`) in degrees Celsius, 
+which will be modeled as a function of elevation (`ELEV_DEM`) and watershed-averaged precipitation (`AREAWTMAP`) with exponential, spherical, and Gaussian structures for the tail-up, tail-down, and Euclidean errors, respectively, and a nugget effect (by default). Using `ssn_lm()`, the model is fit:
+
+``` r
+ssn_mod <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  tailup_type = "exponential",
+  taildown_type = "spherical",
+  euclid_type = "gaussian",
+  additive = "afvArea"
+)
+```
+
+The `additive` argument represents an "additive function value (AFV)" variable that captures branching in the stream network and is required when modeling the tail-up covariance. Cumulative watershed area is commonly used to derive the additive function value (here, `afvArea` represents cumulative watershed area), but other variables like flow can be used (if every line feature in the `edges` dataset contains a non-null value). @ver2010moving provide further details regarding additive function values.
+
+The `ssn_lm()` function is designed to be similar in syntax and structure to the `lm()` function in base **R** for fitting nonspatial linear models. Additionally, `SSN2` accommodates various S3 methods for commonly-used **R** generic functions that operate on model objects. For example, the generic function `summary()` is used to summarize the fitted model:
+
+``` r
+summary(ssn_mod)
+```
+
+```
+## 
+## Call:
+## ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p, 
+##     tailup_type = "exponential", taildown_type = "spherical", 
+##     euclid_type = "gaussian", additive = "afvArea")
+## 
+## Residuals:
+##     Min      1Q  Median      3Q     Max 
+## -3.6393 -2.0646 -0.5952  0.2143  0.7497 
+## 
+## Coefficients (fixed):
+##              Estimate Std. Error z value Pr(>|z|)    
+## (Intercept) 76.195041   7.871574   9.680  < 2e-16 ***
+## ELEV_DEM    -0.026905   0.003646  -7.379  1.6e-13 ***
+## AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *  
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Pseudo R-squared: 0.6124
+## 
+## Coefficients (covariance):
+##               Effect     Parameter   Estimate
+##   tailup exponential  de (parsill)  3.800e+00
+##   tailup exponential         range  4.194e+06
+##   taildown spherical  de (parsill)  4.480e-01
+##   taildown spherical         range  1.647e+05
+##      euclid gaussian  de (parsill)  1.509e-02
+##      euclid gaussian         range  4.496e+03
+##               nugget        nugget  2.087e-02
+```
+
+`SSN2` methods for the `tidy()`, `glance()`, and `augment()` generic functions from the `broom` **R** package [@robinson2021broom] are used to inspect the fitted model and provide diagnostics:
+
+``` r
+tidy(ssn_mod, conf.int = TRUE)
+```
+
+```
+## # A tibble: 3 x 7
+##   term        estimate std.error statistic  p.value conf.low conf.high
+##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
+## 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6     
+## 2 AREAWTMAP   -0.00910   0.00446     -2.04 4.14e- 2  -0.0178 -0.000356
+## 3 ELEV_DEM    -0.0269    0.00365     -7.38 1.60e-13  -0.0341 -0.0198
+```
+
+``` r
+glance(ssn_mod)
+```
+
+```
+## # A tibble: 1 x 9
+##       n     p  npar value   AIC  AICc logLik deviance pseudo.r.squared
+##   <int> <dbl> <int> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl>
+## 1    45     3     7  59.3  73.3  76.3  -29.6     41.9            0.612
+```
+
+``` r
+aug_mod <- augment(ssn_mod)
+subset(aug_mod, select = c(Summer_mn, .fitted, .resid, .hat, .cooksd))
+```
+
+```
+## Simple feature collection with 45 features and 5 fields
+## Geometry type: POINT
+## Dimension:     XY
+## Bounding box:  xmin: -1530805 ymin: 2527111 xmax: -1503079 ymax: 2537823
+## Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version
+## # A tibble: 45 x 6
+##    Summer_mn .fitted .resid   .hat  .cooksd           geometry
+##        <dbl>   <dbl>  <dbl>  <dbl>    <dbl>        <POINT [m]>
+##  1     11.4     14.4  -3.07 0.0915 0.0962   (-1512690 2531883)
+##  2     10.7     12.9  -2.20 0.114  0.00471  (-1512852 2531295)
+##  3     10.4     12.7  -2.25 0.0372 0.00724  (-1513400 2530706)
+##  4     10.1     12.3  -2.18 0.0251 0.00153  (-1514027 2530147)
+##  5     10.1     12.3  -2.13 0.0374 0.000583 (-1514309 2529902)
+##  6      9.81    12.0  -2.16 0.0602 0.0150   (-1515032 2529461)
+##  7      9.76    11.6  -1.85 0.0736 0.00739  (-1515513 2528810)
+##  8      9.77    11.6  -1.84 0.0648 0.00687  (-1515588 2528592)
+##  9      9.53    11.4  -1.87 0.112  0.00152  (-1516440 2527899)
+## 10     12.6     14.9  -2.28 0.0498 0.00964  (-1512464 2531195)
+## # i 35 more rows
+```
+
+Specific generic helper functions (e.g., `coef()`, `AIC()`, `residuals()`) can be used to obtain the same quantities returned by `tidy()`, `glance()`, and `augment()`:
+
+``` r
+coef(ssn_mod)
+```
+
+```
+## (Intercept)    ELEV_DEM   AREAWTMAP 
+## 76.19504087 -0.02690478 -0.00909941
+```
+
+``` r
+AIC(ssn_mod)
+```
+
+```
+## [1] 73.2623
+```
+
+``` r
+head(residuals(ssn_mod))
+```
+
+```
+##         1         2         3         4         5         6 
+## -3.066413 -2.204147 -2.252004 -2.175337 -2.131527 -2.162417
+```
+
+Spatial prediction (i.e., Kriging) at the unobserved sites is performed using the generic functions `predict()` or `augment()`:
+
+``` r
+aug_pred <- augment(ssn_mod, newdata = "pred1km", interval = "prediction")
+subset(aug_pred, select = c(.fitted, .lower, .upper))
+```
+
+```
+## Simple feature collection with 175 features and 3 fields
+## Geometry type: POINT
+## Dimension:     XY
+## Bounding box:  xmin: -1530631 ymin: 2521707 xmax: -1500020 ymax: 2540253
+## Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version
+## # A tibble: 175 x 4
+##    .fitted .lower .upper           geometry
+##      <dbl>  <dbl>  <dbl>        <POINT [m]>
+##  1    14.6   14.3   15.0 (-1520657 2536657)
+##  2    15.0   14.7   15.4 (-1519866 2536812)
+##  3    14.8   14.3   15.3 (-1521823 2536911)
+##  4    15.0   14.5   15.5 (-1523183 2537256)
+##  5    15.2   14.7   15.6 (-1523860 2537452)
+##  6    15.1   14.8   15.5 (-1525443 2537698)
+##  7    15.1   14.7   15.5 (-1526397 2537254)
+##  8    15.0   14.6   15.4 (-1527436 2536803)
+##  9    14.9   14.6   15.3 (-1529043 2536449)
+## 10    14.9   14.5   15.3 (-1529689 2537313)
+## # i 165 more rows
+```
+
+Here, `.fitted` are the predictions, `.lower` are the lower bounds of 95% prediction intervals, and `.upper` are the upper bounds of 95% prediction intervals. Utilizing `augment()` makes the prediction output straightforward to visualize:
+
+
+``` r
+ggplot() +
+    geom_sf(data = mf04p$edges) +
+    geom_sf(data = aug_pred, aes(color = .fitted), size = 2) +
+    scale_color_viridis_c(name = "Pred.", option = "H") +
+    theme_bw()
+```
+
+\begin{figure}
+
+{\centering \includegraphics[width=0.85\linewidth]{paper_files/figure-latex/steam-preds-1} 
+
+}
+
+\caption{Predicted Middle Fork 2004 mean summer temperatures (Celsius) spaced one kilometer apart. As expected, temperature is predicted to be lower in areas of higher elevation.}\label{fig:steam-preds}
+\end{figure}
+
+Spatial generalized linear models for binary, count, proportion, and skewed data [@ver2024marginal] are applied to stream networks via the `ssn_glm()` function. `ssn_lm()` and `ssn_glm()` also accommodate several advanced features, which include nonspatial random effects as in `lme4` [@bates2015lme4] and `nlme` [@pinheiro2006mixed] Euclidean anisotropy [@zimmerman2024spatial], and more. In addition to modeling, simulating data on a stream network is performed via `ssn_simulate()`. 
+
+# Discussion
+
+SSN models are valuable tools for statistical analysis of data collected on stream networks and help improve inference about vital stream ecosystems. These models have been employed to better understand and manage water quality [@scown2017improving; @mcmanus2020variation], ecosystem metabolism [@rodriguez2019estimating], and climate change impacts on freshwater ecosystems [@ruesch2012projected; @isaak2017norwest], as well as generate aquatic population estimates [@isaak2017scalable], inform conservation planning [@rodriguez2019spatial; @sharma2021dendritic], and assess restoration activities [@fuller2022riparian], among other applications. The breadth and applicability of SSN models are further enhanced by data aggregation tools like the National Hydrography Dataset [@mckay2012nhdplus], National Stream Internet Project [@nagel2015national], and StreamCat [@hill2016stream].
+
+There are several spatial modeling packages in **R**, including `geoR` [@ribiero2022geoR], `gstat` [@pebesma2004gstat], `FRK` [@sainsbury2024modeling], `fields` [@nychka2021fields], `R-INLA` [@lindgren2015bayesian], and `spmodel` [@dumelle2023spmodel], among others. However, these aforementioned spatial modeling packages do not account for the unique spatial relationships found in data collected on stream networks. The `rtop` [@skoien2014rtop], `VAST` [@charsley2023catchment], and `SSN2` **R** packages can be used to describe spatial stream network data in **R**, but `SSN2` is unique. It not only provides representations of stream network data in **R** but also provides an extensive suite of functions for model fitting, diagnostics, and spatial prediction that integrate with the popular "tidy" framework [@wickham2019welcome; @kuhn2022tidy]. To learn more about `SSN2`, visit the CRAN webpage at [https://CRAN.R-project.org/package=SSN2](https://CRAN.R-project.org/package=SSN2).
+
+# Acknowledgements
+
+Figures were created using `ggplot2` [@wickham2016ggplot2] and the `viridis` color palettes [@garnier2024viridis].
+
+<!-- Or, can use \nocite{wickham2016ggplot2, garnier2024viridis}. -->
+
+We would like to sincerely thank the editor and reviewers for all of their helpful feedback which greatly improved both the software and the manuscript.
+
+The views expressed in this manuscript are those of the authors and do not necessarily represent the views or policies of USEPA, NOAA, or USFS. Any mention of trade names, products, or services does not imply an endorsement by the U.S. government, USEPA, NOAA, or USFS. USEPA, NOAA, or USFS do not endorse any commercial products, services or enterprises.
+
+# References
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--- a/inst/lsndata/MiddleFork04.ssn/netID1.dat
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-"rid", "binaryID"
-3,1
-4,10
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-6,1000
-81,1001
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-7,10000
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-86,100010
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diff --git a/inst/lsndata/MiddleFork04.ssn/netID2.dat b/inst/lsndata/MiddleFork04.ssn/netID2.dat
index 0c4a602..80097b0 100644
--- a/inst/lsndata/MiddleFork04.ssn/netID2.dat
+++ b/inst/lsndata/MiddleFork04.ssn/netID2.dat
@@ -1,112 +1,112 @@
-"rid", "binaryID"
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diff --git a/inst/lsndata/MiddleFork04.ssn/pred1km.dbf b/inst/lsndata/MiddleFork04.ssn/pred1km.dbf
deleted file mode 100644
index 14bcace..0000000
Binary files a/inst/lsndata/MiddleFork04.ssn/pred1km.dbf and /dev/null differ
diff --git a/inst/lsndata/MiddleFork04.ssn/pred1km.gpkg b/inst/lsndata/MiddleFork04.ssn/pred1km.gpkg
new file mode 100644
index 0000000..dfc74f2
Binary files /dev/null and b/inst/lsndata/MiddleFork04.ssn/pred1km.gpkg differ
diff --git a/inst/lsndata/MiddleFork04.ssn/pred1km.prj b/inst/lsndata/MiddleFork04.ssn/pred1km.prj
deleted file mode 100644
index d4b9fa9..0000000
--- a/inst/lsndata/MiddleFork04.ssn/pred1km.prj
+++ /dev/null
@@ -1 +0,0 @@
-PROJCS["USA_Contiguous_Albers_Equal_Area_Conic_USGS_version",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-96.0],PARAMETER["Standard_Parallel_1",29.5],PARAMETER["Standard_Parallel_2",45.5],PARAMETER["Latitude_Of_Origin",23.0],UNIT["Meter",1.0]]
\ No newline at end of file
diff --git a/inst/lsndata/MiddleFork04.ssn/pred1km.shp b/inst/lsndata/MiddleFork04.ssn/pred1km.shp
deleted file mode 100644
index c8698d5..0000000
Binary files a/inst/lsndata/MiddleFork04.ssn/pred1km.shp and /dev/null differ
diff --git a/inst/lsndata/MiddleFork04.ssn/pred1km.shx b/inst/lsndata/MiddleFork04.ssn/pred1km.shx
deleted file mode 100644
index ecad2bd..0000000
Binary files a/inst/lsndata/MiddleFork04.ssn/pred1km.shx and /dev/null differ
diff --git a/inst/lsndata/MiddleFork04.ssn/sites.dbf b/inst/lsndata/MiddleFork04.ssn/sites.dbf
deleted file mode 100644
index 50abe1f..0000000
Binary files a/inst/lsndata/MiddleFork04.ssn/sites.dbf and /dev/null differ
diff --git a/inst/lsndata/MiddleFork04.ssn/sites.gpkg b/inst/lsndata/MiddleFork04.ssn/sites.gpkg
new file mode 100644
index 0000000..d73e268
Binary files /dev/null and b/inst/lsndata/MiddleFork04.ssn/sites.gpkg differ
diff --git a/inst/lsndata/MiddleFork04.ssn/sites.prj b/inst/lsndata/MiddleFork04.ssn/sites.prj
deleted file mode 100644
index d4b9fa9..0000000
--- a/inst/lsndata/MiddleFork04.ssn/sites.prj
+++ /dev/null
@@ -1 +0,0 @@
-PROJCS["USA_Contiguous_Albers_Equal_Area_Conic_USGS_version",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-96.0],PARAMETER["Standard_Parallel_1",29.5],PARAMETER["Standard_Parallel_2",45.5],PARAMETER["Latitude_Of_Origin",23.0],UNIT["Meter",1.0]]
\ No newline at end of file
diff --git a/inst/lsndata/MiddleFork04.ssn/sites.shp b/inst/lsndata/MiddleFork04.ssn/sites.shp
deleted file mode 100644
index 893084b..0000000
Binary files a/inst/lsndata/MiddleFork04.ssn/sites.shp and /dev/null differ
diff --git a/inst/lsndata/MiddleFork04.ssn/sites.shx b/inst/lsndata/MiddleFork04.ssn/sites.shx
deleted file mode 100644
index 9c82ac4..0000000
Binary files a/inst/lsndata/MiddleFork04.ssn/sites.shx and /dev/null differ
diff --git a/inst/references.bib b/inst/references.bib
index 40f31ab..db7c07f 100644
--- a/inst/references.bib
+++ b/inst/references.bib
@@ -87,6 +87,17 @@ @book{cressie2015statistics
   publisher={John Wiley \& Sons}
 }
 
+@article{dumelle2023spmodel,
+  title={spmodel: Spatial statistical modeling and prediction in R},
+  author={Dumelle, Michael and Higham, Matt and Ver Hoef, Jay M},
+  journal={Plos one},
+  volume={18},
+  number={3},
+  pages={e0282524},
+  year={2023},
+  publisher={Public Library of Science San Francisco, CA USA}
+}
+
 @book{faraway2016extending,
   title={Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models},
   author={Faraway, Julian J},
@@ -326,6 +337,14 @@ @article{peterson2014stars
   year={2014}
 }
 
+@manual{peterson2024SSNbler,
+  title = {{SSNbler}: Assemble SSN objects in {R}},
+  author = {Erin E. Peterson and Michael Dumelle and Alan Pearse and Dan Teleki and Jay M. {Ver Hoef}},
+  year = {2024},
+  note = {{R} package version 0.0.1},
+  url = {https://github.com/pet221/SSNbler}
+}
+
 @book{pinheiro2006mixed,
   title={Mixed-effects models in S and S-PLUS},
   author={Pinheiro, Jos{\'e} and Bates, Douglas},
diff --git a/inst/tutorials/demos/SSN2-introduction.R b/inst/tutorials/demos/SSN2-introduction.R
new file mode 100644
index 0000000..10fed1c
--- /dev/null
+++ b/inst/tutorials/demos/SSN2-introduction.R
@@ -0,0 +1,341 @@
+#:::::::::::::::::::::::::::::::::::::
+# Data Processing ####
+#:::::::::::::::::::::::::::::::::::::
+
+# Install packages ####
+# Remove comment (#) to install packages
+# remotes::install_github("USEPA/SSN2", ref = "develop")
+# install.packages("ggplot2")
+# install.packages("sf")
+
+# Load packages ####
+library(SSN2)
+library(ggplot2)
+library(sf)
+
+# Copy Data Folder ####
+## copy MiddleFork04 to the temporary directory
+copy_lsn_to_temp()
+## Find the path to the MiddleFork04 in the temporary directory
+path <- paste0(tempdir(), "/MiddleFork04.ssn")
+
+# Import MiddleFork04 SSN ####
+## Import data and prediction points
+mf04p <- ssn_import(
+  path = path,
+  predpts = c("pred1km", "CapeHorn"),
+  overwrite = TRUE
+)
+## print a summary
+summary(mf04p)
+## print names of prediction data
+names(mf04p$preds)
+## visualize stream network, prediction data, observed data
+ggplot() +
+  geom_sf(data = mf04p$edges) +
+  geom_sf(data = mf04p$preds$pred1km, pch = 17, color = "blue") +
+  geom_sf(data = mf04p$obs, color = "brown", size = 2) +
+  theme_bw()
+## create distance matrices needed for spatial statistical modeling
+ssn_create_distmat(
+  ssn.object = mf04p,
+  predpts = c("pred1km", "CapeHorn"),
+  among_predpts = TRUE,
+  overwrite = TRUE
+)
+
+#:::::::::::::::::::::::::::::::::::::
+# Statistical Modeling ####
+#:::::::::::::::::::::::::::::::::::::
+
+# Exploratory Visualizations ####
+## Visualize temperature spatially
+ggplot() +
+  geom_sf(data = mf04p$edges) +
+  geom_sf(data = mf04p$obs, aes(color = Summer_mn), size = 2) +
+  scale_color_viridis_c(limits = c(-1.5, 17), option = "H") +
+  theme_bw()
+
+## Visualize relationship between elevation and temperature
+ggplot(data = mf04p$obs, aes(x = ELEV_DEM, y = Summer_mn)) +
+  geom_point(size = 2) +
+  theme_bw()
+
+## Visualize relationship between precipitation and temperature
+ggplot(data = mf04p$obs, aes(x = AREAWTMAP, y = Summer_mn)) +
+  geom_point(size = 2) +
+  theme_bw()
+
+## Create Torgegram
+tg <- Torgegram(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  type = c("flowcon", "flowuncon", "euclid")
+)
+## Visualize Torgegram
+## all types seem to be increasing with distance, suggesting at least some
+## form of spatial dependence
+plot(tg)
+
+# Model Building ####
+
+## Fit a nonspatial model using lm()
+lmod <- lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  data = mf04p$obs
+)
+
+## Summarize the model
+summary(lmod)
+
+## Fit a model with all possible covariance components
+ssn_mod <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  tailup_type = "exponential",
+  taildown_type = "spherical",
+  euclid_type = "gaussian",
+  additive = "afvArea"
+)
+## Summarize the model
+summary(ssn_mod)
+
+## Inspect the variance components
+varcomp(ssn_mod)
+
+## Glance at the model fit
+glance(ssn_mod)
+
+## Fit a model with just taildown and nugget (notice no additive needed)
+ssn_mod2 <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  taildown_type = "spherical"
+)
+
+## Fit a nonspatial (i.e., just nugget) model using ssn_lm() (equivalent to lm())
+ssn_mod3 <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p
+)
+
+## Glance at all model fits (look for lowest AIC)
+glances(ssn_mod, ssn_mod2, ssn_mod3)
+
+## leave-one-out cross validation for each model (look for lowest RMSPE)
+loocv_mod <- loocv(ssn_mod)
+loocv_mod
+loocv_mod$RMSPE
+loocv_mod2 <- loocv(ssn_mod2)
+loocv_mod2$RMSPE
+loocv_mod3 <- loocv(ssn_mod3)
+loocv_mod3$RMSPE
+
+## Fit models with maximum likelihood (this allows us to compare models
+## with different fixed effect structures and covariance structures)
+ml_mod <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  tailup_type = "exponential",
+  taildown_type = "spherical",
+  euclid_type = "gaussian",
+  additive = "afvArea",
+  estmethod = "ml"
+)
+## Model without ELEV_DEM
+ml_mod2 <- ssn_lm(
+  formula = Summer_mn ~ AREAWTMAP,
+  ssn.object = mf04p,
+  euclid_type = "gaussian",
+  estmethod = "ml"
+)
+
+## glance at model fit and leave-one-out (look for lower AIC, RMSPE)
+glances(ml_mod, ml_mod2)
+loocv_mod_ml <- loocv(ml_mod)
+loocv_mod_ml$RMSPE
+loocv_mod_ml2 <- loocv(ml_mod2)
+loocv_mod_ml2$RMSPE
+
+## Refit final model using REML (look for lower RMSPE)
+loocv_mod$RMSPE
+loocv_mod_ml$RMSPE
+
+
+#:::::::::::::::::::::::::::::::::::::
+# Model Inspection ####
+#:::::::::::::::::::::::::::::::::::::
+
+## Confidence intervals on parameters
+tidy(ssn_mod, conf.int = TRUE)
+
+## Augment the data with diagnostics
+aug_ssn_mod <- augment(ssn_mod)
+aug_ssn_mod
+
+## Write out the augmented data to a geopackage
+st_write(aug_ssn_mod, paste0(tempdir(), "/aug_ssn_mod.gpkg"))
+
+## Model visualization (fitted vs residuals [standardized])
+plot(ssn_mod, which = 1)
+
+
+#:::::::::::::::::::::::::::::::::::::
+# Prediction ####
+#:::::::::::::::::::::::::::::::::::::
+
+## Make predictions at all locations in the pred1km data
+predict(ssn_mod, newdata = "pred1km")
+
+## Augment prediction data with predictions
+aug_preds <- augment(ssn_mod, newdata = "pred1km")
+aug_preds[, ".fitted"]
+
+## Visualize predictions on the network
+ggplot() +
+  geom_sf(data = mf04p$edges) +
+  geom_sf(data = aug_preds, aes(color = .fitted), size = 2) +
+  scale_color_viridis_c(limits = c(-1.5, 17), option = "H") +
+  theme_bw()
+
+## Write out augmented prediction data to a GeoPackage
+st_write(aug_preds, paste0(tempdir(), "/aug_preds.gpkg"))
+
+## Predict for all prediction data
+predict(ssn_mod)
+predict(ssn_mod, newdata = "all")
+
+## Block prediction for the average in an area (here, CapeHorn)
+predict(ssn_mod, newdata = "CapeHorn", block = TRUE, interval = "prediction")
+
+#:::::::::::::::::::::::::::::::::::::
+# Advanced Features ####
+#:::::::::::::::::::::::::::::::::::::
+
+# Specify initial values ####
+
+## Fix de at one and fit model
+euclid_init <- euclid_initial("gaussian", de = 1, known = "de")
+euclid_init
+
+ssn_init <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  tailup_type = "exponential",
+  taildown_type = "spherical",
+  euclid_initial = euclid_init,
+  additive = "afvArea"
+)
+summary(ssn_init)
+
+# Random Effects ####
+
+## Random effect for network ID (two networks)
+ssn_rand <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  tailup_type = "exponential",
+  taildown_type = "spherical",
+  euclid_type = "gaussian",
+  additive = "afvArea",
+  random = ~ as.factor(netID)
+)
+summary(ssn_rand)
+
+# Partition Factors ####
+
+## Partition Factor for network ID (even Euclidean component is independent
+## across networks when specifying this)
+ssn_part <- ssn_lm(
+  formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
+  ssn.object = mf04p,
+  tailup_type = "exponential",
+  taildown_type = "spherical",
+  euclid_type = "gaussian",
+  additive = "afvArea",
+  partition_factor = ~ as.factor(netID)
+)
+summary(ssn_part)
+
+# Generalized Linear Models ####
+
+## Visualize C16, the number of days the temperature exceeded 16 C
+## This is a count variable
+ggplot() +
+  geom_sf(data = mf04p$edges) +
+  geom_sf(data = mf04p$obs, aes(color = C16), size = 2) +
+  scale_color_viridis_c(option = "H") +
+  theme_bw()
+
+## Fit a poisson regression model with tailup and taildown components
+ssn_pois <- ssn_glm(
+  formula = C16 ~ ELEV_DEM + AREAWTMAP,
+  family = "poisson",
+  ssn.object = mf04p,
+  tailup_type = "epa",
+  taildown_type = "mariah",
+  additive = "afvArea"
+)
+
+## Sumamrize the model fit
+summary(ssn_pois)
+
+## Fit a negative binomial model with tailup and taildown components
+ssn_nb <- ssn_glm(
+  formula = C16 ~ ELEV_DEM + AREAWTMAP,
+  family = "nbinomial",
+  ssn.object = mf04p,
+  tailup_type = "epa",
+  taildown_type = "mariah",
+  additive = "afvArea"
+)
+
+# Compare via AIC, loocv (look for lower AIC, RMSPE)
+glances(ssn_pois, ssn_nb)
+loocv_pois <- loocv(ssn_pois)
+loocv_pois$RMSPE
+loocv_nb <- loocv(ssn_nb)
+loocv_nb$RMSPE
+
+# Simulating Data on a Stream Network ####
+
+## Specify covariance parameters
+tu_params <- tailup_params("exponential", de = 0.4, range = 1e5)
+td_params <- taildown_params("spherical", de = 0.1, range = 1e6)
+euc_params <- euclid_params("gaussian", de = 0.2, range = 1e3)
+nug_params <- nugget_params("nugget", nugget = 0.1)
+
+## Set a reproducible seed
+set.seed(2)
+
+## Simulate Gaussian data
+sims <- ssn_simulate(
+  family = "gaussian",
+  ssn.object = mf04p,
+  network = "obs",
+  additive = "afvArea",
+  tailup_params = tu_params,
+  taildown_params = td_params,
+  euclid_params = euc_params,
+  nugget_params = nug_params,
+  mean = 0,
+  samples = 1
+)
+## Inspect first few simulations
+head(sims)
+
+## Simulate binomial data
+sims <- ssn_simulate(
+  family = "binomial",
+  ssn.object = mf04p,
+  network = "obs",
+  additive = "afvArea",
+  tailup_params = tu_params,
+  taildown_params = td_params,
+  euclid_params = euc_params,
+  nugget_params = nug_params,
+  mean = 0,
+  samples = 2
+)
+## Inspect first few simulations
+head(sims)
diff --git a/man/MiddleFork04.ssn.Rd b/man/MiddleFork04.ssn.Rd
index 314159b..50bd09b 100644
--- a/man/MiddleFork04.ssn.Rd
+++ b/man/MiddleFork04.ssn.Rd
@@ -7,8 +7,8 @@
 \source{
 \code{edges} are a modified version of the United States
 National Hydrography Dataset
-(http://nhd.usgs.gov/). \code{sites}, \code{pred1km}, \code{CapeHorn}
-and \code{Knapp} are unpublished United States Forest Service data.
+(http://nhd.usgs.gov/). \code{sites}, \code{pred1km} and \code{CapeHorn}
+are unpublished United States Forest Service data.
 }
 \description{
 The \code{MiddleFork04.ssn} data folder contains the spatial, attribute,
@@ -16,24 +16,22 @@ and topological information needed to construct an SSN object using
 the \code{SSN2} package.
 }
 \details{
-The \code{MiddleFork04.ssn} folder contains five shapefiles:
+The \code{MiddleFork04.ssn} folder contains five geopackages:
 
 \itemize{
-\item edges: polyline shapefile representing the stream network
-\item sites: point shapefile representing the observed site locations
-\item pred1km: point shapefile representing prediction site locations at
+\item edges: geopackage with LINESTRING geometry representing the stream network
+\item sites: geopackage with POINT geometry representing the observed site locations
+\item pred1km: geopackage with POINT geometry prediction site locations at
 approximately 1km intervals throughout the stream network
-\item Knapp: point shapefile representing prediction site locations on the Knapp River
-\item CapeHorn: point shapefile representing prediction site locations on the Cape Horn River
+\item CapeHorn: geopackage with POINT geometry representing prediction site locations on the Cape Horn River
 }
 
-The \code{MiddleFork04.ssn} includes one text file, \code{netID1.txt}, which contains the
-topological information for the stream network in the Middle Fork 2004
+The \code{MiddleFork04.ssn} includes two text files, \code{netID1.txt} and \code{netID2.txt}, which contain the
+topological information for the two stream networks in the Middle Fork 2004
 dataset.
 
 The distance folder contains four folders that store the hydrologic
-distance matrices for each of the point shapefiles (\code{obs}, \code{CapeHorn},
-\code{Knapp}, and \code{pred1km}). See \code{\link[=ssn_create_distmat]{ssn_create_distmat()}} for a
+distance matrices for each of the point datasets (\code{obs}, \code{CapeHorn}, and \code{pred1km}). See \code{\link[=ssn_create_distmat]{ssn_create_distmat()}} for a
 detailed description of the distance matrix file structure.
 
 Attribute data is also stored within each of the spatial
@@ -41,16 +39,16 @@ datasets. The column names are defined as follows:
 
 \code{edges}:
 \itemize{
+\item rid: Reach identifier
 \item COMID: Common identifier of an NHD feature or relationship
 \item GNIS_Name: Feature name as found in the Geographic Names Information System
 \item REACHCODE: Unique identifier for a reach. The first 8 digits contain the identfier for the HUC8 and the last 6 digits are a unique within-HUC8 identifier for the reach
 \item FTYPE: three-digit integer used to classify hydrography features in the NHD and define subtypes
 \item FCODE: Numeric code that contains the feature type and its attributes as found in the NHDFCode lookup table
-\item CDRAINAG: Cumulative drainage area (km2) for the lowermost location on the edge
 \item AREAWTMAP: Area weighted mean annual precipitation (mm) at the lowermost location on the edge
 \item SLOPE: Slope of the edge (cm/cm)
-\item h2oAreaKm2: Watershed area (km2) for the lowermost location on the line segment
-\item rid: Reach identifier
+\item rcaAreaKm2: Reach contributing area (km2) for each edge feature. The RCA represents the land area that drains directly to the edge feature
+\item h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature
 \item areaPI: Segment proportional influence value, calculated using watershed area (h2oAreaKm2)
 \item afvArea: Additive function value, calculated using areaPI
 \item upDist: Distance from the stream outlet (most downstream location in the the stream network) to the uppermost location on the line segment
@@ -59,9 +57,10 @@ datasets. The column names are defined as follows:
 }
 \code{sites}:
 \itemize{
+\item rid: Reach identifier of the edge the site resides on
+\item pid: Point identifier
 \item STREAMNAME: Stream name
 \item COMID: Common identifier of an NHD feature or relationship
-\item CDRAINAG: Cumulative drainage area (km2)
 \item AREAWTMAP: Area weighted mean annual precipitation (mm) at lowermost
 location on the line segment where the site resides
 \item SLOPE: Slope of the line segment (cm/cm) where the site resides
@@ -81,34 +80,33 @@ from 1980-2009 across 10 COOP air stations within the domain
 from 1980-2009 across 10 COOP air stations within the domain.
 MWMT = maximum 7-day moving average of the maximum daily temperature
 (i.e. maximum of all the 7-day maximums)
-\item NEAR_X: x coordinate
-\item NEAR_Y: y coordinate
-\item rid: Reach identifier of the edge the site resides on
-\item ratio: Site ratio value; provides the proportional distance along the edge to the site location
+\item rcaAreaKm2: Reach contributing area (km2) for each edge feature the site resides on. The RCA represents the land area that drains directly to the edge feature
+\item h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature the site resides on
+\item ratio: Site ratio value; provides the proportional distance from the downstream node of the edge to the site location
 \item upDist: Distance upstream from the stream outlet (m)
 \item afvArea: Additive function value calculated using waterhsed area (h2oAreaKm2)
 \item locID: Location identifier
 \item netID: Stream network identifier
-\item pid: Point identifier
+\item snapdist: Distance (m) that site was moved when it was snapped to the edges
 }
 
-\code{pred1km}, \code{CapeHorn}, and \code{Knapp}:
+\code{pred1km} and \code{CapeHorn}:
 \itemize{
+\item rid: Reach identifier of the edge the site resides on
+\item pid: Point identifier
 \item COMID: Common identifier of an NHD feature or relationship
 \item GNIS_Name: Feature name of the edge the site resides on, as found in the Geographic Names Information System
-\item CDRAINAG: Cumulative drainage area (km2)
 \item AREAWTMAP: Area weighted mean annual precipitation (mm) at lowermost location on the line segment where the site resides
 \item SLOPE: Slope of the line segment (cm/cm) where the site resides
 \item ELEV_DEM: Elevation at the site based on a 30m DEM
-\item NEAR_X: x coordinate
-\item NEAR_Y: y coordinate
-\item rid: Reach identifier of the edge the site resides on
+\item rcaAreaKm2: Reach contributing area (km2) for each edge feature the site resides on. The RCA represents the land area that drains directly to the edge feature
+\item h2oAreaKm2: Watershed area (km2) for the lowermost location on the edge feature the site resides on
 \item ratio: Site ratio value; provides the proportional distance along the edge to the site location
 \item upDist: Distance upstream from the stream outlet (m)
 \item afvArea: Additive function value calculated using watershed area (h2oAreaKm2)
 \item locID: Location identifier
 \item netID: Stream network identifier
-\item pid: Point identifier
+\item snapdist: Distance (m) that site was moved when it was snapped to the edges
 \item FlowCMS: Average stream flow (cubic meters per sec) for August, by year, from 1950-2010 across 9 USGS gauges in the region
 \item AirMEANc: Average mean air temperature (C) from July 15 - August 31, from 1980-2009 across 10 COOP air stations within the domain
 \item AirMWMTc: Average maximum air temperature (C) from July 15 - August 31, from 1980-2009 across 10 COOP air stations within the domain. MWMT = maximum 7-day moving average of the maximum daily temperature(i.e. maximum of all the 7-day maximums)
diff --git a/man/SSN_to_SSN2.Rd b/man/SSN_to_SSN2.Rd
index a3070bb..ac6b3f4 100644
--- a/man/SSN_to_SSN2.Rd
+++ b/man/SSN_to_SSN2.Rd
@@ -4,22 +4,13 @@
 \alias{SSN_to_SSN2}
 \title{Convert object from \code{SpatialStreamNetwork} class to \code{SSN} class}
 \usage{
-SSN_to_SSN2(object, edge_additive = NULL, site_additive = NULL)
+SSN_to_SSN2(object)
 }
 \arguments{
 \item{object}{A SpatialStreamNetwork object}
-
-\item{edge_additive}{A character vector of additive function value
-column names found in edges. Default is NULL. See Details for
-more information.}
-
-\item{site_additive}{A character vector of additive function value
-column names found in the observed sites and prediction
-sites. See Details for more information. Default is NULL.}
 }
 \value{
-An S3 \code{SSN} class object, with additive function value
-columns converted to text format.
+An S3 \code{SSN} class object.
 }
 \description{
 Convert an S4 \code{SpatialStreamNetwork} object
@@ -34,25 +25,4 @@ objects \emph{stored in saved workspaces} to the S3 \code{SSN} class object
 used in the \code{SSN2} package. Note that \code{\link[SSN2]{ssn_import}} is
 used to create an S3 \code{SSN} object from data stored locally in a .ssn
 directory.
-
-Additive function values are used to generate spatial weights for
-the tail-up covariance function used in \code{ssn_glm}. The range
-of additive function values are restricted to \eqn{0 \le AFV \le
-  1}. In the \code{SSN2} package, columns containing additive
-function values are stored as text, rather than numeric
-format. This prevents values less than 1 with more than 10 digits
-from being truncated when writing/reading shapefiles (and their
-.dbf tables). The columns containing additive function values are
-specified using the \code{edge_additive} and \code{site_additive} arguments
-and converted to character format in the \code{SSN} class object
-returned. The arguments \code{edge_additive} and \code{site_additive}
-accept a single column name in character format, or a vector
-containing multiple column names. Note that, column names for
-additive function values on the edges, sites, and prediction
-sites may differ. If a column specified in \code{edge_additive} or
-\code{site_additive} is not present, the function will return a
-warning, rather than an error. Columns containing additive
-function values can also be converted to text manually using the
-\code{\link[base]{formatC}} function, which provides the
-flexibility needed to store the values with their full precision.
 }
diff --git a/man/create_netgeom.Rd b/man/create_netgeom.Rd
new file mode 100644
index 0000000..393f29a
--- /dev/null
+++ b/man/create_netgeom.Rd
@@ -0,0 +1,82 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/create_netgeom.R
+\name{create_netgeom}
+\alias{create_netgeom}
+\title{Create netgeom column in SSN object}
+\usage{
+create_netgeom(sf_data, type = NULL, overwrite = FALSE)
+}
+\arguments{
+\item{sf_data}{An \code{sf} object with LINESTING or POINT geometry
+created using \code{link{lsn_to_ssn}} (see Details).}
+
+\item{type}{Character string defining geometry type of
+\code{sf_data}. Default = \code{NULL}.}
+
+\item{overwrite}{Logical indicating whether existing data should be
+overwritten if present. Default = \code{FALSE}.}
+}
+\value{
+An \code{sf} object containing the original data from
+\code{sf_data} and an additional column named netgeom.
+}
+\description{
+Create netgeom column for edges, observed sites,
+and/or prediction sites in a Landscape Network (LSN).
+}
+\details{
+Most users will not need to run \code{create_netgeom}
+themselves because it is called internally when \code{lsn_to_ssn}
+is run or an \code{SSN} is imported using
+\code{link[SSN2]{ssn_import}} found in the \code{SSN2}
+package. For users who do wish to run \code{create_netgeom}, the
+\code{sf_data} object must represent edges, observed sites, or
+prediction sites in a \code{SSN} object created using
+\code{link{lsn_to_ssn}}.
+
+The netgeom column contains information in character format used
+to describe the topology of the LSN. The format and content of
+the netgeom column differs depending on whether \code{sf_data}
+contains LINESTRING (edges) or POINT (observed or prediction
+sites) geometry. For edges, the netgeom format is:
+\itemize{
+\item{\code{'ENETWORK (netID, rid, upDist)'}}
+}
+
+For observed or prediction sites, the netgeom format is:
+\itemize{
+\item{\code{'SNETWORK (netID, rid, upDist, ratio, pid, locID)'}}
+}
+
+The rid, ratio, upDist, netID, pid, and locID columns must be
+present in \code{sf_data} before netgeom is added.
+
+If \code{overwrite = TRUE} and a column named netgeom is present in
+\code{sf_data}, the data will be overwritten. Default = FALSE.
+}
+\examples{
+## Create local temporary copy of MiddleFork04.ssn found in
+## the SSN2 package. Only necessary for this example.
+copy_lsn_to_temp()
+
+# Import the SSN object with prediction points, pred1km
+mf04<- ssn_import(
+   paste0(tempdir(), "/MiddleFork04.ssn"),
+   predpts = c("pred1km"),
+   overwrite = TRUE
+)
+
+# Recalculate the netgeom column for the observed sites
+sf_obs <- create_netgeom(
+    mf04$obs,
+    type = "POINT",
+    overwrite = TRUE
+)
+
+# Recalculate the netgeom column for the edges
+sf_edges <- create_netgeom(
+    mf04$edges,
+    type = "LINESTRING",
+    overwrite = TRUE
+)
+}
diff --git a/man/names.SSN.Rd b/man/names.SSN.Rd
deleted file mode 100644
index df492f4..0000000
--- a/man/names.SSN.Rd
+++ /dev/null
@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/names.SSN.R
-\name{names.SSN}
-\alias{names.SSN}
-\title{names SSN object}
-\usage{
-\method{names}{SSN}(x, ...)
-}
-\arguments{
-\item{x}{An SSN object.}
-
-\item{...}{Other arguments. Not used (needed for generic consistency).}
-}
-\value{
-Print variable names to console
-}
-\description{
-Extract and print names from the SSN object
-}
diff --git a/man/predict.SSN2.Rd b/man/predict.SSN2.Rd
index d0b5cd4..3ae4d2b 100644
--- a/man/predict.SSN2.Rd
+++ b/man/predict.SSN2.Rd
@@ -32,7 +32,9 @@
 \item{object}{A fitted model object from \code{\link[=ssn_lm]{ssn_lm()}} or \code{\link[=ssn_glm]{ssn_glm()}}.}
 
 \item{newdata}{A character vector that indicates the name of the prediction data set
-in the SSN object for which predictions are desired. If omitted, predictions
+for which predictions are desired (accessible via \code{object$ssn.object$preds}).
+Note that the prediction data must be in the original SSN object used to fit the model.
+If \code{newdata} is omitted, predictions
 for all prediction data sets are returned. Note that the name \code{".missing"}
 indicates the prediction data set that contains the missing observations in the data used
 to fit the model.}
diff --git a/man/ssn_create_distmat.Rd b/man/ssn_create_distmat.Rd
index 0a97418..3fae1c9 100644
--- a/man/ssn_create_distmat.Rd
+++ b/man/ssn_create_distmat.Rd
@@ -131,7 +131,7 @@ locations will also be calculated if \code{among_predpts = TRUE}.
 copy_lsn_to_temp()
 ## Import SSN data
 mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-  predpts = c("pred1km.shp", "Knapp"),
+  predpts = c("pred1km.gpkg", "CapeHorn"),
   overwrite = TRUE
 )
 
@@ -146,7 +146,7 @@ ssn_create_distmat(mf04p,
 ## Distance matrices for observations and pred1km prediction sites are
 ## not recalculated.
 ssn_create_distmat(mf04p,
-  predpts = "Knapp", overwrite = TRUE,
+  predpts = "CapeHorn", overwrite = TRUE,
   among_predpts = TRUE, only_predpts = TRUE
 )
 }
diff --git a/man/ssn_get_netgeom.Rd b/man/ssn_get_netgeom.Rd
index fc7d85f..eaa2a39 100644
--- a/man/ssn_get_netgeom.Rd
+++ b/man/ssn_get_netgeom.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/ssn_get_netgeometry.R
+% Please edit documentation in R/ssn_get_netgeom.R
 \name{ssn_get_netgeom}
 \alias{ssn_get_netgeom}
 \title{Extract netgeom column}
diff --git a/man/ssn_import.Rd b/man/ssn_import.Rd
index 4cc8c57..3531565 100644
--- a/man/ssn_import.Rd
+++ b/man/ssn_import.Rd
@@ -4,14 +4,7 @@
 \alias{ssn_import}
 \title{Import \code{SSN} object}
 \usage{
-ssn_import(
-  path,
-  include_obs = TRUE,
-  predpts,
-  format_additive = FALSE,
-  names_additive = NULL,
-  overwrite = FALSE
-)
+ssn_import(path, include_obs = TRUE, predpts = NULL, overwrite = FALSE)
 }
 \arguments{
 \item{path}{Filepath to the .ssn directory. See details.}
@@ -19,20 +12,13 @@ ssn_import(
 \item{include_obs}{default = \code{TRUE}. Logical indicating
 whether observed sites should be included in the SSN object.}
 
-\item{predpts}{Vector of shapefile basenames for prediction sites
-found within the .ssn folder.}
-
-\item{format_additive}{Logical indicating whether the columns containing
-the addtive function values should be formated for
-\code{SSN2}. Default = \code{FALSE}.}
-
-\item{names_additive}{Character vector of column names in observed and
-prediction site datasets containing additive function values.
-Must be defined if \code{format_additive = TRUE}. Default =
-\code{NULL}.}
+\item{predpts}{Vector of prediction site dataset names found within
+the .ssn folder. See details.}
 
 \item{overwrite}{default = \code{FALSE}. If \code{TRUE}, overwrite
-existing binaryID.db files.}
+existing binaryID.db files and netgeom column(s) if it exists in the
+edges, observed sites (if \code{include_obs = TRUE}), and
+prediction site datasets (if they exist).}
 }
 \value{
 \code{ssn_import} returns an object of class SSN, which is a list
@@ -44,10 +30,8 @@ with an additional 'netgeom' column.}
 with an additional 'netgeom' column. NA if \code{include_obs =
     FALSE}.}
 \item{\code{preds}: A list of sf data.frames containing prediction
-site locations. The names of the preds list correspond to the
-basenames of the prediction site shapefiles (without the .shp
-extension) specified in \code{predpts}. Empty list if \code{predpts} is not provided.}
-\item{path: The local file to the .ssn directory associated with the \code{SSN}
+site locations. An empty list is returned if \code{predpts} is not provided.}
+\item{path: The local filepath for the .ssn directory associated with the \code{SSN}
 object.}
 }
 }
@@ -56,48 +40,30 @@ This function reads spatial data from a .ssn folder
 and creates an \code{SSN} object.
 }
 \details{
-The \command{importSSN} function imports spatial data from a .ssn
-folder to create an \code{SSN} object. The information contained in the
-.ssn folder can be generated using a number of proprietary and
-open source software tools:
-\itemize{
-\item{The Spatial Tools for the Analysis of River Systems
-(STARS) tools for ArcGIS Desktop versions 9.3x-10.8x (Peterson
-and Ver Hoef 2014). This custom ArcGIS toolset is designed to
-work with existing streams data in vector format.}
-\item{The openSTARS package (Kattwinkel et al. 2020) extends
-the functionality of the STARS toolset, which makes use of R
-and GRASS GIS. It is open source and designed to derive streams
-in raster format from a digital elevation model (DEM).}
-\item{The SSNbler package (currently in development as of
-September 2023) is an open source version of the STARS toolset,
-which makes use of the functionality found in the sf package to
-process streams data in vector format.}
-}
-When spatial data are processed using one of these software
-tools, a .ssn directory is output which contains all of the
-spatial, topological and attribute data needed to fit a spatial
-statistical stream network model to streams data.  This includes:
+The \command{ssn_import} function imports spatial data (shapefile or GeoPackage format)
+from a .ssn folder generated using the
+\code{SSNbler} package function \code{SSNbler::lsn_to_ssn}. The .ssn folder contains all of the spatial, topological and
+attribute data needed to fit a spatial statistical stream network
+model to streams data.  This includes:
 \itemize{
-\item{An edges shapefile of lines that represent the stream
-network.}
-\item{A sites shapefile of points where observed data were
-collected on the stream network.}
-\item{Prediction sites shapefile(s) of locations where
-predictions will be made.}
-\item{netID.dat files for each distinct network, which store
-the topological relationships of the line segments in edges.}
-}
+\item{An edges dataset with LINESTRING geometry representing the stream network.}
+\item{A sites dataset with POINT geometry where observed data were collected on
+the stream network.}
+\item{Prediction sites dataset(s) representing
+locations where predictions will be made.}
+\item{netID.dat file(s) for each distinct network, which stores the topological
+relationships of the line features in edges.}}
+
 A more detailed description of the .ssn directory and its
 contents is provided in Peterson and Ver Hoef (2014).
 
-The \command{ssn_import} imports the edges, observed sites, and
-prediction sites as \code{sf data.frame} objects. A new column named 'netgeom'
-is created to store important data that represents
+The \command{ssn_import} imports the edges, observed sites (optional), and
+prediction sites (optional) as \code{sf data.frame} objects. A new column named 'netgeom'
+is created to store important data representing
 topological relationships in a spatial stream network
 model. These data are stored in character format, which is less
 likely to be inadvertantly changed by users. See
-\code{\link[SSN2]{ssn_get_netgeom}} for a more detailed description of
+\code{\link[SSN2]{create_netgeom}} for a more detailed description of
 the format and contents of 'netgeom'.
 
 The information contained in the netID text files is imported
@@ -111,20 +77,25 @@ to fit a spatial statistical model to stream network data. If
 file already exists within the .ssn directory, it will be
 overwriten when the \code{SSN} object is created.
 
-At a minimum, an \code{SSN} object must always contain streams, which
-are referred to as edges. The \code{SSN} object would also typically
-contain a set of observed sites, where measurements have been
-collected and only one observed dataset is permitted. When
-\code{include_obs=FALSE}, an \code{SSN} object is created without
-observations. This option provides flexibility for users who
-would like to simulate data on a set of artifical sites on an
-existing stream network. Note that observation sites must be
-included in the \code{SSN} object in order to fit models using
-\command{ssn_lm} or \command{ssn_glm}. The \code{SSN} object may contain
-multiple sets of prediction points (or none), which are stored as
-separate shapefiles in the .ssn directory. The
-\code{\link[SSN2]{ssn_import_predpts}} function allows users to import additional
-sets of prediction sites to a an existing \code{SSN} object.
+At a minimum, an \code{SSN} object must always contain streams,
+which are referred to as edges. The \code{SSN} object would also
+typically contain a set of observed sites, where measurements
+have been collected. Only one observed dataset is permitted in an
+\code{SSN} object. When \code{include_obs=FALSE}, an \code{SSN}
+object is created without observations. This option provides
+flexibility for users who would like to simulate data on a set of
+artifical sites on an existing stream network. Note that
+observation sites must be included in the \code{SSN} object in
+order to fit models using \command{ssn_lm} or
+\command{ssn_glm}. The \code{SSN} object may contain multiple
+sets of prediction points (or none), which are stored as separate
+datasets in the .ssn directory. If \code{predpts} is a named
+vector, the names of the \code{preds} list in the \code{SSN}
+object correspond to the vector names. Otherwise, they are set to
+the basename of the prediction site dataset file(s) specified in
+\code{predpts}. The \code{\link[SSN2]{ssn_import_predpts}}
+function allows users to import additional sets of prediction
+sites to a an existing \code{SSN} object.
 }
 \examples{
 ## Create local temporary copy of MiddleFork04.ssn found in
@@ -139,25 +110,17 @@ mf04 <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
 ## Import SSN object with 3 sets of prediction sites
 mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
   predpts = c(
-    "pred1km.shp",
-    "CapeHorn.shp",
-    "Knapp.shp"
+    "pred1km",
+    "CapeHorn"
   ),
   overwrite = TRUE
 )
 
 }
 \references{
-Kattwinkel, M., Szocs, E., Peterson, E., and Schafer,
-R.B. (2020) Preparing GIS data for analysis of stream monitoring
-data: The R package openSTARS. \emph{PLOS One} \bold{15(9)},
-e0239237.
 Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
 ArcGIS toolset used to calculate the spatial information needed
 to fit spatial statistical stream network models to stream
 network data. \emph{Journal of Statistical Software}
 \bold{56(2)}, 1--17.
 }
-\seealso{
-\link{ssn_get_netgeom}
-}
diff --git a/man/ssn_import_predpts.Rd b/man/ssn_import_predpts.Rd
index 86b69b4..7d3cafd 100644
--- a/man/ssn_import_predpts.Rd
+++ b/man/ssn_import_predpts.Rd
@@ -4,32 +4,18 @@
 \alias{ssn_import_predpts}
 \title{Import prediction points into an SSN, ssn_lm, or ssn_glm object}
 \usage{
-ssn_import_predpts(x, predpts, format_additive = FALSE, names_additive = NULL)
+ssn_import_predpts(x, predpts)
 }
 \arguments{
 \item{x}{An object of class\code{SSN}, \code{ssn_lm}, or
 \code{ssn_glm}.}
 
-\item{predpts}{Name of the prediction point shapefile to import in
-character format, without the .shp extension.}
-
-\item{format_additive}{Logical indicating whether the columns containing
-the addtive function values should be formated for
-\code{SSN2}. Default = \code{FALSE}.}
-
-\item{names_additive}{Character vector of column names in observed and
-prediction site datasets containing additive function
-values. Must be defined if \code{format_additive = TRUE}. Default =
-\code{NULL}.}
+\item{predpts}{Name of the prediction point dataset to import in
+character format. See details.}
 }
 \value{
 an object of class \code{SSN}, \code{ssn_lm}, or
-\code{ssn_glm} which contains the new prediction dataset. The
-name of the prediction dataset in the preds list corresponds to
-the basenames of the prediction site shapefiles (without the .shp
-extension) specified in \code{predpts}. See
-\code{\link[SSN2]{ssn_import}} for a detailed description of
-the prediction dataset format within the \code{SSN} class object.
+\code{ssn_glm} which contains the new prediction dataset.
 }
 \description{
 A shapefile of prediction points found in the .ssn
@@ -37,28 +23,35 @@ directory are imported into an existing object of class
 \code{SSN}, \code{ssn_lm}, or \code{ssn_glm}.
 }
 \details{
-\command{ssn_import_predpts} imports a shapefile of
-prediction points residing in the .ssn directory into an existing
+\command{ssn_import_predpts} imports one set of prediction
+points residing in the .ssn directory into an existing
 \code{SSN}, \code{ssn_lm}, or \code{ssn_glm} object. The
-prediction dataset must reside in the ssn.object$path
+prediction dataset must be in shapefile or geopackage format
+(.shp or .gpkg, respectively) and reside in the ssn.object$path
 directory. The path for an \code{SSN} object can be updated using
 \command{ssn_update_path()} prior to importing prediction
-datasets. Note that, the prediction dataset must contain the
+datasets. The argument \code{predpts} accepts the name of the
+prediction point dataset, with or without the file extension. If
+it is passed as a named vector (of length 1), then the name
+provided is used as the prediction dataset name in the \code{SSN}
+object prediction sites list
+(e.g. \code{names(ssn.obj$preds)}). Otherwise, the file basename
+is used in the names attribute. See
+\code{\link[SSN2]{ssn_import}} for a detailed description of the
+prediction dataset format within the \code{SSN} class object.
+
+When the prediction dataset is imported, a new column named
+\code{netgeom} is created. If this column already exists it is
+overwritten. Please see \code{\link{create_netgeom}} for a
+detailed description of the \code{netgeom} column and the
+information it contains.
+
+The prediction dataset specified in \code{predpts} must contain the
 spatial, topological and attribute information needed to make
-predictions using an ssn_lm or ssn_glm object.  This information
-can be generated using a number of proprietary and open source
-software tools: \itemize{ \item{The Spatial Tools for the
-Analysis of River Systems (STARS) tools for ArcGIS Desktop
-versions 9.3x-10.8x (Peterson and Ver Hoef 2014). This custom
-ArcGIS toolset is designed to work with existing streams data in
-vector format.}  \item{The openSTARS package (Kattwinkel et
-al. 2020) extends the functionality of the STARS toolset, which
-makes use of R and GRASS GIS. It is open source and designed to
-derive streams in raster format from a digital elevation model
-(DEM).}  \item{The SSNbler package (currently in development as
-of September 2023) is an open source version of the STARS
-toolset, which makes use of the functionality found in the sf
-package to process streams data in vector format.}  }
+predictions using an ssn_lm or ssn_glm object. This information
+is generated using the \code{SSNbler} package, which makes use of
+the functionality found in the \code{sf} and \code{igraph}
+packages to process streams data in vector format.
 }
 \examples{
 ## Create local temporary copy of MiddleFork04.ssn found in
@@ -70,27 +63,20 @@ mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
   overwrite = TRUE
 )
 
-## Import pred1km prediction dataset into SSN object
-mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"))
-mf04p <- ssn_import_predpts(mf04p, predpts = "pred1km")
+## Import pred1km prediction dataset into SSN object and assign the
+## name preds1
+mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
+    overwrite = TRUE)
+mf04p <- ssn_import_predpts(mf04p, predpts = c(preds1 = "pred1km"))
 names(mf04p$preds)
 
-## Import pred1km prediction dataset into a ssn_glm object
+## Import CapeHorn prediction dataset into a ssn_glm object, using
+## the default file basename as the name
 ssn_gmod <- ssn_glm(Summer_mn ~ netID, mf04p,
   family = "Gamma",
   tailup_type = "exponential", additive = "afvArea"
 )
 ssn_gmod <- ssn_import_predpts(ssn_gmod, predpts = "CapeHorn")
 names(ssn_gmod$ssn.object$preds)
-}
-\references{
-Kattwinkel, M., Szocs, E., Peterson, E., and Schafer,
-R.B. (2020) Preparing GIS data for analysis of stream monitoring
-data: The R package openSTARS. \emph{PLOS One} \bold{15(9)},
-e0239237.
-Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
-ArcGIS toolset used to calculate the spatial information needed
-to fit spatial statistical stream network models to stream
-network data. \emph{Journal of Statistical Software}
-\bold{56(2)}, 1--17.
+
 }
diff --git a/man/ssn_names.Rd b/man/ssn_names.Rd
new file mode 100644
index 0000000..2e21729
--- /dev/null
+++ b/man/ssn_names.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ssn_names.R
+\name{ssn_names}
+\alias{ssn_names}
+\title{Return names of data in an SSN object}
+\usage{
+ssn_names(ssn.object)
+}
+\arguments{
+\item{ssn.object}{An \code{SSN} object.}
+}
+\value{
+Print variable names to console
+}
+\description{
+Extract and print names from the \code{edges}, \code{sites}
+and \code{preds} elements of an SSN object.
+}
diff --git a/man/ssn_split_predpts.Rd b/man/ssn_split_predpts.Rd
index d579b59..4b1568f 100644
--- a/man/ssn_split_predpts.Rd
+++ b/man/ssn_split_predpts.Rd
@@ -20,7 +20,7 @@ ssn_split_predpts(
 \item{ssn}{An \code{SSN} object.}
 
 \item{predpts}{A character string representing the name of the
-prediction dataset}
+prediction dataset.}
 
 \item{size_predpts}{numeric value representing the size of the new
 prediction sets. The existing prediction set is split equally to
@@ -46,12 +46,12 @@ levels should be dropped in the \code{by} column when the new
 prediction dataset(s) are created. Default is \code{FALSE}}
 
 \item{overwrite}{logical indicating whether the new prediction
-dataset shapefile should be deleted in the .ssn directory if it
+dataset geopackage should be deleted in the .ssn directory if it
 already exists. Default = \code{FALSE}}
 }
 \value{
 returns the \code{SSN} specified in \code{ssn}, with one or more new prediction
-sets. Shapefiles of the new prediction sets are written to the
+sets. Geopackages of the new prediction sets are written to the
 .ssn directory designated in ssn$path.
 }
 \description{
@@ -64,16 +64,16 @@ set, or a logical expression.
 }
 \details{
 Three methods have been provided to split prediction sets:
-\code{size_predpts}, \code{by}, and \code{subset}. The
-\code{size_predpts} method is used to split the existing prediction
-set into multiple equally-sized prediction sets. Note that the
+size, by, and subset. The
+size method is used to split the existing prediction
+set into multiple equally-sized prediction sets using the \code{size_predpts} argument. Note that the
 final prediction set may be smaller in size than the others if
 the total number of predictions is not evenly divisible by
-\code{size_predpts}. The \code{by} method is used if the prediction
+\code{size_predpts}. The by method is used if the prediction
 set is to be split into multiple new prediction sets based on an
 existing column of type factor, integer, character, or
-logical. The \code{subset} method is used to create one new
-prediction set based on a logical expression.
+logical specified using the argument \code{by}. The subset method is used to create one new
+prediction set based on a logical expression defined in \code{subset}.
 
 When more than one prediction dataset is created the prediction
 dataset names will be appended with a hyphen and prediction
@@ -96,11 +96,11 @@ predictions can be made.
 ## Import SSN object
 copy_lsn_to_temp() ## Only needed for this example
 ssn <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-  predpts = c("pred1km.shp", "Knapp", "CapeHorn"),
+  predpts = c("pred1km", "CapeHorn"),
   overwrite = TRUE
 )
 
-## Split predictions into size_predpts 200
+## Split predictions based on 'size' method
 ssn1 <- ssn_split_predpts(ssn, "CapeHorn",
   size_predpts = 200,
   keep = FALSE, overwrite = TRUE
@@ -108,7 +108,7 @@ ssn1 <- ssn_split_predpts(ssn, "CapeHorn",
 names(ssn1$preds)
 nrow(ssn1$preds[["CapeHorn-1"]])
 
-## Split predictions using by method
+## Split predictions using 'by' method
 ssn$preds$pred1km$net.fac <- as.factor(ssn$preds$pred1km$netID)
 ssn2 <- ssn_split_predpts(ssn, "pred1km",
   by = "net.fac",
@@ -116,7 +116,7 @@ ssn2 <- ssn_split_predpts(ssn, "pred1km",
 )
 names(ssn2$preds)
 
-## Split predictions using subset method
+## Split predictions using 'subset' method
 ssn3 <- ssn_split_predpts(ssn, "pred1km",
   subset = ratio > 0.5,
   id_predpts = "RATIO_05", overwrite = TRUE
diff --git a/man/ssn_subset.Rd b/man/ssn_subset.Rd
index d107d3d..597cc0b 100644
--- a/man/ssn_subset.Rd
+++ b/man/ssn_subset.Rd
@@ -58,7 +58,7 @@ matrices before fitting models to the data in the subset
 ## Import SSN object
 copy_lsn_to_temp() ## Only needed for this example
 mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-  predpts = c("pred1km.shp", "Knapp"),
+  predpts = "pred1km",
   overwrite = TRUE
 )
 
diff --git a/man/ssn_write.Rd b/man/ssn_write.Rd
index d9be1e9..32f0908 100644
--- a/man/ssn_write.Rd
+++ b/man/ssn_write.Rd
@@ -23,7 +23,9 @@ after writing to file. Defaults to \code{FALSE}.}
 \value{
 {ssn_write} creates an .ssn directory that contains the
 spatial, topological, and attribute information stored in the
-original \code{SSN} object. When \code{import = TRUE}, the
+original \code{SSN} object. Spatial datasets found in the
+\code{SSN} object (e.g. edges, obs, and prediction sites) are
+saved in GeoPackage format. When \code{import = TRUE}, the
 \code{SSN} object is imported and returned.
 }
 \description{
@@ -36,12 +38,15 @@ This function writes an \code{SSN} object to a local
 copy_lsn_to_temp()
 ## Import SSN object with prediction sites
 mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-  predpts = c("pred1km.shp"),
+  predpts = "pred1km",
   overwrite = TRUE
 )
 
 ## Write SSN to new .ssn directory
-ssn_write(mf04p, path = paste0(tempdir(), "/tempSSN.ssn"))
+ssn_write(mf04p,
+  path = paste0(tempdir(), "/tempSSN.ssn"),
+  overwrite = TRUE
+)
 
 ## Write SSN to .ssn directory and return SSN object
 tempSSN <- ssn_write(mf04p, path = paste0(
diff --git a/src/SSN2.dll b/src/SSN2.dll
index a386d27..ce3513e 100644
Binary files a/src/SSN2.dll and b/src/SSN2.dll differ
diff --git a/tests/testthat/helper-data.R b/tests/testthat/helper-data.R
new file mode 100644
index 0000000..5acb050
--- /dev/null
+++ b/tests/testthat/helper-data.R
@@ -0,0 +1,16 @@
+# Copy the mf04p .ssn data to a local directory and read it into R
+# When modeling with your .ssn object, you will load it using the relevant
+# path to the .ssn data on your machine
+copy_lsn_to_temp()
+temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
+mf04p <- ssn_import(
+  temp_path,
+  predpts = c("pred1km", "CapeHorn"),
+  overwrite = TRUE
+)
+ssn_create_distmat(
+  mf04p,
+  predpts = c("pred1km", "CapeHorn"),
+  overwrite = TRUE,
+  among_predpts = TRUE
+)
diff --git a/tests/testthat/test-Torgegram.R b/tests/testthat/test-Torgegram.R
index 0bfbf86..ad2f98b 100644
--- a/tests/testthat/test-Torgegram.R
+++ b/tests/testthat/test-Torgegram.R
@@ -1,41 +1,45 @@
-# Copy the mf04p .ssn data to a local directory and read it into R
-# When modeling with your .ssn object, you will load it using the relevant
-# path to the .ssn data on your machine
-copy_lsn_to_temp()
-temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-mf04p <- ssn_import(
-  temp_path,
-  predpts = c("pred1km", "CapeHorn", "Knapp"),
-  overwrite = TRUE
-)
-
 test_that("torgegram works", {
   tg <- Torgegram(Summer_mn ~ ELEV_DEM, mf04p)
-  tg_names <- names(tg$flowcon)
+  expect_s3_class(tg, "Torgegram")
+  tg_names <- names(tg)
+  expect_identical(c("flowcon", "flowuncon"), tg_names)
+  tg_names_flowcon <- names(tg$flowcon)
+  expect_identical(tg_names_flowcon, c("bins", "dist", "gamma", "np"))
   expect_s3_class(tg$flowcon, "data.frame")
-  expect_equal(names(tg$flowcon), tg_names)
+  expect_equal(unlist(tg$flowcon[1, c("dist", "gamma", "np")]), c("dist" = 409.648, "gamma" = 0.167, "np" = 11), tolerance = 0.01)
+  tg_names_flowuncon <- names(tg$flowuncon)
+  expect_identical(tg_names_flowuncon, c("bins", "dist", "gamma", "np"))
   expect_s3_class(tg$flowuncon, "data.frame")
-  expect_equal(names(tg$flowuncon), tg_names)
-  expect_false("euclid" %in% names(tg))
+  expect_equal(unlist(tg$flowuncon[1, c("dist", "gamma", "np")]), c("dist" = 487.595, "gamma" = 2.739, "np" = 6), tolerance = 0.01)
   expect_invisible(plot(tg))
 })
 
 test_that("torgegram works euclid", {
   tg <- Torgegram(Summer_mn ~ ELEV_DEM, mf04p, type = "euclid")
-  tg_names <- names(tg$euclid)
-  expect_s3_class(tg$euclid, "data.frame")
-  expect_equal(names(tg$euclid), tg_names)
-  expect_false(any(c("flowcon", "flowuncon") %in% names(tg)))
+  expect_s3_class(tg, "Torgegram")
+  tg_names <- names(tg)
+  expect_identical(c("euclid"), tg_names)
+  tg_names_euclid <- names(tg$euclid)
+  expect_identical(tg_names_euclid, c("bins", "dist", "gamma", "np"))
+  expect_equal(unlist(tg$euclid[1, c("dist", "gamma", "np")]), c("dist" = 545.053, "gamma" = 0.742, "np" = 40), tolerance = 0.01)
   expect_invisible(plot(tg))
 })
 
 test_that("torgegram works (partition factor)", {
-  tg <- Torgegram(Summer_mn ~ ELEV_DEM, mf04p, partition_factor = ~ as.factor(netID))
-  tg_names <- names(tg$flowcon)
+  tg <- Torgegram(Summer_mn ~ ELEV_DEM, mf04p, partition_factor = ~ as.factor(netID), type = c("flowcon", "flowuncon", "euclid"))
+  expect_s3_class(tg, "Torgegram")
+  tg_names <- names(tg)
+  expect_identical(c("flowcon", "flowuncon", "euclid"), tg_names)
+  tg_names_flowcon <- names(tg$flowcon)
+  expect_identical(tg_names_flowcon, c("bins", "dist", "gamma", "np"))
   expect_s3_class(tg$flowcon, "data.frame")
-  expect_equal(names(tg$flowcon), tg_names)
+  expect_equal(unlist(tg$flowcon[1, c("dist", "gamma", "np")]), c("dist" = 409.648, "gamma" = 0.167, "np" = 11), tolerance = 0.01)
+  tg_names_flowuncon <- names(tg$flowuncon)
+  expect_identical(tg_names_flowuncon, c("bins", "dist", "gamma", "np"))
   expect_s3_class(tg$flowuncon, "data.frame")
-  expect_equal(names(tg$flowuncon), tg_names)
-  expect_false("euclid" %in% names(tg))
+  expect_equal(unlist(tg$flowuncon[1, c("dist", "gamma", "np")]), c("dist" = 487.595, "gamma" = 2.739, "np" = 6), tolerance = 0.01)
+  tg_names_euclid <- names(tg$euclid)
+  expect_identical(tg_names_euclid, c("bins", "dist", "gamma", "np"))
+  expect_equal(unlist(tg$euclid[1, c("dist", "gamma", "np")]), c("dist" = 310.580, "gamma" = 0.526, "np" = 17), tolerance = 0.01)
   expect_invisible(plot(tg))
 })
diff --git a/tests/testthat/test-dist_objects.R b/tests/testthat/test-dist_objects.R
index ff509ed..fd59f8b 100644
--- a/tests/testthat/test-dist_objects.R
+++ b/tests/testthat/test-dist_objects.R
@@ -1,14 +1,3 @@
-# Copy the mf04p .ssn data to a local directory and read it into R
-# When modeling with your .ssn object, you will load it using the relevant
-# path to the .ssn data on your machine
-copy_lsn_to_temp()
-temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-mf04p <- ssn_import(
-  temp_path,
-  predpts = c("pred1km", "CapeHorn", "Knapp"),
-  overwrite = TRUE
-)
-
 ssn_create_distmat(
   ssn.object = mf04p,
   predpts = c("pred1km"),
@@ -50,7 +39,7 @@ test_that("dist object output appropriate", {
   n_obs_dim <- c(n_obs, n_obs)
 
   # run test on object structure
-  expect_true(identical(names(object), names_vec))
+  expect_identical(names(object), names_vec)
   expect_equal(dim(object$distjunc_mat), n_obs_dim)
   expect_equal(dim(object$mask_mat), n_obs_dim)
   expect_equal(dim(object$a_mat), n_obs_dim)
@@ -58,10 +47,10 @@ test_that("dist object output appropriate", {
   expect_equal(dim(object$hydro_mat), n_obs_dim)
   expect_equal(dim(object$w_mat), n_obs_dim)
   expect_equal(dim(object$euclid_mat), n_obs_dim)
-  expect_true(is.vector(object$network_index))
-  expect_true(is.vector(object$pid))
-  expect_true(is.vector(object$dist_order))
-  expect_true(is.vector(object$inv_dist_order))
+  expect_equal(length(object$network_index), n_obs)
+  expect_equal(length(object$pid), n_obs)
+  expect_equal(length(object$dist_order), n_obs)
+  expect_equal(length(object$inv_dist_order), n_obs)
 })
 
 test_that("dist pred object output appropriate", {
@@ -86,7 +75,7 @@ test_that("dist pred object output appropriate", {
   n_dim <- c(n_pred, n_obs)
 
   # run test on object structure
-  expect_true(identical(names(dist_pred_object), names_vec))
+  expect_identical(names(dist_pred_object), names_vec)
   expect_equal(dim(dist_pred_object$distjunca_pred_mat), n_dim)
   expect_equal(dim(t(dist_pred_object$distjuncb_pred_mat)), n_dim)
   expect_equal(dim(dist_pred_object$mask_pred_mat), n_dim)
@@ -95,14 +84,14 @@ test_that("dist pred object output appropriate", {
   expect_equal(dim(dist_pred_object$hydro_pred_mat), n_dim)
   expect_equal(dim(dist_pred_object$w_pred_mat), n_dim)
   expect_equal(dim(dist_pred_object$euclid_pred_mat), n_dim)
-  expect_true(is.vector(dist_pred_object$network_index))
-  expect_true(is.vector(dist_pred_object$pid))
-  expect_true(is.vector(dist_pred_object$dist_order))
-  expect_true(is.vector(dist_pred_object$inv_dist_order))
-  expect_true(is.vector(dist_pred_object$network_index_pred))
-  expect_true(is.vector(dist_pred_object$pid_pred))
-  expect_true(is.vector(dist_pred_object$dist_order_pred))
-  expect_true(is.vector(dist_pred_object$inv_dist_order_pred))
+  expect_equal(length(dist_pred_object$network_index), n_obs)
+  expect_equal(length(dist_pred_object$pid), n_obs)
+  expect_equal(length(dist_pred_object$dist_order), n_obs)
+  expect_equal(length(dist_pred_object$inv_dist_order), n_obs)
+  expect_equal(length(dist_pred_object$network_index_pred), n_pred)
+  expect_equal(length(dist_pred_object$pid_pred), n_pred)
+  expect_equal(length(dist_pred_object$dist_order_pred), n_pred)
+  expect_equal(length(dist_pred_object$inv_dist_order_pred), n_pred)
 })
 
 test_that("dist pred bk object output appropriate", {
@@ -123,7 +112,7 @@ test_that("dist pred bk object output appropriate", {
   n_pred_dim <- c(n_pred, n_pred)
 
   # run test on object structure
-  expect_true(identical(names(object), names_vec))
+  expect_identical(names(object), names_vec)
   expect_equal(dim(object$distjunc_mat), n_pred_dim)
   expect_equal(dim(object$mask_mat), n_pred_dim)
   expect_equal(dim(object$a_mat), n_pred_dim)
@@ -131,8 +120,8 @@ test_that("dist pred bk object output appropriate", {
   expect_equal(dim(object$hydro_mat), n_pred_dim)
   expect_equal(dim(object$w_mat), n_pred_dim)
   expect_equal(dim(object$euclid_mat), n_pred_dim)
-  expect_true(is.vector(object$network_index))
-  expect_true(is.vector(object$pid))
-  expect_true(is.vector(object$dist_order))
-  expect_true(is.vector(object$inv_dist_order))
+  expect_equal(length(object$network_index), n_pred)
+  expect_equal(length(object$pid), n_pred)
+  expect_equal(length(object$dist_order), n_pred)
+  expect_equal(length(object$inv_dist_order), n_pred)
 })
diff --git a/tests/testthat/test-extras-ssn_glm.R b/tests/testthat/test-extras-ssn_glm.R
index c4e7d33..f70bb3e 100644
--- a/tests/testthat/test-extras-ssn_glm.R
+++ b/tests/testthat/test-extras-ssn_glm.R
@@ -1,152 +1,164 @@
-test_that("blank test", {
-  expect_null(NULL)
-})
-
-test_local <- FALSE # FALSE for CRAN
-
-if (test_local) {
-  # Copy the mf04p .ssn data to a local directory and read it into R
-  # When modeling with your .ssn object, you will load it using the relevant
-  # path to the .ssn data on your machine
-  copy_lsn_to_temp()
-  temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-  mf04p <- ssn_import(
-    temp_path,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
-
-  ssn_create_distmat(
-    ssn.object = mf04p,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
-
-  # set a seed
-  set.seed(2)
-
-  test_that("ssn_glm models fit Gaussian", {
-    ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM,
-      family = "gaussian", mf04p, tailup_type = "exponential",
-      taildown_type = "exponential", euclid_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea"
-    )
-    expect_s3_class(ssn_mod, "ssn_lm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("ssn_glm models fit poisson", {
-    ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM,
-      family = "poisson", mf04p, tailup_type = "exponential",
-      taildown_type = "exponential", euclid_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea"
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("ssn_glm models fit negative binomial", {
-    ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM,
-      family = "nbinomial", mf04p, tailup_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea"
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("ssn_glm models fit binomial", {
-    ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM,
-      family = "binomial", mf04p, tailup_type = "exponential",
-      taildown_type = "exponential", euclid_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea", estmethod = "ml"
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("ssn_glm models fit beta", {
-    mf04p$obs$betavar <- runif(NROW(mf04p$obs), min = 0.25, max = 0.75)
-    ssn_mod <- ssn_glm(betavar ~ ELEV_DEM,
-      family = "beta", mf04p,
-      taildown_type = "exponential", euclid_type = "exponential",
-      nugget_type = "nugget"
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("ssn_glm models fit gamma", {
-    ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM,
-      family = "Gamma", mf04p, tailup_type = "exponential",
-      nugget_type = "none", additive = "afvArea"
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("ssn_glm models fit inverse gaussian", {
-    ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, inverse.gaussian,
-     euclid_type = "exponential",
-      nugget_type = "nugget",
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-
-  test_that("random effects work", {
-    ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
-      family = "binomial", tailup_type = "exponential",
-      taildown_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea",
-      random = ~ as.factor(netID)
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("partition factors work", {
-    ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
-      family = "binomial",
-      taildown_type = "exponential",
-      partition_factor = ~ as.factor(netID)
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("anisotropy works", {
-    ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
-      family = "binomial", tailup_type = "exponential",
-      euclid_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea",
-      anisotropy = TRUE
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("fixing parameters works", {
-    tu <- tailup_initial("exponential", de = 1, known = "de")
-    ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
-      family = "binomial", tailup_initial = tu,
-      taildown_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea"
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-    expect_equal(coef(ssn_mod, type = "tailup")[["de"]], 1)
-  })
-
-  test_that("missing data works", {
-    mf04p$obs$Summer_mn[1] <- NA
-    ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
-      family = "binomial",
-      taildown_type = "exponential",
-      nugget_type = "nugget"
-    )
-    expect_s3_class(ssn_mod, "ssn_glm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-    expect_vector(predict(ssn_mod, ".missing"))
-  })
-}
+#
+#
+# test_local <- FALSE # FALSE for CRAN
+#
+# if (test_local) {
+#
+#   ssn_create_distmat(
+#     ssn.object = mf04p,
+#     predpts = c("pred1km", "CapeHorn"),
+#     overwrite = TRUE
+#   )
+#
+#   # set a seed
+#   set.seed(2)
+#
+#   test_that("ssn_glm models fit Gaussian", {
+#     ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM,
+#       family = "gaussian", mf04p, tailup_type = "exponential",
+#       taildown_type = "exponential", euclid_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_lm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("ssn_glm models fit poisson", {
+#     ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM,
+#       family = "poisson", mf04p, tailup_type = "exponential",
+#       taildown_type = "exponential", euclid_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("ssn_glm models fit negative binomial", {
+#     ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM,
+#       family = "nbinomial", mf04p, tailup_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("ssn_glm models fit binomial", {
+#     ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM,
+#       family = "binomial", mf04p, tailup_type = "exponential",
+#       taildown_type = "exponential", euclid_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea", estmethod = "ml"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("ssn_glm models fit beta", {
+#     mf04p$obs$betavar <- runif(NROW(mf04p$obs), min = 0.25, max = 0.75)
+#     ssn_mod <- ssn_glm(betavar ~ ELEV_DEM,
+#       family = "beta", mf04p,
+#       taildown_type = "exponential", euclid_type = "exponential",
+#       nugget_type = "nugget"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("ssn_glm models fit gamma", {
+#     ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM,
+#       family = "Gamma", mf04p, tailup_type = "exponential",
+#       nugget_type = "none", additive = "afvArea"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("ssn_glm models fit inverse gaussian", {
+#     ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, inverse.gaussian,
+#      euclid_type = "exponential",
+#       nugget_type = "nugget",
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#
+#   test_that("random effects work", {
+#     ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
+#       family = "binomial", tailup_type = "exponential",
+#       taildown_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea",
+#       random = ~ as.factor(netID)
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("partition factors work", {
+#     ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
+#       family = "binomial",
+#       taildown_type = "exponential",
+#       partition_factor = ~ as.factor(netID)
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("anisotropy works", {
+#     ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
+#       family = "binomial", tailup_type = "exponential",
+#       euclid_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea",
+#       anisotropy = TRUE
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("fixing parameters works", {
+#     tu <- tailup_initial("exponential", de = 1, known = "de")
+#     ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
+#       family = "binomial", tailup_initial = tu,
+#       taildown_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#     expect_equal(coef(ssn_mod, type = "tailup")[["de"]], 1)
+#   })
+#
+#   test_that("missing data works", {
+#     mf04p$obs$Summer_mn[1] <- NA
+#     ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p,
+#       family = "binomial",
+#       taildown_type = "exponential",
+#       nugget_type = "nugget"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#     expect_vector(predict(ssn_mod, ".missing"))
+#   })
+#
+#   # previously from test-extras
+#   test_that("extra test fits", {
+#     ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p, family = "binomial",
+#                        tailup_type = "exponential", additive = "afvArea",
+#                        estmethod = "ml")
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#
+#     ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM, mf04p, family = "poisson",
+#                        taildown_type = "exponential")
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#
+#     ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM, mf04p, family = "nbinomial",
+#                        euclid_type = "exponential")
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#
+#     ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, family = "inverse.gaussian",
+#                        tailup_type = "exponential", additive = "afvArea")
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#
+#     ssn_mod <- ssn_glm(ratio ~ ELEV_DEM, mf04p, family = "beta",
+#                        taildown_type = "exponential")
+#     expect_s3_class(ssn_mod, "ssn_glm")
+#   })
+# }
diff --git a/tests/testthat/test-extras-ssn_lm.R b/tests/testthat/test-extras-ssn_lm.R
index 82b7f09..1e731ef 100644
--- a/tests/testthat/test-extras-ssn_lm.R
+++ b/tests/testthat/test-extras-ssn_lm.R
@@ -1,78 +1,83 @@
-test_that("blank test", {
-  expect_null(NULL)
-})
-
-test_local <- FALSE # FALSE for CRAN
-
-if (test_local) {
-  # Copy the mf04p .ssn data to a local directory and read it into R
-  # When modeling with your .ssn object, you will load it using the relevant
-  # path to the .ssn data on your machine
-  copy_lsn_to_temp()
-  temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-  mf04p <- ssn_import(
-    temp_path,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
-
-  ssn_create_distmat(
-    ssn.object = mf04p,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
-
-  test_that("random effects work", {
-    ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-      tailup_type = "exponential",
-      taildown_type = "exponential", euclid_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea",
-      random = ~ as.factor(netID)
-    )
-    expect_s3_class(ssn_mod, "ssn_lm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("partition factors work", {
-    ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-      tailup_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea",
-      partition_factor = ~ as.factor(netID)
-    )
-    expect_s3_class(ssn_mod, "ssn_lm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("anisotropy works", {
-    ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-      taildown_type = "exponential",
-      nugget_type = "nugget", estmethod = "ml",
-      anisotropy = TRUE
-    )
-    expect_s3_class(ssn_mod, "ssn_lm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-  })
-
-  test_that("fixing parameters works", {
-    tu <- tailup_initial("exponential", de = 1, known = "de")
-    ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-      tailup_initial = tu,
-      taildown_type = "exponential", euclid_type = "exponential",
-      nugget_type = "none", additive = "afvArea"
-    )
-    expect_s3_class(ssn_mod, "ssn_lm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-    expect_equal(coef(ssn_mod, type = "tailup")[["de"]], 1)
-  })
-
-  test_that("missing data works", {
-    mf04p$obs$Summer_mn[1] <- NA
-    ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-      tailup_type = "exponential",
-      nugget_type = "nugget", additive = "afvArea"
-    )
-    expect_s3_class(ssn_mod, "ssn_lm")
-    expect_vector(predict(ssn_mod, "pred1km"))
-    expect_vector(predict(ssn_mod, ".missing"))
-  })
-}
+# test_local <- FALSE # FALSE for CRAN
+#
+# if (test_local) {
+#
+#   ssn_create_distmat(
+#     ssn.object = mf04p,
+#     predpts = c("pred1km", "CapeHorn"),
+#     overwrite = TRUE
+#   )
+#
+#   test_that("random effects work", {
+#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
+#       tailup_type = "exponential",
+#       taildown_type = "exponential", euclid_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea",
+#       random = ~ as.factor(netID)
+#     )
+#     expect_s3_class(ssn_mod, "ssn_lm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("partition factors work", {
+#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
+#       tailup_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea",
+#       partition_factor = ~ as.factor(netID)
+#     )
+#     expect_s3_class(ssn_mod, "ssn_lm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("anisotropy works", {
+#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
+#       taildown_type = "exponential",
+#       nugget_type = "nugget", estmethod = "ml",
+#       anisotropy = TRUE
+#     )
+#     expect_s3_class(ssn_mod, "ssn_lm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#   })
+#
+#   test_that("fixing parameters works", {
+#     tu <- tailup_initial("exponential", de = 1, known = "de")
+#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
+#       tailup_initial = tu,
+#       taildown_type = "exponential", euclid_type = "exponential",
+#       nugget_type = "none", additive = "afvArea"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_lm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#     expect_equal(coef(ssn_mod, type = "tailup")[["de"]], 1)
+#   })
+#
+#   test_that("missing data works", {
+#     mf04p$obs$Summer_mn[1] <- NA
+#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
+#       tailup_type = "exponential",
+#       nugget_type = "nugget", additive = "afvArea"
+#     )
+#     expect_s3_class(ssn_mod, "ssn_lm")
+#     expect_vector(predict(ssn_mod, "pred1km"))
+#     expect_vector(predict(ssn_mod, ".missing"))
+#   })
+#
+#
+#   # previously from test-extras
+#   test_that("extra test fits", {
+#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p, family = "binomial",
+#                       tailup_type = "exponential", additive = "afvArea",
+#                       random = ~ as.factor(netID))
+#     expect_output(print(ssn_mod))
+#     expect_output(print(summary(ssn_mod)))
+#     expect_type(fitted(ssn_mod, type = "randcov"), "list")
+#
+#     ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, family = "Gamma",
+#                        random = ~ as.factor(netID))
+#     expect_output(print(ssn_mod))
+#     expect_output(print(summary(ssn_mod)))
+#     expect_type(fitted(ssn_mod, type = "randcov"), "list")
+#
+#   })
+#
+# }
diff --git a/tests/testthat/test-extras.R b/tests/testthat/test-extras.R
index 3316bc3..4f49eb0 100644
--- a/tests/testthat/test-extras.R
+++ b/tests/testthat/test-extras.R
@@ -1,16 +1,4 @@
-copy_lsn_to_temp()
-temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-mf04p <- ssn_import(
-  temp_path,
-  predpts = c("pred1km"),
-  overwrite = TRUE
-)
-
 test_that("covariance matrix functions run", {
-
-
-
-
   ################
   ##### create distance object
   ################
@@ -40,19 +28,28 @@ test_that("covariance matrix functions run", {
     anisotropy = TRUE
   )
 
+  n_obs <- NROW(mf04p$obs)
+  n_obs_dim <- c(n_obs, n_obs)
+
   ################
   ##### tailup
   ################
 
-  tailup_covs <- c("linear", "spherical", "exponential",
-                   "mariah", "epa", "none")
+  tailup_covs <- c(
+    "linear", "spherical", "exponential",
+    "mariah", "epa", "none"
+  )
   lapply(tailup_covs, function(x) {
     if (x == "none") {
       params <- tailup_params(x)
-      expect_equal(cov_matrix(params, object), 0)
+      covmx <- cov_matrix(params, object)
+      expect_equal(covmx, 0)
     } else {
-      params <- tailup_params(x, de = 1, range = 1)
-      expect_equal(dim(cov_matrix(params, object)), c(45, 45))
+      de <- 1
+      params <- tailup_params(x, de = de, range = 1)
+      covmx <- cov_matrix(params, object)
+      expect_equal(dim(covmx), n_obs_dim)
+      expect_equal(unique(diag(covmx)), de) # de value is overall variance
     }
   })
 
@@ -60,15 +57,21 @@ test_that("covariance matrix functions run", {
   ##### taildown
   ################
 
-  taildown_covs <- c("linear", "spherical", "exponential",
-                   "mariah", "epa", "none")
+  taildown_covs <- c(
+    "linear", "spherical", "exponential",
+    "mariah", "epa", "none"
+  )
   lapply(taildown_covs, function(x) {
     if (x == "none") {
       params <- taildown_params(x)
-      expect_equal(cov_matrix(params, object), 0)
+      covmx <- cov_matrix(params, object)
+      expect_equal(covmx, 0)
     } else {
-      params <- taildown_params(x, de = 1, range = 1)
-      expect_equal(dim(cov_matrix(params, object)), c(45, 45))
+      de <- 1
+      params <- taildown_params(x, de = de, range = 1)
+      covmx <- cov_matrix(params, object)
+      expect_equal(dim(covmx), n_obs_dim)
+      expect_equal(unique(diag(covmx)), de)
     }
   })
 
@@ -76,19 +79,28 @@ test_that("covariance matrix functions run", {
   ##### euclid
   ################
 
-  euclid_covs <- c("exponential", "spherical", "gaussian", "cubic",
-                    "pentaspherical", "cosine", "wave", "jbessel", "gravity",
-                   "rquad", "magnetic", "none")
+  euclid_covs <- c(
+    "exponential", "spherical", "gaussian", "cubic",
+    "pentaspherical", "cosine", "wave", "jbessel", "gravity",
+    "rquad", "magnetic", "none"
+  )
   lapply(euclid_covs, function(x) {
     if (x == "none") {
       params <- euclid_params(x)
-      expect_equal(cov_matrix(params, object), 0)
+      covmx <- cov_matrix(params, object)
+      expect_equal(covmx, 0)
     } else if (x == "spherical") {
-      params <- euclid_params(x, de = 1, range = 1, rotate = 0.5, scale = 0.5)
-      expect_equal(dim(cov_matrix(params, object_anis, anisotropy = TRUE)), c(45, 45))
+      de <- 1
+      params <- euclid_params(x, de = de, range = 1, rotate = 0.5, scale = 0.5)
+      covmx <- cov_matrix(params, object_anis, anisotropy = TRUE)
+      expect_equal(dim(covmx), n_obs_dim)
+      expect_equal(unique(diag(covmx)), de)
     } else {
+      de <- 1
       params <- euclid_params(x, de = 1, range = 1, rotate = 0, scale = 1)
-      expect_equal(dim(cov_matrix(params, object, anisotropy = FALSE)), c(45, 45))
+      covmx <- cov_matrix(params, object, anisotropy = FALSE)
+      expect_equal(dim(covmx), n_obs_dim)
+      expect_equal(unique(diag(covmx)), de)
     }
   })
 
@@ -100,27 +112,27 @@ test_that("covariance matrix functions run", {
   lapply(nugget_covs, function(x) {
     if (x == "none") {
       params <- nugget_params(x)
-      expect_equal(dim(cov_matrix(params, object, de_scale = 1)), c(45, 45))
+      covmx <- cov_matrix(params, object, de_scale = 1)
+      expect_equal(dim(covmx), n_obs_dim)
     } else {
       params <- nugget_params(x, nugget = 1)
-      expect_equal(dim(cov_matrix(params, object, de_scale = 1)), c(45, 45))
+      covmx <- cov_matrix(params, object, de_scale = 1)
+      expect_equal(dim(covmx), n_obs_dim)
     }
   })
-
 })
 
 test_that("covariance vector functions run", {
-
-
-
   ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-                    tailup_type = "exponential", taildown_type = "exponential",
-                    euclid_type = "exponential", anisotropy = FALSE,
-                    random = ~ as.factor(netID), additive = "afvArea",
-                    estmethod = "ml", partition_factor = ~ as.factor(netID))
+    tailup_type = "exponential", taildown_type = "exponential",
+    euclid_type = "exponential", anisotropy = FALSE,
+    random = ~ as.factor(netID), additive = "afvArea",
+    estmethod = "ml", partition_factor = ~ as.factor(netID)
+  )
 
   ssn_mod_anis <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-                         euclid_type = "exponential", anisotropy = TRUE)
+    euclid_type = "exponential", anisotropy = TRUE
+  )
 
 
   ################
@@ -161,20 +173,29 @@ test_that("covariance vector functions run", {
     initial_object = initial_object_val
   )
 
+  n_obs <- NROW(ssn_mod$ssn.object$obs)
+  n_pred <- NROW(ssn_mod$ssn.object$preds[["pred1km"]])
+  n_dim <- c(n_pred, n_obs)
+
 
   ################
   ##### tailup
   ################
 
-  tailup_covs <- c("linear", "spherical", "exponential",
-                   "mariah", "epa", "none")
+  tailup_covs <- c(
+    "linear", "spherical", "exponential",
+    "mariah", "epa", "none"
+  )
   lapply(tailup_covs, function(x) {
     if (x == "none") {
       params <- tailup_params(x)
-      expect_equal(cov_vector(params, object), 0)
+      covv <- cov_vector(params, object)
+      expect_equal(covv, 0)
     } else {
-      params <- tailup_params(x, de = 1, range = 1)
-      expect_equal(dim(cov_vector(params, object)), c(175, 45))
+      de <- 1
+      params <- tailup_params(x, de = de, range = 1)
+      covv <- cov_vector(params, object)
+      expect_equal(dim(covv), n_dim)
     }
   })
 
@@ -182,15 +203,20 @@ test_that("covariance vector functions run", {
   ##### taildown
   ################
 
-  taildown_covs <- c("linear", "spherical", "exponential",
-                     "mariah", "epa", "none")
+  taildown_covs <- c(
+    "linear", "spherical", "exponential",
+    "mariah", "epa", "none"
+  )
   lapply(taildown_covs, function(x) {
     if (x == "none") {
       params <- taildown_params(x)
-      expect_equal(cov_vector(params, object), 0)
+      covv <- cov_vector(params, object)
+      expect_equal(covv, 0)
     } else {
-      params <- taildown_params(x, de = 1, range = 1)
-      expect_equal(dim(cov_vector(params, object)), c(175, 45))
+      de <- 1
+      params <- taildown_params(x, de = de, range = 1)
+      covv <- cov_vector(params, object)
+      expect_equal(dim(covv), n_dim)
     }
   })
 
@@ -198,19 +224,26 @@ test_that("covariance vector functions run", {
   ##### euclid
   ################
 
-  euclid_covs <- c("exponential", "spherical", "gaussian", "cubic",
-                   "pentaspherical", "cosine", "wave", "jbessel", "gravity",
-                   "rquad", "magnetic", "none")
+  euclid_covs <- c(
+    "exponential", "spherical", "gaussian", "cubic",
+    "pentaspherical", "cosine", "wave", "jbessel", "gravity",
+    "rquad", "magnetic", "none"
+  )
   lapply(euclid_covs, function(x) {
     if (x == "none") {
       params <- euclid_params(x)
-      expect_equal(cov_vector(params, object), 0)
+      covv <- cov_vector(params, object)
+      expect_equal(covv, 0)
     } else if (x == "spherical") {
+      de <- 1
       params <- euclid_params(x, de = 1, range = 1, rotate = 0.5, scale = 0.5)
-      expect_equal(dim(cov_vector(params, object_anis, anisotropy = TRUE)), c(175, 45))
+      covv <- cov_vector(params, object_anis, anisotropy = TRUE)
+      expect_equal(dim(covv), n_dim)
     } else {
+      de <- 1
       params <- euclid_params(x, de = 1, range = 1, rotate = 0, scale = 1)
-      expect_equal(dim(cov_vector(params, object, anisotropy = FALSE)), c(175, 45))
+      covv <- cov_vector(params, object, anisotropy = FALSE)
+      expect_equal(dim(covv), n_dim)
     }
   })
 
@@ -218,33 +251,48 @@ test_that("covariance vector functions run", {
   ##### prediction
   ################
 
-  expect_no_error(predict(ssn_mod, "pred1km", interval = "prediction", level = 0.9))
-  expect_no_error(predict(ssn_mod_anis, "pred1km"))
-
-
-
+  preds <- predict(ssn_mod, "pred1km", interval = "prediction", level = 0.9)
+  expect_equal(dim(preds), c(n_pred, 3))
+  expect_identical(colnames(preds), c("fit", "lwr", "upr"))
+  preds_anis <- predict(ssn_mod_anis, "pred1km")
+  expect_equal(length(preds_anis), n_pred)
 })
 
 
 test_that("initial objects", {
-  tu <- tailup_initial("exponential", de = 1, range = 1, known = "given")
-  td <- taildown_initial("exponential", de = 1, range = 1, known = c("de", "range"))
-  eu <- euclid_initial("exponential", de = 1, range = 1,
-                       rotate = 0, scale = 1, known = "given")
-  nu <- nugget_initial("nugget", 1, known = "nugget")
-  disp <- dispersion_initial("Gamma", dispersion = 1, known = "given")
+  de <- 1
+  range <- 1
+  nugget <- 1
+  dispersion <- 1
+  tu <- tailup_initial("exponential", de = de, range = range, known = "given")
+  td <- taildown_initial("exponential", de = de, range = range, known = c("de", "range"))
+  eu <- euclid_initial("exponential",
+    de = de, range = range,
+    rotate = 0, scale = 1, known = "given"
+  )
+  nu <- nugget_initial("nugget", nugget, known = "nugget")
+  disp <- dispersion_initial("Gamma", dispersion = dispersion, known = "given")
   ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
-                    tailup_initial = tu, taildown_initial = td,
-                    euclid_initial = eu, nugget_initial = nu,
-                    additive = "afvArea")
-  expect_s3_class(ssn_mod, "ssn_lm")
+    tailup_initial = tu, taildown_initial = td,
+    euclid_initial = eu, nugget_initial = nu,
+    additive = "afvArea"
+  )
+  expect_equal(as.vector(coef(ssn_mod, "tailup")), c(de, range))
+  expect_equal(as.vector(coef(ssn_mod, "taildown")), c(de, range))
+  expect_equal(as.vector(coef(ssn_mod, "euclid")), c(de, range, 0, 1))
+  expect_equal(as.vector(coef(ssn_mod, "nugget")), c(de))
 
   ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p,
-                    tailup_initial = tu, taildown_initial = td,
-                    euclid_initial = eu, nugget_initial = nu,
-                    dispersion_initial = disp,
-                    additive = "afvArea")
-  expect_s3_class(ssn_mod, "ssn_glm")
+    tailup_initial = tu, taildown_initial = td,
+    euclid_initial = eu, nugget_initial = nu,
+    dispersion_initial = disp,
+    additive = "afvArea"
+  )
+  expect_equal(as.vector(coef(ssn_mod, "tailup")), c(de, range))
+  expect_equal(as.vector(coef(ssn_mod, "taildown")), c(de, range))
+  expect_equal(as.vector(coef(ssn_mod, "euclid")), c(de, range, 0, 1))
+  expect_equal(as.vector(coef(ssn_mod, "nugget")), c(de))
+  expect_equal(as.vector(coef(ssn_mod, "dispersion")), c(dispersion))
 })
 
 
@@ -252,43 +300,5 @@ test_that("print an ssn", {
   expect_output(print(mf04p))
   expect_output(print(summary(mf04p)))
   expect_output(print(names(mf04p)))
-})
-
-
-
-test_that("extra test fits", {
-  ssn_mod <- ssn_glm(Summer_mn > 11 ~ ELEV_DEM, mf04p, family = "binomial",
-                     tailup_type = "exponential", additive = "afvArea",
-                     estmethod = "ml")
-  expect_s3_class(ssn_mod, "ssn_glm")
-
-  ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM, mf04p, family = "poisson",
-                     taildown_type = "exponential")
-  expect_s3_class(ssn_mod, "ssn_glm")
-
-  ssn_mod <- ssn_glm(round(Summer_mn) ~ ELEV_DEM, mf04p, family = "nbinomial",
-                     euclid_type = "exponential")
-  expect_s3_class(ssn_mod, "ssn_glm")
-
-  ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, family = "inverse.gaussian",
-                     tailup_type = "exponential", additive = "afvArea")
-  expect_s3_class(ssn_mod, "ssn_glm")
-
-  ssn_mod <- ssn_glm(ratio ~ ELEV_DEM, mf04p, family = "beta",
-                     taildown_type = "exponential")
-  expect_s3_class(ssn_mod, "ssn_glm")
-
-  ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p, family = "binomial",
-                     tailup_type = "exponential", additive = "afvArea",
-                     random = ~ as.factor(netID))
-  expect_output(print(ssn_mod))
-  expect_output(print(summary(ssn_mod)))
-  expect_type(fitted(ssn_mod, type = "randcov"), "list")
-
-  ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, family = "Gamma",
-                    random = ~ as.factor(netID))
-  expect_output(print(ssn_mod))
-  expect_output(print(summary(ssn_mod)))
-  expect_type(fitted(ssn_mod, type = "randcov"), "list")
-
+  expect_output(print(ssn_names(mf04p)))
 })
diff --git a/tests/testthat/test-predict.R b/tests/testthat/test-predict.R
index bff6d68..97fc425 100644
--- a/tests/testthat/test-predict.R
+++ b/tests/testthat/test-predict.R
@@ -1,41 +1,31 @@
 test_that("prediction works", {
-  # Copy the mf04p .ssn data to a local directory and read it into R
-  # When modeling with your .ssn object, you will load it using the relevant
-  # path to the .ssn data on your machine
-
-  copy_lsn_to_temp()
-  temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-  mf04p <- ssn_import(
-    temp_path,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
-
-  ssn_create_distmat(
-    ssn.object = mf04p,
-    predpts = c("pred1km"),
-    overwrite = TRUE,
-    among_predpts = TRUE
-  )
-
-  ssn_create_distmat(
-    ssn.object = mf04p,
-    predpts = c("CapeHorn", "Knapp"),
-    overwrite = TRUE,
-    only_predpts = TRUE
-  )
-
   ssn_mod1 <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
     tailup_type = "exponential",
     taildown_type = "exponential", euclid_type = "exponential",
     nugget_type = "nugget", additive = "afvArea"
   )
 
-  expect_vector(predict(ssn_mod1, "CapeHorn"))
-  expect_vector(predict(ssn_mod1, "pred1km", block = TRUE))
-  expect_true(inherits(predict(ssn_mod1, "Knapp", interval = "confidence"), "matrix"))
-  expect_true(inherits(predict(ssn_mod1, "pred1km", interval = "prediction", level = 0.9), "matrix"))
-  expect_type(predict(ssn_mod1), type = "list")
+  n_p1 <- nrow(mf04p$preds$pred1km)
+  n_CH <- nrow(mf04p$preds$CapeHorn)
+
+  preds_CH <- predict(ssn_mod1, "CapeHorn")
+  expect_equal(length(preds_CH), n_CH)
+  expect_equal(preds_CH[1:2], c("1" = 9.972, "2" = 9.973), tolerance = 0.01)
+
+  preds_p1_block <- predict(ssn_mod1, "pred1km", block = TRUE)
+  expect_equal(length(preds_p1_block), 1)
+  expect_equal(preds_p1_block, c("1" = 10.295), tolerance = 0.01)
+
+  preds_CH_conf <- predict(ssn_mod1, "CapeHorn", interval = "confidence")
+  expect_equal(dim(preds_CH_conf), c(n_CH, 3))
+  expect_equal(preds_CH_conf[1, ], c("fit" = 12.411, "lwr" = 9.951, "upr" = 14.871), tolerance = 0.01)
+
+  preds_p1_pred <- predict(ssn_mod1, "pred1km", interval = "prediction", level = 0.9)
+  expect_equal(dim(preds_p1_pred), c(n_p1, 3))
+  expect_equal(preds_p1_pred[1, ], c("fit" = 14.690, "lwr" = 14.416, "upr" = 14.964), tolerance = 0.01)
+
+  preds_list <- predict(ssn_mod1)
+  expect_identical(names(preds_list), c("pred1km", "CapeHorn"))
 
   ssn_mod2 <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, "Gamma",
     tailup_type = "exponential",
@@ -43,8 +33,22 @@ test_that("prediction works", {
     nugget_type = "nugget", additive = "afvArea"
   )
 
-  expect_vector(predict(ssn_mod2, "Knapp", type = "link"))
-  expect_true(inherits(predict(ssn_mod2, "pred1km", interval = "confidence", level = 0.9), "matrix"))
-  expect_true(inherits(predict(ssn_mod2, "CapeHorn", interval = "prediction"), "matrix"))
-  expect_type(predict(ssn_mod2), type = "list")
+  preds_CH <- predict(ssn_mod2, "CapeHorn")
+  expect_equal(length(preds_CH), n_CH)
+  expect_equal(preds_CH[1:2], c("1" = 2.301, "2" = 2.301), tolerance = 0.01)
+
+  preds_CH <- predict(ssn_mod2, "CapeHorn", type = "response")
+  expect_equal(length(preds_CH), n_CH)
+  expect_equal(preds_CH[1:2], c("1" = 9.992, "2" = 9.993), tolerance = 0.01)
+
+  preds_CH_conf <- predict(ssn_mod2, "CapeHorn", interval = "confidence")
+  expect_equal(dim(preds_CH_conf), c(n_CH, 3))
+  expect_equal(preds_CH_conf[1, ], c("fit" = 2.497, "lwr" = 2.318, "upr" = 2.676), tolerance = 0.01)
+
+  preds_p1_pred <- predict(ssn_mod2, "pred1km", interval = "prediction", level = 0.9)
+  expect_equal(dim(preds_p1_pred), c(n_p1, 3))
+  expect_equal(preds_p1_pred[1, ], c("fit" = 2.687, "lwr" = 2.672, "upr" = 2.701), tolerance = 0.01)
+
+  preds_list <- predict(ssn_mod2)
+  expect_identical(names(preds_list), c("pred1km", "CapeHorn"))
 })
diff --git a/tests/testthat/test-ssn-object.R b/tests/testthat/test-ssn-object.R
index c91b195..e0aad06 100644
--- a/tests/testthat/test-ssn-object.R
+++ b/tests/testthat/test-ssn-object.R
@@ -1,41 +1,114 @@
 test_that("ssn manipulation functions work", {
+  # test class of original SSN object
+  expect_s3_class(mf04p, "SSN")
+
 
-  expect_no_error(copy_lsn_to_temp())
+  tempdir_path <- paste0(tempdir(), "/MiddleFork04.ssn")
 
-  mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
-                      predpts = c("pred1km", "Knapp"),
-                      overwrite = TRUE
+  # copy lsn to temporary directory (does it exist?)
+  copy_lsn_to_temp()
+  # problem on linux with reading stated files from path
+  # expect_true(file.exists(tempdir_path))
+  # are the correct files present?
+  file_names <- c(
+    "binaryID.db", "CapeHorn.gpkg", "edges.gpkg", "netID1.dat",
+    "netID2.dat", "pred1km.gpkg", "sites.gpkg"
   )
-  expect_s3_class(mf04p, "SSN")
+  # problem on linux
+  # expect_true(all(file_names %in% list.files(tempdir_path)))
 
 
-  expect_no_error(ssn_import_predpts(mf04p, predpts = "CapeHorn"))
+  # import new SSN object from temporary LSN
+  mf04p_new <- ssn_import(tempdir_path,
+    predpts = c("pred1km"),
+    overwrite = TRUE
+  )
+  expect_s3_class(mf04p_new, "SSN")
+  expect_identical(names(mf04p_new), c("edges", "obs", "preds", "path"))
+  expect_s3_class(mf04p_new$edges, "sf")
+  expect_s3_class(mf04p_new$obs, "sf")
+  expect_s3_class(mf04p_new$preds$pred1km, "sf")
+  expect_identical(mf04p_new$path, tempdir_path)
 
-  expect_no_error(ssn_update_path(mf04p, mf04p$path))
+  # import capehorn prediction data
+  mf04p_new <- ssn_import_predpts(mf04p_new, predpts = "CapeHorn")
+  expect_s3_class(mf04p_new$preds$CapeHorn, "sf")
 
-  expect_message(ssn_create_distmat(mf04p, predpts = c("pred1km")))
-  expect_no_error(ssn_create_distmat(mf04p, overwrite = TRUE))
-  expect_no_error(ssn_create_distmat(mf04p, predpts = c("pred1km", "Knapp"),
-                     overwrite = TRUE, among_predpts = TRUE, only_predpts = TRUE))
+  # update path
+  mf04p <- ssn_update_path(mf04p, tempdir_path)
+  expect_identical(mf04p$path, tempdir_path)
 
+  # create distance matrices
+  ssn_create_distmat(mf04p_new, predpts = c("pred1km"), overwrite = TRUE)
+  distance_path <- paste0(tempdir_path, "/distance")
+  # problem on linux
+  # expect_true(file.exists(distance_path))
+  # expect_true(all(c("obs", "pred1km") %in% list.files(distance_path)))
+  ssn_create_distmat(mf04p_new,
+    predpts = c("pred1km"),
+    overwrite = TRUE, among_predpts = TRUE, only_predpts = TRUE
+  )
+  obs_path <- paste0(distance_path, "/obs")
+  # problem on linux
+  # expect_true(file.exists(obs_path))
+  obs_files <- c("dist.net1.RData", "dist.net2.RData")
+  # expect_true(all(obs_files %in% list.files(obs_path)))
+  # problem on linux
+  pred1km_path <- paste0(distance_path, "/pred1km")
+  # expect_true(file.exists(pred1km_path))
+  pred1km_files <- c(
+    "dist.net1.RData", "dist.net2.RData", "dist.net1.a.RData",
+    "dist.net1.b.RData", "dist.net2.a.RData", "dist.net2.b.RData"
+  )
+  # expect_true(all(pred1km_files %in% list.files(pred1km_path)))
 
 
-  expect_no_error(ssn_subset(mf04p, path = paste0(tempdir(), "/subset1.ssn"),
-            subset = netID == 1, clip = TRUE, overwrite = TRUE))
+  # subset SSN data
+  subset_path <- paste0(tempdir(), "/subset1.ssn")
+  ssn_subset(mf04p_new,
+    path = subset_path,
+    subset = netID == 1, clip = TRUE, overwrite = TRUE
+  )
+  expect_true(file.exists(subset_path))
+  mf04p_new_subset <- ssn_import(subset_path, overwrite = TRUE)
+  expect_equal(unique(mf04p_new_subset$obs$netID), 1)
 
 
-  expect_no_error(ssn_split_predpts(mf04p, "pred1km", size_predpts = 200,
-                            keep = FALSE, overwrite = TRUE))
+  # split prediction points
+  mf04p_new_split <- ssn_split_predpts(mf04p, "pred1km",
+    size_predpts = 100,
+    keep = FALSE, overwrite = TRUE
+  )
+  expect_identical(names(mf04p_new_split$preds), c("CapeHorn", "pred1km-1", "pred1km-2"))
   mf04p$preds$pred1km$net.fac <- as.factor(mf04p$preds$pred1km$netID)
-  expect_no_error(ssn_split_predpts(mf04p, "pred1km", by = "net.fac", overwrite = TRUE))
-  expect_no_error(ssn_split_predpts(mf04p, "pred1km", subset = ratio > 0.5,
-                                    id_predpts = "RATIO_05", overwrite = TRUE))
+  mf04p_new_split <- ssn_split_predpts(mf04p, "pred1km",
+    by = "net.fac",
+    keep = FALSE, overwrite = TRUE
+  )
+  expect_identical(names(mf04p_new_split$preds), c("CapeHorn", "pred1km-net.fac-1", "pred1km-net.fac-2"))
+  mf04p_new_split <- ssn_split_predpts(mf04p, "pred1km",
+    subset = ratio > 0.5,
+    id_predpts = "RATIO_05", overwrite = TRUE
+  )
+  expect_identical(names(mf04p_new_split$preds), c("pred1km", "CapeHorn", "RATIO_05"))
+
+
+  mf04p_distmats <- ssn_get_stream_distmat(mf04p)
+  expect_identical(names(mf04p_distmats), c("dist.net1", "dist.net2"))
+  expect_identical(dim(mf04p_distmats$dist.net1), as.integer(c(13, 13)))
+  expect_identical(dim(mf04p_distmats$dist.net2), as.integer(c(32, 32)))
 
-  expect_no_error(ssn_get_stream_distmat(mf04p))
 
-  expect_no_error(ssn_write(mf04p, path = paste0(tempdir(), "/tempSSN.ssn"),
-                            overwrite = TRUE))
+  # get SSN data
+  mf04p_new_get <- ssn_get_data(mf04p_new)
+  expect_identical(mf04p_new_get, mf04p_new$obs)
 
-  expect_no_error(ssn_put_data(ssn_get_data(mf04p), mf04p))
+  # put SSN data
+  mf04p_new <- ssn_put_data(mf04p_new_get, mf04p_new)
+  expect_identical(mf04p_new_get, mf04p_new$obs)
 
+  # write SSN object
+  write_path <- paste0(tempdir_path, "/tempSSN.ssn")
+  ssn_write(mf04p, path = write_path, overwrite = TRUE)
+  # expect_true(file.exists(write_path))
 })
diff --git a/tests/testthat/test-ssn_glm.R b/tests/testthat/test-ssn_glm.R
index b821033..11c801d 100644
--- a/tests/testthat/test-ssn_glm.R
+++ b/tests/testthat/test-ssn_glm.R
@@ -1,21 +1,13 @@
 test_that("generics work ssn_glm point data", {
-  # Copy the mf04p .ssn data to a local directory and read it into R
-  # When modeling with your .ssn object, you will load it using the relevant
-  # path to the .ssn data on your machine
-  copy_lsn_to_temp()
-  temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-  mf04p <- ssn_import(
-    temp_path,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
+  set.seed(2)
 
-  ssn_mod1 <- ssn_glm(Summer_mn ~ ELEV_DEM,
+  form <- Summer_mn ~ ELEV_DEM
+  ssn_mod1 <- ssn_glm(form,
     family = "Gamma", mf04p, tailup_type = "exponential",
     taildown_type = "exponential", euclid_type = "exponential",
     nugget_type = "nugget", additive = "afvArea"
   )
-  ssn_mod2 <- ssn_glm(Summer_mn ~ ELEV_DEM,
+  ssn_mod2 <- ssn_glm(form,
     family = Gamma, mf04p, tailup_type = "exponential",
     taildown_type = "none", euclid_type = "none",
     nugget_type = "nugget", additive = "afvArea"
@@ -23,99 +15,168 @@ test_that("generics work ssn_glm point data", {
 
   # AIC
   expect_vector(AIC(ssn_mod1))
-  expect_s3_class(AIC(ssn_mod1, ssn_mod2), "data.frame") # turn reml fixed effects warning off
+  expect_equal(AIC(ssn_mod1), 181.112, tolerance = 0.01)
+  expect_s3_class(AIC(ssn_mod1, ssn_mod2), "data.frame")
+  expect_equal(AIC(ssn_mod1, ssn_mod2)$AIC, c(181.112, 181.888), tolerance = 0.01)
 
   # anova
-  expect_s3_class(anova(ssn_mod1), "data.frame")
-  expect_s3_class(anova(ssn_mod1), "anova.ssn_glm")
-  expect_s3_class(tidy(anova(ssn_mod1)), "data.frame")
-  expect_s3_class(anova(ssn_mod1, ssn_mod2), "data.frame")
-  expect_s3_class(anova(ssn_mod1, ssn_mod2), "anova.ssn_glm")
-  expect_s3_class(tidy(anova(ssn_mod1, ssn_mod2)), "data.frame")
+  anova1 <- anova(ssn_mod1)
+  expect_s3_class(anova1, "data.frame")
+  expect_s3_class(anova1, "anova.ssn_glm")
+  expect_s3_class(tidy(anova1), "data.frame")
+  expect_equal(tidy(anova1)$statistic, c(44.9, 19.9), tolerance = 0.01)
+  anova12 <- anova(ssn_mod1, ssn_mod2)
+  expect_s3_class(anova12, "data.frame")
+  expect_s3_class(anova12, "anova.ssn_glm")
+  expect_s3_class(tidy(anova12), "data.frame")
+  expect_equal(tidy(anova12)$statistic, 8.78, tolerance = 0.01)
 
   # augment
-  expect_s3_class(augment(ssn_mod1), "data.frame")
-  expect_s3_class(augment(ssn_mod1, newdata = "pred1km"), "data.frame")
+  aug_ssn_mod1 <- augment(ssn_mod1)
+  expect_s3_class(aug_ssn_mod1, "sf")
+  expect_true(all(c(".fitted", ".resid", ".hat", ".cooksd", ".std.resid") %in% names(aug_ssn_mod1)))
+  expect_equal(aug_ssn_mod1$.fitted[1], 14.918, tolerance = 0.01)
+  aug_pred_ssn_mod1 <- augment(ssn_mod1, newdata = "pred1km")
+  expect_s3_class(aug_pred_ssn_mod1, "sf")
+  expect_true(all(c(".fitted") %in% names(aug_ssn_mod1)))
+  expect_equal(aug_pred_ssn_mod1$.fitted[1], 2.687, tolerance = 0.01)
 
   # coef
   expect_vector(coef(ssn_mod1))
+  expect_equal(coef(ssn_mod1), c("(Intercept)" = 7.379, "ELEV_DEM" = -0.002), tolerance = 0.01)
   expect_s3_class(coef(ssn_mod1, type = "tailup"), "tailup_exponential")
+  expect_equal(unclass(coef(ssn_mod1, type = "tailup")), c("de" = 1.113e-02, "range" = 1.439e07), tolerance = 0.01)
+  expect_s3_class(coef(ssn_mod1, type = "taildown"), "taildown_exponential")
+  expect_equal(unclass(coef(ssn_mod1, type = "taildown")), c("de" = 1.826e-02, "range" = 7.101e04), tolerance = 0.01)
   expect_s3_class(coef(ssn_mod1, type = "euclid"), "euclid_exponential")
+  expect_equal(unclass(coef(ssn_mod1, type = "euclid")), c("de" = 1.158e-07, "range" = 4.94e03, "rotate" = 0, "scale" = 1), tolerance = 0.01)
   expect_type(coef(ssn_mod1, type = "ssn"), "list")
   expect_null(coef(ssn_mod1, type = "randcov"))
+  expect_vector(coef(ssn_mod1, type = "dispersion"))
+  expect_s3_class(coef(ssn_mod1, type = "dispersion"), "Gamma")
+  expect_equal(unclass(coef(ssn_mod1, type = "dispersion")), c("dispersion" = 82810.24), tolerance = 0.01)
+  expect_error(coef(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "fixed", "ssn", "tailup",  "taildown",  "euclid",  "nugget", "dispersion", or "randcov".')
+
+
+  # coefficients alias
   expect_vector(coefficients(ssn_mod1))
+  expect_equal(coefficients(ssn_mod1), c("(Intercept)" = 7.379, "ELEV_DEM" = -0.002), tolerance = 0.01)
+  expect_s3_class(coefficients(ssn_mod1, type = "tailup"), "tailup_exponential")
+  expect_equal(unclass(coefficients(ssn_mod1, type = "tailup")), c("de" = 1.113e-02, "range" = 1.439e07), tolerance = 0.01)
   expect_s3_class(coefficients(ssn_mod1, type = "taildown"), "taildown_exponential")
-  expect_s3_class(coef(ssn_mod1, type = "nugget"), "nugget_nugget")
+  expect_equal(unclass(coefficients(ssn_mod1, type = "taildown")), c("de" = 1.826e-02, "range" = 7.101e04), tolerance = 0.01)
+  expect_s3_class(coefficients(ssn_mod1, type = "euclid"), "euclid_exponential")
+  expect_equal(unclass(coefficients(ssn_mod1, type = "euclid")), c("de" = 1.158e-07, "range" = 4.94e03, "rotate" = 0, "scale" = 1), tolerance = 0.01)
+  expect_type(coefficients(ssn_mod1, type = "ssn"), "list")
   expect_null(coefficients(ssn_mod1, type = "randcov"))
+  expect_vector(coefficients(ssn_mod1, type = "dispersion"))
+  expect_s3_class(coefficients(ssn_mod1, type = "dispersion"), "Gamma")
+  expect_equal(unclass(coefficients(ssn_mod1, type = "dispersion")), c("dispersion" = 82810.24), tolerance = 0.01)
+  expect_error(coefficients(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "fixed", "ssn", "tailup",  "taildown",  "euclid",  "nugget", "dispersion", or "randcov".')
 
   # confint
-  expect_true(inherits(confint(ssn_mod1), "matrix"))
-  expect_true(inherits(confint(ssn_mod1, parm = c("x"), level = 0.9), "matrix"))
+  expect_equal(dim(confint(ssn_mod1)), c(2, 2))
+  expect_equal(dim(confint(ssn_mod1, parm = c("ELEV_DEM"), level = 0.9)), c(1, 2))
 
   # cooks.distance
   expect_vector(cooks.distance(ssn_mod1))
+  expect_equal(cooks.distance(ssn_mod1)[1], c("1" = 4.303e-10), tolerance = 0.01)
 
   # covmatrix
-  expect_true(inherits(covmatrix(ssn_mod1), "matrix"))
-  expect_true(inherits(covmatrix(ssn_mod1, "pred1km"), "matrix"))
-  expect_true(inherits(covmatrix(ssn_mod1, "pred1km", type = "obs.pred"), "matrix"))
-  expect_true(inherits(covmatrix(ssn_mod1, "pred1km", cov_type = "pred.pred"), "matrix"))
+  expect_equal(dim(covmatrix(ssn_mod1)), c(45, 45))
+  expect_equal(dim(covmatrix(ssn_mod1, "pred1km")), c(175, 45))
+  expect_equal(dim(covmatrix(ssn_mod1, "pred1km", cov_type = "obs.pred")), c(45, 175))
+  expect_equal(dim(covmatrix(ssn_mod1, "pred1km", cov_type = "pred.pred")), c(175, 175))
+  expect_error(covmatrix(ssn_mod1, "pred1km", cov_type = "error"), 'Invalid "cov_type" argument.')
 
   # deviance
   expect_vector(deviance(ssn_mod1))
+  expect_equal(deviance(ssn_mod1), 1.599e-05, tolerance = 0.01)
 
   # fitted
   expect_vector(fitted(ssn_mod1))
-  expect_vector(fitted(ssn_mod1, type = "link"))
+  expect_equal(fitted(ssn_mod1)[1], c("1" = 14.918), tolerance = 0.01)
   expect_vector(fitted(ssn_mod1, type = "tailup"))
+  expect_equal(fitted(ssn_mod1, type = "tailup")[1], c("1" = -0.01113), tolerance = 0.01)
   expect_vector(fitted(ssn_mod1, type = "taildown"))
+  expect_equal(fitted(ssn_mod1, type = "taildown")[1], c("1" = 0.061), tolerance = 0.01)
+  expect_vector(fitted(ssn_mod1, type = "euclid"))
+  expect_equal(fitted(ssn_mod1, type = "euclid")[1], c("1" = 3.574e-09), tolerance = 0.01)
   expect_null(fitted(ssn_mod1, type = "randcov"))
+  expect_error(fitted(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "response", "tailup",  "taildown",  "euclid",  "nugget", or "randcov".')
+
+  # fitted.values alias
   expect_vector(fitted.values(ssn_mod1))
-  expect_vector(fitted.values(ssn_mod1, type = "link"))
+  expect_equal(fitted.values(ssn_mod1)[1], c("1" = 14.918), tolerance = 0.01)
+  expect_vector(fitted.values(ssn_mod1, type = "tailup"))
+  expect_equal(fitted.values(ssn_mod1, type = "tailup")[1], c("1" = -0.01113), tolerance = 0.01)
+  expect_vector(fitted.values(ssn_mod1, type = "taildown"))
+  expect_equal(fitted.values(ssn_mod1, type = "taildown")[1], c("1" = 0.061), tolerance = 0.01)
   expect_vector(fitted.values(ssn_mod1, type = "euclid"))
-  expect_vector(fitted.values(ssn_mod1, type = "nugget"))
+  expect_equal(fitted.values(ssn_mod1, type = "euclid")[1], c("1" = 3.574e-09), tolerance = 0.01)
   expect_null(fitted.values(ssn_mod1, type = "randcov"))
+  expect_error(fitted.values(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "response", "tailup",  "taildown",  "euclid",  "nugget", or "randcov".')
+
 
   # formula
   expect_type(formula(ssn_mod1), "language")
+  expect_equal(formula(ssn_mod1), form)
 
   # getCall
   expect_type(getCall(ssn_mod1), "language")
+  expect_equal(getCall(ssn_mod1), ssn_mod1$call)
 
   # glance
-  expect_s3_class(glance(ssn_mod1), "data.frame")
+  glance_ssn_mod1 <- glance(ssn_mod1)
+  names_glance <- c("n", "p", "npar", "value", "AIC", "AICc", "logLik", "deviance", "pseudo.r.squared")
+  expect_s3_class(glance_ssn_mod1, "data.frame")
+  expect_equal(dim(glance_ssn_mod1), c(1, 9))
+  expect_identical(names_glance, names(glance_ssn_mod1))
 
   # glances
-  expect_s3_class(glances(ssn_mod1), "data.frame")
+  expect_identical(glance_ssn_mod1, glances(ssn_mod1)[, -1])
+  glance_ssn_mod12 <- glances(ssn_mod1, ssn_mod2)
   expect_s3_class(glances(ssn_mod1, ssn_mod2), "data.frame")
+  expect_equal(dim(glance_ssn_mod12), c(2, 10))
+  expect_identical(c("model", names_glance), names(glance_ssn_mod12))
 
   # hatvalues
   expect_vector(hatvalues(ssn_mod1))
+  expect_equal(hatvalues(ssn_mod1)[1], c("1" = 0.06171), tolerance = 0.01)
 
   # influence
-  expect_s3_class(influence(ssn_mod1), "data.frame")
+  infl_ssn_mod1 <- influence(ssn_mod1)
+  names_infl <- c(".resid", ".hat", ".cooksd", ".std.resid")
+  expect_s3_class(infl_ssn_mod1, "data.frame")
+  expect_equal(dim(infl_ssn_mod1), c(45, 4))
+  expect_identical(names_infl, names(infl_ssn_mod1))
 
   # labels
   expect_type(labels(ssn_mod1), "character")
+  expect_identical(labels(ssn_mod1), "ELEV_DEM")
 
   # logLik
   expect_vector(logLik(ssn_mod1))
+  expect_equal(logLik(ssn_mod1), -82.556, tolerance = 0.01)
 
   # loocv
-  expect_s3_class(loocv(ssn_mod1), "data.frame")
-  expect_type(loocv(ssn_mod1, cv_predict = TRUE, se.fit = TRUE), "list")
+  loocv_ssn_mod1 <- loocv(ssn_mod1)
+  expect_s3_class(loocv_ssn_mod1, "data.frame")
+  expect_identical(names(loocv_ssn_mod1), c("bias", "MSPE", "RMSPE", "RAV"))
+  expect_equal(loocv_ssn_mod1$MSPE, 0.243, tolerance = 0.01)
+  expect_identical(names(loocv(ssn_mod1, cv_predict = TRUE, se.fit = TRUE)), c("stats", "cv_predict", "se.fit"))
 
   # model.frame
-  expect_s3_class(model.frame(ssn_mod1), "data.frame")
+  expect_equal(dim(model.frame(ssn_mod1)), c(45, 2))
 
   # model.matrix
-  expect_true(inherits(model.matrix(ssn_mod1), "matrix"))
+  expect_equal(dim(model.matrix(ssn_mod1)), c(45, 2))
 
   # model.offset
   expect_null(model.offset(model.frame(ssn_mod1)))
 
   # model.response
-  expect_vector(model.response(model.frame(ssn_mod1)))
+  expect_length(model.response(model.frame(ssn_mod1)), 45)
 
   # plot
   expect_invisible(plot(ssn_mod1, which = 1))
@@ -126,10 +187,35 @@ test_that("generics work ssn_glm point data", {
   expect_invisible(plot(ssn_mod1, which = 6))
 
   # predict
-  expect_vector(predict(ssn_mod1, newdata = "pred1km"))
-  expect_type(predict(ssn_mod1, newdata = "pred1km", se.fit = TRUE), "list")
-  expect_type(predict(ssn_mod1, newdata = "pred1km", interval = "prediction", se.fit = TRUE), "list")
-  expect_true(inherits(predict(ssn_mod1, newdata = "pred1km", interval = "confidence", level = 0.9), "matrix"))
+  expect_equal(predict(ssn_mod1, newdata = "pred1km")[1],
+    c("1" = 2.687),
+    tolerance = 0.01
+  )
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", type = "response")[1],
+    c("1" = 14.688),
+    tolerance = 0.01
+  )
+  expect_equal(length(predict(ssn_mod1, newdata = "pred1km")), 175)
+  expect_equal(length(predict(ssn_mod1, newdata = "pred1km", type = "response")), 175)
+  expect_identical(names(predict(ssn_mod1, newdata = "pred1km", se.fit = TRUE)), c("fit", "se.fit"))
+  expect_identical(names(predict(ssn_mod1, newdata = "pred1km", type = "response", se.fit = TRUE)), c("fit", "se.fit"))
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", interval = "prediction", se.fit = TRUE)$fit[1, ],
+    c("fit" = 2.687, "lwr" = 2.670, "upr" = 2.740),
+    tolerance = 0.01
+  )
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", interval = "prediction", type = "response", se.fit = TRUE)$fit[1, ],
+    c("fit" = 14.688, "lwr" = 14.440, "upr" = 14.941),
+    tolerance = 0.01
+  )
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", interval = "confidence")[1, ],
+    c("fit" = 2.635, "lwr" = 2.444, "upr" = 2.827),
+    tolerance = 0.01
+  )
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", type = "response", interval = "confidence")[1, ],
+    c("fit" = 13.952, "lwr" = 11.519, "upr" = 16.898),
+    tolerance = 0.01
+  )
+  expect_error(predict(ssn_mod1, newdata = "pred1km", interval = "error"))
 
   # print
   expect_output(print(ssn_mod1))
@@ -138,37 +224,65 @@ test_that("generics work ssn_glm point data", {
 
   # pseudoR2
   expect_vector(pseudoR2(ssn_mod1))
+  expect_equal(pseudoR2(ssn_mod1), 0.162, tolerance = 0.01)
 
   # residuals
   expect_vector(residuals(ssn_mod1))
+  expect_equal(length(residuals(ssn_mod1)), 45)
   expect_vector(residuals(ssn_mod1, type = "pearson"))
-  expect_vector(residuals(ssn_mod1, type = "standardized"))
+  expect_equal(residuals(ssn_mod1, type = "pearson")[1], c("1" = 0.0318), tolerance = 0.01)
+  expect_equal(residuals(ssn_mod1, type = "standardized")[1], c("1" = 0.0001), tolerance = 0.01)
+  expect_identical(residuals(ssn_mod1, type = "standardized"), rstandard(ssn_mod1))
+  expect_error(residuals(ssn_mod1, type = "error"), "residuals must be deviance or response or pearson or standardized")
+  # resid alias
   expect_vector(resid(ssn_mod1))
+  expect_equal(length(resid(ssn_mod1)), 45)
   expect_vector(resid(ssn_mod1, type = "pearson"))
-  expect_vector(resid(ssn_mod1, type = "standardized"))
-  expect_vector(rstandard(ssn_mod1))
+  expect_equal(resid(ssn_mod1, type = "pearson")[1], c("1" = 0.0318), tolerance = 0.01)
+  expect_equal(resid(ssn_mod1, type = "standardized")[1], c("1" = 0.0001), tolerance = 0.01)
+  expect_identical(resid(ssn_mod1, type = "standardized"), rstandard(ssn_mod1))
+  expect_error(resid(ssn_mod1, type = "error"), "residuals must be deviance or response or pearson or standardized")
 
   # summary
   expect_type(summary(ssn_mod1), "list")
+  expect_equal(length(ssn_mod1), 35)
+  expect_equal(length(summary(ssn_mod1)), 8)
+  expect_identical(ssn_mod1$family, "Gamma")
 
   # terms
   expect_type(terms(ssn_mod1), "language")
+  expect_s3_class(terms(ssn_mod1), "terms")
+  expect_s3_class(terms(ssn_mod1), "formula")
 
   # tidy
   expect_s3_class(tidy(ssn_mod1), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1)), c(2, 5))
   expect_s3_class(tidy(ssn_mod1, conf.int = TRUE, level = 0.9), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, conf.int = TRUE, level = 0.9)), c(2, 7))
   expect_s3_class(tidy(ssn_mod1, effects = "ssn"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "ssn")), c(8, 4))
   expect_s3_class(tidy(ssn_mod1, effects = "tailup"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "tailup")), c(2, 3))
   expect_s3_class(tidy(ssn_mod1, effects = "taildown"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "taildown")), c(2, 3))
   expect_s3_class(tidy(ssn_mod1, effects = "euclid"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "euclid")), c(2, 3))
   expect_s3_class(tidy(ssn_mod1, effects = "nugget"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "nugget")), c(1, 3))
+  expect_s3_class(tidy(ssn_mod1, effects = "dispersion"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "nugget")), c(1, 3))
 
   # update
   expect_s3_class(update(ssn_mod2), "ssn_glm")
 
   # varcomp
   expect_s3_class(varcomp(ssn_mod1), "data.frame")
+  expect_equal(dim(varcomp(ssn_mod1)), c(5, 2))
+  expect_equal(varcomp(ssn_mod1)$proportion, c(0.163, 0.317, 0.520, 3.3e-08, 4.33e-11), tolerance = 0.01)
 
   # vcov
-  expect_true(inherits(vcov(ssn_mod1), "matrix"))
+  expect_equal(dim(vcov(ssn_mod1)), c(2, 2))
+  expect_equal(diag(vcov(ssn_mod1)), c("(Intercept)" = 1.213, "ELEV_DEM" = 2.959e-07), tolerance = 0.01)
+  expect_equal(dim(vcov(ssn_mod1, var_correct = FALSE)), c(2, 2))
+  expect_equal(diag(vcov(ssn_mod1, var_correct = FALSE)), c("(Intercept)" = 1.194, "ELEV_DEM" = 2.911e-07), tolerance = 0.01)
 })
diff --git a/tests/testthat/test-ssn_lm.R b/tests/testthat/test-ssn_lm.R
index 4ae674e..750d6ba 100644
--- a/tests/testthat/test-ssn_lm.R
+++ b/tests/testthat/test-ssn_lm.R
@@ -1,21 +1,13 @@
 test_that("generics work ssn_lm point data", {
-  # Copy the mf04p .ssn data to a local directory and read it into R
-  # When modeling with your .ssn object, you will load it using the relevant
-  # path to the .ssn data on your machine
-  copy_lsn_to_temp()
-  temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-  mf04p <- ssn_import(
-    temp_path,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
+  set.seed(2)
 
-  ssn_mod1 <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
+  form <- Summer_mn ~ ELEV_DEM
+  ssn_mod1 <- ssn_lm(form, mf04p,
     tailup_type = "exponential",
     taildown_type = "exponential", euclid_type = "exponential",
     nugget_type = "nugget", additive = "afvArea"
   )
-  ssn_mod2 <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
+  ssn_mod2 <- ssn_lm(form, mf04p,
     tailup_type = "exponential",
     taildown_type = "none", euclid_type = "none",
     nugget_type = "nugget", additive = "afvArea"
@@ -23,97 +15,164 @@ test_that("generics work ssn_lm point data", {
 
   # AIC
   expect_vector(AIC(ssn_mod1))
-  expect_s3_class(AIC(ssn_mod1, ssn_mod2), "data.frame") # turn reml fixed effects warning off
+  expect_equal(AIC(ssn_mod1), 84.934, tolerance = 0.01)
+  expect_s3_class(AIC(ssn_mod1, ssn_mod2), "data.frame")
+  expect_equal(AIC(ssn_mod1, ssn_mod2)$AIC, c(84.934, 82.893), tolerance = 0.01)
 
   # anova
-  expect_s3_class(anova(ssn_mod1), "data.frame")
-  expect_s3_class(anova(ssn_mod1), "anova.ssn_lm")
-  expect_s3_class(tidy(anova(ssn_mod1)), "data.frame")
-  expect_s3_class(anova(ssn_mod1, ssn_mod2), "data.frame")
-  expect_s3_class(anova(ssn_mod1, ssn_mod2), "anova.ssn_lm")
-  expect_s3_class(tidy(anova(ssn_mod1, ssn_mod2)), "data.frame")
+  anova1 <- anova(ssn_mod1)
+  expect_s3_class(anova1, "data.frame")
+  expect_s3_class(anova1, "anova.ssn_lm")
+  expect_s3_class(tidy(anova1), "data.frame")
+  expect_equal(tidy(anova1)$statistic, c(30.6, 21.1), tolerance = 0.01)
+  anova12 <- anova(ssn_mod1, ssn_mod2)
+  expect_s3_class(anova12, "data.frame")
+  expect_s3_class(anova12, "anova.ssn_lm")
+  expect_s3_class(tidy(anova12), "data.frame")
+  expect_equal(tidy(anova12)$statistic, 5.96, tolerance = 0.01)
 
   # augment
-  expect_s3_class(augment(ssn_mod1), "data.frame")
-  expect_s3_class(augment(ssn_mod1, newdata = "pred1km"), "data.frame")
+  aug_ssn_mod1 <- augment(ssn_mod1)
+  expect_s3_class(aug_ssn_mod1, "sf")
+  expect_true(all(c(".fitted", ".resid", ".hat", ".cooksd", ".std.resid") %in% names(aug_ssn_mod1)))
+  expect_equal(aug_ssn_mod1$.fitted[1], 14.261, tolerance = 0.01)
+  aug_pred_ssn_mod1 <- augment(ssn_mod1, newdata = "pred1km")
+  expect_s3_class(aug_pred_ssn_mod1, "sf")
+  expect_true(all(c(".fitted") %in% names(aug_ssn_mod1)))
+  expect_equal(aug_pred_ssn_mod1$.fitted[1], 14.690, tolerance = 0.01)
 
   # coef
   expect_vector(coef(ssn_mod1))
+  expect_equal(coef(ssn_mod1), c("(Intercept)" = 70.54, "ELEV_DEM" = -0.0289), tolerance = 0.01)
   expect_s3_class(coef(ssn_mod1, type = "tailup"), "tailup_exponential")
+  expect_equal(unclass(coef(ssn_mod1, type = "tailup")), c("de" = 1.387, "range" = 4.0744e06), tolerance = 0.01)
+  expect_s3_class(coef(ssn_mod1, type = "taildown"), "taildown_exponential")
+  expect_equal(unclass(coef(ssn_mod1, type = "taildown")), c("de" = 2.419, "range" = 80694), tolerance = 0.01)
   expect_s3_class(coef(ssn_mod1, type = "euclid"), "euclid_exponential")
+  expect_equal(unclass(coef(ssn_mod1, type = "euclid")), c("de" = 0.445, "range" = 7.97e05, "rotate" = 0, "scale" = 1), tolerance = 0.01)
   expect_type(coef(ssn_mod1, type = "ssn"), "list")
   expect_null(coef(ssn_mod1, type = "randcov"))
+  expect_error(coef(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "fixed", "ssn", "tailup",  "taildown",  "euclid",  "nugget", or "randcov".')
+
+  # coefficients alias
   expect_vector(coefficients(ssn_mod1))
+  expect_equal(coefficients(ssn_mod1), c("(Intercept)" = 70.54, "ELEV_DEM" = -0.0289), tolerance = 0.01)
+  expect_s3_class(coefficients(ssn_mod1, type = "tailup"), "tailup_exponential")
+  expect_equal(unclass(coefficients(ssn_mod1, type = "tailup")), c("de" = 1.387, "range" = 4.0744e06), tolerance = 0.01)
   expect_s3_class(coefficients(ssn_mod1, type = "taildown"), "taildown_exponential")
-  expect_s3_class(coef(ssn_mod1, type = "nugget"), "nugget_nugget")
+  expect_equal(unclass(coefficients(ssn_mod1, type = "taildown")), c("de" = 2.419, "range" = 80694), tolerance = 0.01)
+  expect_s3_class(coefficients(ssn_mod1, type = "euclid"), "euclid_exponential")
+  expect_equal(unclass(coefficients(ssn_mod1, type = "euclid")), c("de" = 0.445, "range" = 7.97e05, "rotate" = 0, "scale" = 1), tolerance = 0.01)
+  expect_type(coefficients(ssn_mod1, type = "ssn"), "list")
   expect_null(coefficients(ssn_mod1, type = "randcov"))
+  expect_error(coefficients(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "fixed", "ssn", "tailup",  "taildown",  "euclid",  "nugget", or "randcov".')
+
 
   # confint
-  expect_true(inherits(confint(ssn_mod1), "matrix"))
-  expect_true(inherits(confint(ssn_mod1, parm = c("x"), level = 0.9), "matrix"))
+  expect_equal(dim(confint(ssn_mod1)), c(2, 2))
+  expect_equal(dim(confint(ssn_mod1, parm = c("ELEV_DEM"), level = 0.9)), c(1, 2))
 
   # cooks.distance
   expect_vector(cooks.distance(ssn_mod1))
+  expect_equal(cooks.distance(ssn_mod1)[1], c("1" = 0.004), tolerance = 0.01)
 
   # covmatrix
-  expect_true(inherits(covmatrix(ssn_mod1), "matrix"))
-  expect_true(inherits(covmatrix(ssn_mod1, "pred1km"), "matrix"))
-  expect_true(inherits(covmatrix(ssn_mod1, "pred1km", type = "obs.pred"), "matrix"))
-  expect_true(inherits(covmatrix(ssn_mod1, "pred1km", cov_type = "pred.pred"), "matrix"))
+  expect_equal(dim(covmatrix(ssn_mod1)), c(45, 45))
+  expect_equal(dim(covmatrix(ssn_mod1, "pred1km")), c(175, 45))
+  expect_equal(dim(covmatrix(ssn_mod1, "pred1km", cov_type = "obs.pred")), c(45, 175))
+  expect_equal(dim(covmatrix(ssn_mod1, "pred1km", cov_type = "pred.pred")), c(175, 175))
+  expect_error(covmatrix(ssn_mod1, "pred1km", cov_type = "error"), 'Invalid "cov_type" argument.')
 
   # deviance
   expect_vector(deviance(ssn_mod1))
+  expect_equal(deviance(ssn_mod1), 41.63, tolerance = 0.01)
 
   # fitted
   expect_vector(fitted(ssn_mod1))
+  expect_equal(fitted(ssn_mod1)[1], c("1" = 14.26), tolerance = 0.01)
   expect_vector(fitted(ssn_mod1, type = "tailup"))
+  expect_equal(fitted(ssn_mod1, type = "tailup")[1], c("1" = -0.130), tolerance = 0.01)
   expect_vector(fitted(ssn_mod1, type = "taildown"))
+  expect_equal(fitted(ssn_mod1, type = "taildown")[1], c("1" = 0.772), tolerance = 0.01)
+  expect_vector(fitted(ssn_mod1, type = "euclid"))
+  expect_equal(fitted(ssn_mod1, type = "euclid")[1], c("1" = 0.003), tolerance = 0.01)
   expect_null(fitted(ssn_mod1, type = "randcov"))
+  expect_error(fitted(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "response", "tailup",  "taildown",  "euclid",  "nugget", or "randcov".')
+
+  # fitted.values alias
   expect_vector(fitted.values(ssn_mod1))
+  expect_equal(fitted.values(ssn_mod1)[1], c("1" = 14.26), tolerance = 0.01)
+  expect_vector(fitted.values(ssn_mod1, type = "tailup"))
+  expect_equal(fitted.values(ssn_mod1, type = "tailup")[1], c("1" = -0.130), tolerance = 0.01)
+  expect_vector(fitted.values(ssn_mod1, type = "taildown"))
+  expect_equal(fitted.values(ssn_mod1, type = "taildown")[1], c("1" = 0.772), tolerance = 0.01)
   expect_vector(fitted.values(ssn_mod1, type = "euclid"))
-  expect_vector(fitted.values(ssn_mod1, type = "nugget"))
+  expect_equal(fitted.values(ssn_mod1, type = "euclid")[1], c("1" = 0.003), tolerance = 0.01)
   expect_null(fitted.values(ssn_mod1, type = "randcov"))
+  expect_error(fitted.values(ssn_mod1, type = "error"), 'Invalid type argument. The type argument must be "response", "tailup",  "taildown",  "euclid",  "nugget", or "randcov".')
 
   # formula
   expect_type(formula(ssn_mod1), "language")
+  expect_equal(formula(ssn_mod1), form)
 
   # getCall
   expect_type(getCall(ssn_mod1), "language")
+  expect_equal(getCall(ssn_mod1), ssn_mod1$call)
 
   # glance
-  expect_s3_class(glance(ssn_mod1), "data.frame")
+  glance_ssn_mod1 <- glance(ssn_mod1)
+  names_glance <- c("n", "p", "npar", "value", "AIC", "AICc", "logLik", "deviance", "pseudo.r.squared")
+  expect_s3_class(glance_ssn_mod1, "data.frame")
+  expect_equal(dim(glance_ssn_mod1), c(1, 9))
+  expect_identical(names_glance, names(glance_ssn_mod1))
 
   # glances
-  expect_s3_class(glances(ssn_mod1), "data.frame")
+  expect_identical(glance_ssn_mod1, glances(ssn_mod1)[, -1])
+  glance_ssn_mod12 <- glances(ssn_mod1, ssn_mod2)
   expect_s3_class(glances(ssn_mod1, ssn_mod2), "data.frame")
+  expect_equal(dim(glance_ssn_mod12), c(2, 10))
+  expect_identical(c("model", names_glance), names(glance_ssn_mod12))
 
   # hatvalues
   expect_vector(hatvalues(ssn_mod1))
+  expect_equal(hatvalues(ssn_mod1)[1], c("1" = 0.068), tolerance = 0.01)
 
   # influence
-  expect_s3_class(influence(ssn_mod1), "data.frame")
+  infl_ssn_mod1 <- influence(ssn_mod1)
+  names_infl <- c(".resid", ".hat", ".cooksd", ".std.resid")
+  expect_s3_class(infl_ssn_mod1, "data.frame")
+  expect_equal(dim(infl_ssn_mod1), c(45, 4))
+  expect_identical(names_infl, names(infl_ssn_mod1))
 
   # labels
   expect_type(labels(ssn_mod1), "character")
+  expect_identical(labels(ssn_mod1), "ELEV_DEM")
 
   # logLik
   expect_vector(logLik(ssn_mod1))
+  expect_equal(logLik(ssn_mod1), -35.46, tolerance = 0.01)
 
   # loocv
-  expect_s3_class(loocv(ssn_mod1), "data.frame")
-  expect_type(loocv(ssn_mod1, cv_predict = TRUE, se.fit = TRUE), "list")
+  loocv_ssn_mod1 <- loocv(ssn_mod1)
+  expect_s3_class(loocv_ssn_mod1, "data.frame")
+  expect_identical(names(loocv_ssn_mod1), c(
+    "bias", "std.bias", "MSPE", "RMSPE",
+    "std.MSPE", "RAV", "cor2", "cover.80", "cover.90", "cover.95"
+  ))
+  expect_equal(loocv_ssn_mod1$MSPE, 0.256, tolerance = 0.01)
+  expect_identical(names(loocv(ssn_mod1, cv_predict = TRUE, se.fit = TRUE)), c("stats", "cv_predict", "se.fit"))
 
   # model.frame
-  expect_s3_class(model.frame(ssn_mod1), "data.frame")
+  expect_equal(dim(model.frame(ssn_mod1)), c(45, 2))
 
   # model.matrix
-  expect_true(inherits(model.matrix(ssn_mod1), "matrix"))
+  expect_equal(dim(model.matrix(ssn_mod1)), c(45, 2))
 
   # model.offset
   expect_null(model.offset(model.frame(ssn_mod1)))
 
   # model.response
-  expect_vector(model.response(model.frame(ssn_mod1)))
+  expect_length(model.response(model.frame(ssn_mod1)), 45)
 
   # plot
   expect_invisible(plot(ssn_mod1, which = 1))
@@ -124,14 +183,43 @@ test_that("generics work ssn_lm point data", {
   expect_invisible(plot(ssn_mod1, which = 6))
 
   # predict
-  expect_vector(predict(ssn_mod1, newdata = "pred1km"))
-  expect_type(predict(ssn_mod1, newdata = "pred1km", se.fit = TRUE), "list")
-  expect_type(predict(ssn_mod1, newdata = "pred1km", interval = "prediction", se.fit = TRUE), "list")
-  expect_true(inherits(predict(ssn_mod1, newdata = "pred1km", interval = "confidence", level = 0.9), "matrix"))
+  expect_equal(predict(ssn_mod1, newdata = "pred1km")[1],
+    c("1" = 14.690),
+    tolerance = 0.01
+  )
+  expect_equal(length(predict(ssn_mod1, newdata = "pred1km")), 175)
+  expect_identical(names(predict(ssn_mod1, newdata = "pred1km", se.fit = TRUE)), c("fit", "se.fit"))
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", interval = "prediction", se.fit = TRUE)$fit[1, ],
+    c("fit" = 14.690, "lwr" = 14.363, "upr" = 15.016),
+    tolerance = 0.01
+  )
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", interval = "confidence")[1, ],
+    c("fit" = 14.058, "lwr" = 11.477, "upr" = 16.639),
+    tolerance = 0.01
+  )
+  expect_error(predict(ssn_mod1, newdata = "pred1km", interval = "error"))
+
+  # block predict
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", block = TRUE)[1],
+    c("1" = 10.295),
+    tolerance = 0.01
+  )
+  expect_equal(length(predict(ssn_mod1, newdata = "pred1km", block = TRUE)), 1)
+  expect_identical(names(predict(ssn_mod1, newdata = "pred1km", block = TRUE, se.fit = TRUE)), c("fit", "se.fit"))
+  expect_equal(
+    predict(ssn_mod1,
+      newdata = "pred1km", block = TRUE,
+      interval = "prediction", se.fit = TRUE
+    )$fit[1, ],
+    c("fit" = 10.295, "lwr" = 9.321, "upr" = 11.269),
+    tolerance = 0.01
+  )
+  expect_equal(predict(ssn_mod1, newdata = "pred1km", block = TRUE, interval = "confidence")[1, ],
+    c("fit" = 10.579, "lwr" = 8.023, "upr" = 13.134),
+    tolerance = 0.01
+  )
   expect_vector(predict(ssn_mod1, newdata = "pred1km", block = TRUE))
-  expect_type(predict(ssn_mod1, newdata = "pred1km", block = TRUE, se.fit = TRUE), "list")
-  expect_true(inherits(predict(ssn_mod1, newdata = "pred1km", block = TRUE, interval = "confidence"), "matrix"))
-  expect_true(inherits(predict(ssn_mod1, newdata = "pred1km", block = TRUE, interval = "prediction"), "matrix"))
+  expect_error(predict(ssn_mod1, newdata = "pred1km", block = TRUE, interval = "error"))
 
 
   # print
@@ -141,38 +229,61 @@ test_that("generics work ssn_lm point data", {
 
   # pseudoR2
   expect_vector(pseudoR2(ssn_mod1))
+  expect_equal(pseudoR2(ssn_mod1), 0.336, tolerance = 0.01)
 
   # residuals
   expect_vector(residuals(ssn_mod1))
+  expect_equal(length(residuals(ssn_mod1)), 45)
   expect_vector(residuals(ssn_mod1, type = "pearson"))
-  expect_vector(residuals(ssn_mod1, type = "standardized"))
+  expect_equal(residuals(ssn_mod1, type = "pearson")[1], c("1" = 0.329), tolerance = 0.01)
+  expect_equal(residuals(ssn_mod1, type = "standardized")[1], c("1" = 0.341), tolerance = 0.01)
+  expect_identical(residuals(ssn_mod1, type = "standardized"), rstandard(ssn_mod1))
+  expect_error(residuals(ssn_mod1, type = "error"), "residuals must be response or pearson or standardized")
+
+  # resid alias
   expect_vector(resid(ssn_mod1))
+  expect_equal(length(resid(ssn_mod1)), 45)
   expect_vector(resid(ssn_mod1, type = "pearson"))
-  expect_vector(resid(ssn_mod1, type = "standardized"))
-  expect_vector(rstandard(ssn_mod1))
+  expect_equal(resid(ssn_mod1, type = "pearson")[1], c("1" = 0.329), tolerance = 0.01)
+  expect_equal(resid(ssn_mod1, type = "standardized")[1], c("1" = 0.341), tolerance = 0.01)
+  expect_identical(resid(ssn_mod1, type = "standardized"), rstandard(ssn_mod1))
+  expect_error(resid(ssn_mod1, type = "error"), "residuals must be response or pearson or standardized")
 
   # summary
   expect_type(summary(ssn_mod1), "list")
+  expect_equal(length(ssn_mod1), 32)
+  expect_equal(length(summary(ssn_mod1)), 8)
 
   # terms
   expect_type(terms(ssn_mod1), "language")
+  expect_s3_class(terms(ssn_mod1), "terms")
+  expect_s3_class(terms(ssn_mod1), "formula")
 
   # tidy
   expect_s3_class(tidy(ssn_mod1), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1)), c(2, 5))
   expect_s3_class(tidy(ssn_mod1, conf.int = TRUE, level = 0.9), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, conf.int = TRUE, level = 0.9)), c(2, 7))
   expect_s3_class(tidy(ssn_mod1, effects = "ssn"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "ssn")), c(7, 4))
   expect_s3_class(tidy(ssn_mod1, effects = "tailup"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "tailup")), c(2, 3))
   expect_s3_class(tidy(ssn_mod1, effects = "taildown"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "taildown")), c(2, 3))
   expect_s3_class(tidy(ssn_mod1, effects = "euclid"), "data.frame")
+  expect_equal(dim(tidy(ssn_mod1, effects = "euclid")), c(2, 3))
   expect_s3_class(tidy(ssn_mod1, effects = "nugget"), "data.frame")
-
+  expect_equal(dim(tidy(ssn_mod1, effects = "nugget")), c(1, 3))
 
   # update
   expect_s3_class(update(ssn_mod2), "ssn_lm")
 
   # varcomp
   expect_s3_class(varcomp(ssn_mod1), "data.frame")
+  expect_equal(dim(varcomp(ssn_mod1)), c(5, 2))
+  expect_equal(varcomp(ssn_mod1)$proportion, c(0.336, 0.216, 0.376, 0.069, 0.002), tolerance = 0.01)
 
   # vcov
-  expect_true(inherits(vcov(ssn_mod1), "matrix"))
+  expect_equal(dim(vcov(ssn_mod1)), c(2, 2))
+  expect_equal(diag(vcov(ssn_mod1)), c("(Intercept)" = 1.626e02, "ELEV_DEM" = 3.957e-05), tolerance = 0.01)
 })
diff --git a/tests/testthat/test-ssn_simulate.R b/tests/testthat/test-ssn_simulate.R
index 141c66b..1d08c35 100644
--- a/tests/testthat/test-ssn_simulate.R
+++ b/tests/testthat/test-ssn_simulate.R
@@ -1,15 +1,4 @@
 test_that("simulating works", {
-  # Copy the mf04p .ssn data to a local directory and read it into R
-  # When modeling with your .ssn object, you will load it using the relevant
-  # path to the .ssn data on your machine
-  copy_lsn_to_temp()
-  temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-  mf04p <- ssn_import(
-    temp_path,
-    predpts = c("pred1km", "CapeHorn", "Knapp"),
-    overwrite = TRUE
-  )
-
   tu <- tailup_params("exponential", de = 1, range = 1)
   td <- taildown_params("exponential", de = 1, range = 1)
   eu <- euclid_params("exponential", de = 1, range = 1, rotate = 0, scale = 1)
@@ -19,7 +8,8 @@ test_that("simulating works", {
 
   # mean seq
   set.seed(0)
-  mean_seq <- rnorm(n = NROW(mf04p$obs), 0, sd = 0.25)
+  n_obs <- NROW(mf04p$obs)
+  mean_seq <- rnorm(n = n_obs, 0, sd = 0.25)
 
   # set netID as factor
   mf04p$obs$netID <- as.factor(mf04p$obs$netID)
@@ -28,83 +18,117 @@ test_that("simulating works", {
   pf <- ~netID
 
   # ssn_rnorm
-  expect_vector(ssn_simulate(
+  set.seed(0)
+  sim1 <- ssn_simulate(
     family = "gaussian", ssn.object = mf04p, network = "obs",
     tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1
-  ))
-  expect_true(inherits(ssn_simulate(
+  )
+
+  expect_equal(length(sim1), n_obs)
+  expect_equal(sim1[1:2], c(2.525, -0.652), tolerance = 0.01)
+
+  sim2 <- ssn_simulate(
     family = Gaussian, ssn.object = mf04p, network = "obs",
     tu, td, eu2, nug, additive = "afvArea", mean = mean_seq, samples = 2,
     randcov_params = rand, partition_factor = pf
-  ), "matrix"))
+  )
+
+  expect_equal(dim(sim2), c(n_obs, 2))
+  expect_equal(sim2[1, ], c(-2.066, 3.221), tolerance = 0.01)
+
 
   # ssn_rpois
-  expect_vector(ssn_simulate(
+  set.seed(0)
+  sim1 <- ssn_simulate(
     family = "poisson", ssn.object = mf04p, network = "obs",
     tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1
-  ))
-  expect_true(inherits(ssn_simulate(
+  )
+
+  expect_equal(length(sim1), n_obs)
+  expect_equal(sim1[1:2], c(19, 1), tolerance = 0.01)
+
+  sim2 <- ssn_simulate(
     family = poisson, ssn.object = mf04p, network = "obs",
     tu, td, eu2, nug, additive = "afvArea", mean = mean_seq, samples = 2,
     randcov_params = rand, partition_factor = pf
-  ), "matrix"))
+  )
 
-  # ssn_rnbinom
-  expect_vector(ssn_simulate(
-    family = "nbinomial", ssn.object = mf04p, network = "obs",
-    tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1,
-    dispersion = 1
-  ))
-  expect_true(inherits(ssn_simulate(
-    family = nbinomial, ssn.object = mf04p, network = "obs",
-    tu, td, eu2, nug, additive = "afvArea", mean = mean_seq, samples = 2,
-    dispersion = 1, randcov_params = rand, partition_factor = pf
-  ), "matrix"))
+  expect_equal(dim(sim2), c(n_obs, 2))
+  expect_equal(sim2[1, ], c("1" = 3, "2" = 6), tolerance = 0.01)
 
-  # ssn_rbinom
-  expect_vector(ssn_simulate(
+  # ssn_rnbinom
+  set.seed(0)
+  sim1 <- ssn_simulate(
     family = "binomial", ssn.object = mf04p, network = "obs",
     tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1
-  ))
-  expect_true(inherits(ssn_simulate(
+  )
+
+  expect_equal(length(sim1), n_obs)
+  expect_equal(sim1[1:2], c(1, 0), tolerance = 0.01)
+
+  sim2 <- ssn_simulate(
     family = binomial, ssn.object = mf04p, network = "obs",
     tu, td, eu2, nug, additive = "afvArea", mean = mean_seq, samples = 2,
     randcov_params = rand, partition_factor = pf
-  ), "matrix"))
+  )
+
+  expect_equal(dim(sim2), c(n_obs, 2))
+  expect_equal(sim2[1, ], c("1" = 1, "2" = 1), tolerance = 0.01)
 
   # ssn_rbeta
-  expect_vector(ssn_simulate(
+  set.seed(0)
+  sim1 <- ssn_simulate(
     family = "beta", ssn.object = mf04p, network = "obs",
-    tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1,
-    dispersion = 1
-  ))
-  expect_true(inherits(ssn_simulate(
+    tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1
+  )
+
+  expect_equal(length(sim1), n_obs)
+  expect_equal(sim1[1:2], c(0.9997, 0.7529), tolerance = 0.01)
+
+  sim2 <- ssn_simulate(
     family = beta, ssn.object = mf04p, network = "obs",
     tu, td, eu2, nug, additive = "afvArea", mean = mean_seq, samples = 2,
-    dispersion = 1, randcov_params = rand, partition_factor = pf
-  ), "matrix"))
+    randcov_params = rand, partition_factor = pf
+  )
+
+  expect_equal(dim(sim2), c(n_obs, 2))
+  expect_equal(sim2[1, ], c("1" = 0.0001, "2" = 0.0141), tolerance = 0.01)
 
   # ssn_rgamma
-  expect_vector(ssn_simulate(
+  set.seed(0)
+  sim1 <- ssn_simulate(
     family = "Gamma", ssn.object = mf04p, network = "obs",
-    tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1,
-    dispersion = 1
-  ))
-  expect_true(inherits(ssn_simulate(
+    tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1
+  )
+
+  expect_equal(length(sim1), n_obs)
+  expect_equal(sim1[1:2], c(47.063, 0.427), tolerance = 0.01)
+
+  sim2 <- ssn_simulate(
     family = Gamma, ssn.object = mf04p, network = "obs",
     tu, td, eu2, nug, additive = "afvArea", mean = mean_seq, samples = 2,
-    dispersion = 1, randcov_params = rand, partition_factor = pf
-  ), "matrix"))
+    randcov_params = rand, partition_factor = pf
+  )
+
+  expect_equal(dim(sim2), c(n_obs, 2))
+  expect_equal(sim2[1, ], c("1" = 0.275, "2" = 0.087), tolerance = 0.01)
 
   # ssn_rinvgauss
-  expect_vector(ssn_simulate(
+  set.seed(0)
+  sim1 <- ssn_simulate(
     family = "inverse.gaussian", ssn.object = mf04p, network = "obs",
-    tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1,
-    dispersion = 1
-  ))
-  expect_true(inherits(ssn_simulate(
+    tu, td, eu, nug, additive = afvArea, mean = 0, samples = 1
+  )
+
+  expect_equal(length(sim1), n_obs)
+  expect_equal(sim1[1:2], c(34.695, 0.123), tolerance = 0.01)
+
+  sim2 <- ssn_simulate(
     family = inverse.gaussian, ssn.object = mf04p, network = "obs",
     tu, td, eu2, nug, additive = "afvArea", mean = mean_seq, samples = 2,
-    dispersion = 1, randcov_params = rand, partition_factor = pf
-  ), "matrix"))
+    randcov_params = rand, partition_factor = pf
+  )
+
+  expect_equal(dim(sim2), c(n_obs, 2))
+  expect_equal(sim2[1, ], c("1" = 7.874, "2" = 0.465), tolerance = 0.01)
 })
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
index 51921e0..8ca3454 100644
--- a/tests/testthat/test-utils.R
+++ b/tests/testthat/test-utils.R
@@ -1,27 +1,4 @@
-# Copy the mf04p .ssn data to a local directory and read it into R
-# When modeling with your .ssn object, you will load it using the relevant
-# path to the .ssn data on your machine
-copy_lsn_to_temp()
-temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
-mf04p <- ssn_import(
-  temp_path,
-  predpts = c("pred1km", "CapeHorn", "Knapp"),
-  overwrite = TRUE
-)
-
-# fit an example model
-ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p, tailup_type = "exponential", additive = "afvArea")
-
-initial_object_val <- get_initial_object(
-  tailup_type = "exponential",
-  taildown_type = "exponential",
-  euclid_type = "exponential",
-  nugget_type = "nugget",
-  tailup_initial = NULL,
-  taildown_initial = NULL,
-  euclid_initial = NULL,
-  nugget_initial = NULL
-)
+# test some utility functions
 
 ################################################################################
 ############################ check_optim_method
@@ -43,45 +20,9 @@ test_that("check optim method works", {
   expect_equal(optim_dotlist_val$upper, 50)
 })
 
-################################################################################
-############################ params, cov_matrix, cov_vector work
-################################################################################
-
-test_that("params, cov_matrix, cov_vector work", {
-  tailup_par <- tailup_params("exponential", 1, 1)
-  taildown_par <- taildown_params("exponential", 1, 1)
-  euclid_par <- euclid_params("exponential", 1, 1, 0, 1)
-  nugget_par <- nugget_params("nugget", 0.1)
-
-  # create dist object
-  dist_object <- get_dist_object(
-    ssn.object = mf04p,
-    initial_object = initial_object_val,
-    additive = "afvArea",
-    anisotropy = FALSE
-  )
-
-  n_obs <- NROW(mf04p$obs)
-  n_obs_dim <- c(n_obs, n_obs)
-
-  expect_equal(dim(cov_matrix(tailup_par, dist_object)), n_obs_dim)
-  expect_equal(dim(cov_matrix(taildown_par, dist_object)), n_obs_dim)
-  expect_equal(dim(cov_matrix(euclid_par, dist_object, anisotropy = FALSE)), n_obs_dim)
-  expect_equal(dim(cov_matrix(nugget_par, dist_object, de_scale = 0)), n_obs_dim)
-
-
-  # create distance object
-  dist_pred_object <- get_dist_pred_object(
-    object = ssn_mod,
-    newdata_name = "pred1km",
-    initial_object = initial_object_val
-  )
-
-  n_obs <- NROW(ssn_mod$ssn.object$obs)
-  n_pred <- NROW(ssn_mod$ssn.object$preds[["pred1km"]])
-  n_dim <- c(n_pred, n_obs)
-
-  expect_equal(dim(cov_vector(tailup_par, dist_pred_object)), n_dim)
-  expect_equal(dim(cov_vector(taildown_par, dist_pred_object)), n_dim)
-  expect_equal(dim(cov_vector(euclid_par, dist_pred_object, anisotropy = FALSE)), n_dim)
+test_that("logit and expit work", {
+  expect_equal(logit(1 / 2), 0)
+  expect_equal(expit(0), 1 / 2)
+  expect_equal(expit(logit(4 / 7)), 4 / 7)
+  expect_equal(logit(expit(4 / 7)), 4 / 7)
 })
diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index c4c1446..b96f996 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -40,7 +40,7 @@ water flow, and differences in flow volume throughout the network
 [@peterson2013modelling]. In addition, stream networks are embedded
 within a spatial environment, which can also influence
 observations on the stream network. Traditional spatial
-statistical models, which are based solely on Eucliean distance, often fail to
+statistical models, which are based solely on Euclidean distance, often fail to
 adequately describe these unique and complex spatial dependencies. 
 
 Spatial stream network models are based on a moving-average
@@ -61,11 +61,11 @@ to account for differences in flow volume or other influential variables [@peter
 result, non-zero covariances are restricted to flow-connected sites in a
 tail-up model. In a tail-down model, the moving average function
 points in the downstream direction. In contrast to the tail-up model,
-tail-down models describe both flow-connected and flow-unconnected
-relationships, although covariances will always be equal or stronger
+tail-down models allow for both flow-connected and flow-unconnected
+autocorrelation, autocorrelation will always be equal or stronger
 for flow-unconnected sites than flow-connected sites separated by
 equal stream distances [@ver2010moving]. In the tail-down model, covariance is
-a function of stream distance and weights are not required.
+a function of stream distance and weights are not required. 
 However, it is also possible and often preferable to build
 spatial stream network models based on a mixture of four components: a
 tail-up component, a tail-down component, a Euclidean component, and a
@@ -79,13 +79,13 @@ of spatial stream network models and their covariance components, see
 @cressie2006spatial, @ver2006spatial, @ver2010moving,
 @peterson2010mixed, and @isaak2014applications.
 
-The `SSN2` **R** package is used to fit and summarize spatial stream network models and make predictions at unobserved locations (Kriging). `SSN2` is an updated version of the `SSN` **R** package [@ver2014ssn].  Why did we create `SSN2` to replace `SSN`? There are two main reasons:
+The `SSN2` **R** package is used to fit and summarize spatial stream network models and make predictions at unobserved locations (Kriging). `SSN2` is an updated version of the `SSN` **R** package [@ver2014ssn].  Why did we create `SSN2` to replace the `SSN` **R** package? There are two main reasons:
 
-1.  `SSN` depends on the `rgdal` [@bivand2021rgdal], `rgeos` [@bivand2020rgeos], and `maptools` [@bivand2021maptools] **R** packages, which are being retired in October, 2023. Their functionality has been replaced and modernized by the `sf` package [@pebesma2018sf]. `SSN2` depends on `sf` instead of `rgdal`, `rgeos`, and `maptools`, reflecting this broader change regarding handling spatial data in **R**.
+1.  The `SSN` **R** package depends on the `rgdal` [@bivand2021rgdal], `rgeos` [@bivand2020rgeos], and `maptools` [@bivand2021maptools] **R** packages, which were retired in October, 2023. Their functionality has been replaced and modernized by the `sf` package [@pebesma2018sf]. `SSN2` depends on `sf` instead of `rgdal`, `rgeos`, and `maptools`, reflecting this broader change regarding handling spatial data in **R**.
     
-    * See @nowosad2023 for more information regarding the retirement of `rgdal`, `rgeos`, and `maptools`, available at https://geocompx.org//post/2023/rgdal-retirement .
+    * See @nowosad2023 for more information regarding the retirement of `rgdal`, `rgeos`, and `maptools`, available at [this link](https://geocompx.org//post/2023/rgdal-retirement).
 
-2.  There are features we added to `SSN2` that would have been difficult to implement in `SSN` without a massive restructuring of `SSN`'s foundation, so we created a new package. For example, the `SSN` objects in `SSN2` are S3 objects but the `SSN` objects in `SSN` were S4 objects. Additionally, many functions were rewritten and/or repurposed in `SSN2` to use generic functions (e.g., block prediction in `SSN2` is performed using `predict()` while in `SSN` it was performed using `BlockPredict()`).
+2.  There are features added to `SSN2` that would have been difficult to implement in the `SSN` **R** package without a massive restructuring of its foundation, so a new package was created. For example, the `SSN` objects in `SSN2` are S3 objects but the `SSN` objects in the `SSN` **R** package were S4 objects. Additionally, many functions were rewritten and/or repurposed in `SSN2` to use generic functions (e.g., block prediction in `SSN2` is performed using `predict()` while in the `SSN` **R** package it was performed using `BlockPredict()`). Moreover, `SSN2` leverages many of the tools in the `spmodel` **R** package for spatial statistical modeling [@dumelle2023spmodel].
 
 This vignette provides an overview of basic features in `SSN2`. We load `SSN2` by running
 ```{r, eval = FALSE}
@@ -99,23 +99,12 @@ citation(package = "SSN2")
 
 # Input Data
 
-Spatial input data must be pre-processed before it can be used to fit spatial stream
-network models in the `SSN2` package. This information is
-generated using the Spatial Tools for the Analysis of River Systems
-(STARS) toolset for ArcGIS Desktop versions 9.3x-10.8x
-[@peterson2014stars]. Note that `STARS` is designed to work with
-existing streams data in vector format. The `openSTARS` R package
-[@kattwinkel2020openSTARS] provides an open source alternative to the
-STARS toolset, which depends on GRASS GIS. In contrast to `STARS`, `openSTARS` is
-designed to derive streams in raster format from a digital elevation
-model (DEM). Both tools output a non-proprietary `.ssn` folder (i.e., directory) which contains all
-of the spatial, topological and attribute information needed to fit a
-spatial stream network model using `SSN2`. This includes:
-
-1. edges: a shapefile of lines representing the linear geometry of the
+The streams, observation, and prediction datasets must be pre-processed prior to fitting SSN models and making predictions at unobserved locations using `SSN2`. Previously, the STARS toolset for ArcGIS Desktop versions 9.3x - 10.8x [@peterson2014stars] or the `openSTARS` **R** package [@kattwinkel2020preparing] were used to generate spatial information required for model fitting and prediction. However, both software packages have recently been retired and are replaced by the `SSNbler` **R** package [@peterson2024SSNbler], which is a new, **R**-based version of the STARS tools. `SSNbler` is currently available on GitHub ([link here](https://github.com/pet221/SSNbler/)), will soon be available on CRAN, and contains several useful resources that guide users through these pre-processing steps. Pre-processing using either `SSNbler`, STARS, or `openSTARS` ends with the creatino of a `.ssn` folder, which is non-proprietary. Files residing in the `.ssn` folder are read into R using `ssn_import()` from `SSN2` and palced into a list structure called an SSN object, which contains all the spatial, topological, and attribute information needed to leverage the modeling tools in `SSN2`. This includes:
+
+1. edges: a geopackage of lines representing the linear geometry of the
    stream network(s).
-2. sites: a shapefile of site locations where observed data were collected on the stream network.
-3. prediction sites: one or more shapefiles of locations where
+2. sites: a geopackage of site locations where observed data were collected on the stream network.
+3. prediction sites: one or more geopackages of locations where
    predictions will be made. Optional. 
 4. netIDx.dat for each stream network: a text file containing topological relationships for
    the line segments in edges, by network. 
@@ -125,7 +114,7 @@ spatial stream network model using `SSN2`. This includes:
 The data contained in the `.ssn` object are read into R and stored as
 an `SSN` object, which has a special list structure with four elements:
 
-1.  `edges`: An `sf` object that contains the edges with `LINESTRING` geometry.
+1.  `edges`: An `sf` object that contains the edges with `LINESTRING` geometry (representing the linear geometry of the stream network/s). The endpoints of the edges are called nodes.
 2.  `obs`: An `sf` object that contains the observed data with `POINT` geometry.
 3.  `preds` A list of `sf` objects with `POINT` geometry, each containing a set of locations
     where predictions will be made.
@@ -146,8 +135,8 @@ and for sites
 `"SNETWORK (netID rid upDist ratio pid locID)"`,
 
 **The data used to define the `netgeom` column are found in the edges,**
-**observed sites, and prediction sites shapefiles, which are created**
-**using the `STARS` or `openSTARS` software.**
+**observed sites, and prediction sites geopackages, which are created**
+**using the `SSNbler` (recommended), `STARS`, or `openSTARS` software.**
 For edges, this includes a
 unique network identifier (`netID`) and reach (i.e., edge) identifier
 (`rid`), as well as the distance between the most downstream location on
@@ -189,14 +178,14 @@ path <- system.file("lsndata/MiddleFork04.ssn", package = "SSN2")
 Several functions in `SSN2` for reading and writing data (which we use shortly) directly manipulate the `.ssn` folder. If it is not desirable to directly manipulate the `MiddleFork04.ssn` data installed alongside `SSN2`, `MiddleFork04.ssn` may be copied it into a temporary directory and the relevant path to this alternative location can be stored:
 ```{r}
 copy_lsn_to_temp()
-path <- paste0(tempdir(), "/MiddleFork04.ssn")
+path <- file.path(tempdir(), "MiddleFork04.ssn")
 ```
 
 After specifying `path` (using `system.file()` or `copy_lsn_to_temp()`), we import the stream reaches, observed sites, and prediction sites:
 ```{r}
 mf04p <- ssn_import(
   path = path,
-  predpts = c("pred1km", "CapeHorn", "Knapp"),
+  predpts = c("pred1km", "CapeHorn"),
   overwrite = TRUE
 )
 ```
@@ -207,7 +196,7 @@ summary(mf04p)
 ```
 
 We see that `mf04p` contains 45 observation sites and a
-total of 2102 prediction sites stored in three different prediction
+total of 829 prediction sites stored in two different prediction
 datasets. We will explore several of these variables throughout the rest of the vignette:
 
 * `AREAWTMAP`: Precipitation (area-weighted in mm)
@@ -246,7 +235,7 @@ Later we will fit models to stream network data. Before doing this, however, we
 ```{r}
 ssn_create_distmat(
   ssn.object = mf04p,
-  predpts = c("pred1km", "CapeHorn", "Knapp"),
+  predpts = c("pred1km", "CapeHorn"),
   among_predpts = TRUE,
   overwrite = TRUE
 )
@@ -273,7 +262,7 @@ Linear spatial stream network models for a quantitative response vector $\mathbf
 \mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon},
 \end{equation*}
 ```
-where $\mathbf{X}$ is a matrix of explanatory variables (usually including a column of 1's for an intercept), $\boldsymbol{\beta}$ is a vector of fixed effects that describe the average impact of $\mathbf{X}$ on $\mathbf{y}$, $\boldsymbol{\tau}_{tu}$ is a vector of spatially dependent (correlated) tail-up random errors, $\boldsymbol{\tau}_{td}$ is a vector of spatially dependent (correlated) tail-down random errors, $\boldsymbol{\tau}_{eu}$ is a vector of spatially dependent (correlated) Euclidean random errors, and $\boldsymbol{\epsilon}$ is a vector of spatially independent (uncorrelated) random errors. The spatial dependence of each $\boldsymbol{\tau}$ term is explicitly specified using a spatial covariance function that incorporates the variance of the respective $\boldsymbol{\tau}$ term, often called a partial sill, and a range parameter that controls the behavior of the respective spatial covariance. The variance of $\boldsymbol{\epsilon}$ is often called the nugget (or nugget effect).
+where $\mathbf{X}$ is a matrix of explanatory variables (usually including a column of 1's for an intercept), $\boldsymbol{\beta}$ is a vector of fixed effects that describe the average impact of $\mathbf{X}$ on $\mathbf{y}$, $\boldsymbol{\tau}_{tu}$ is a vector of spatially dependent (autocorrelated) tail-up random errors, $\boldsymbol{\tau}_{td}$ is a vector of spatially dependent (autocorrelated) tail-down random errors, $\boldsymbol{\tau}_{eu}$ is a vector of spatially dependent (autocorrelated) Euclidean random errors, and $\boldsymbol{\epsilon}$ is a vector of spatially independent (uncorrelated) random errors. The spatial dependence of each $\boldsymbol{\tau}$ term is explicitly specified using a spatial covariance function that incorporates the variance of the respective $\boldsymbol{\tau}$ term, often called a partial sill, and a range parameter that controls the behavior of the respective spatial covariance. The variance of $\boldsymbol{\epsilon}$ is often called the nugget (or nugget effect). Sometimes several unconnected networks are modeled simultaneously. For two observations on separate networks, tail-up and tail-down random errors are uncorrelated but Euclidean random errors can be autocorrelated. 
 
 Suppose we are interested in studying summer mean temperature (`Summer_mn`) on the stream network. We can visualize the distribution of summer mean temperature (overlain onto the stream network) by running
 
@@ -281,7 +270,7 @@ Suppose we are interested in studying summer mean temperature (`Summer_mn`) on t
 ggplot() +
   geom_sf(data = mf04p$edges) +
   geom_sf(data = mf04p$obs, aes(color = Summer_mn), size = 2) +
-  scale_color_viridis_c(limits = c(-1.5, 17), option = "H") +
+  scale_color_viridis_c(limits = c(0, 17), option = "H") +
   theme_bw()
 ```
 
@@ -314,7 +303,7 @@ The first argument to `Torgegram()` is `formula`. Residuals from a non-spatial l
 plot(tg)
 ```
 
-The flow-connected semivariances seem to generally increase with distance, which suggests that the model will benefit from at least a tail-up component. The takeaway for flow-unconnected and Euclidean semivariances is less clear -- they seem to generally increase with distance but there are some low distances with high semivariances. This suggests that tail-down and Euclidean components may not be too impactful on the model fit. We investigate this next while we fit a model with all three components: tail-up, tail-down, and Euclidean.
+The flow-connected semivariances seem to generally increase with distance, which suggests that the model will benefit from at least a tail-up component. The takeaway for flow-unconnected and Euclidean semivariances is less clear -- they seem to generally increase with distance but there are some short distances with high semivariances. We more empirically investigate the impact of each dependence structure next while we fit a model with all three components: tail-up, tail-down, and Euclidean.
 
 We fit a spatial stream network model regressing summer mean stream temperature on elevation and watershed-averaged precipitation using an exponential tail-up covariance function with additive weights created using watershed area (`afvArea`), a spherical tail-down covariance function, a Gaussian Euclidean covariance function, and a nugget effect by running
 
@@ -338,7 +327,7 @@ We summarize the fitted model by running
 summary(ssn_mod)
 ```
 
-Similar to summaries of `lm()` objects, summaries of `ssn_lm()` objects include the original function call, residuals, and a coefficients table of fixed effects. The `(Intercept)` represents the average summer mean stream temperature at sea level (an elevation of zero) and no precipitation, `ELEV_DEM` represents the decrease in average summer mean stream temperature with a one unit (meter) increase in elevation, and `AREAWTMAP` represents the decrease in average summer mean stream temperature with a one unit (mm) increase in precipitation. There is strong evidence that average summer mean stream temperature decreases with elevation ($p$-value $< 0.001$), while there is moderate evidence that average summer mean stream temperature decreases with precipitation ($p$-value $\approx$ 0.05). A pseudo r-squared is also returned, which quantifies the proportion of variability explained by the fixed effects. The coefficients table of covariance parameters describes the model's dependence. The larger the `de` parameter, the more variability in the process is attributed to the relevant effect. Here, most of the model's random variability comes from the tail-up portion of the model. The larger the `range` parameter, the more correlated nearby observations are with respect to the relevant effect.
+Similar to summaries of `lm()` objects, summaries of `ssn_lm()` objects include the original function call, residuals, and a coefficients table of fixed effects. The `(Intercept)` represents the average summer mean stream temperature at sea level (an elevation of zero) and no precipitation, `ELEV_DEM` represents the decrease in average summer mean stream temperature with a one unit (meter) increase in elevation, and `AREAWTMAP` represents the decrease in average summer mean stream temperature with a one unit (mm) increase in precipitation. There is strong evidence that average summer mean stream temperature decreases with elevation ($p$-value $< 0.001$), while there is moderate evidence that average summer mean stream temperature decreases with precipitation ($p$-value $\approx$ 0.05). A pseudo r-squared is also returned, which quantifies the proportion of variability explained by the fixed effects. The coefficients table of covariance parameters describes the model's dependence. The larger the `de` parameter, the more variability in the process is attributed to the relevant effect. Here, most of the model's random variability comes from the tail-up and tail-down portions of the model. The larger the `range` parameter, the more autocorrelated nearby observations are with respect to the relevant effect.
 
 We directly compare the sources of variability in the model using the `varcomp` function:
 ```{r}
@@ -346,7 +335,7 @@ varcomp(ssn_mod)
 ```
 
 Most of the variability in summer mean stream temperature is
-explained by the fixed effects of elevation and precipitation (`Covariates (PR-sq)`) as well as the tail-up component.  Note that the values in the `proportion` column sum to one.
+explained by the fixed effects of elevation and precipitation (`Covariates (PR-sq)`), the tail-up component, and the tail-down component.  Note that the values in the `proportion` column sum to one.
 
 In the remainder of this subsection, we describe the broom [@robinson2021broom] functions `tidy()`, `glance()` and `augment()`. `tidy()` tidies coefficient output in a convenient `tibble`, `glance()` glances at model-fit statistics, and `augment()` augments the data with fitted model diagnostics.
 
@@ -416,7 +405,7 @@ loocv_mod2 <- loocv(ssn_mod2)
 loocv_mod2$RMSPE
 ```
 
-`ssn_mod` is the better model with respect to `AIC`, `AICc`, and `RMSPE` and shortly we use it to return model diagnostics and make predictions. `loocv()` predictions using `ssn_mod` are typically within `r round(loocv_mod$RMSPE, digits = 3)`. of the true summer mean stream temperature. Note that model comparison using `loocv()` does not depend on the estimation method (ML vs REML).
+`ssn_mod` is the better model with respect to `AIC`, `AICc`, and `RMSPE` and shortly we use it to return model diagnostics and make predictions. Note that model comparison using `loocv()` does not depend on the estimation method (ML vs REML).
 
 We augment the data with model diagnostics by running
 ```{r}
@@ -428,7 +417,7 @@ The columns of this tibble represent:
 
 -   `Summer_mn`: Summer mean stream temperature.
 -   `ELEV_DEM`: Elevation.
--   `Precipitation`: Precipitation.
+-   `AREAWTMAP`: Precipitation.
 -   `.fitted`: The fitted values (the estimated mean given the explanatory variable values).
 -   `.resid`: The residuals (the response minus the fitted values).
 -   `.hat`: The leverage (hat) values.
@@ -437,10 +426,10 @@ The columns of this tibble represent:
 -   `pid`: The `pid` value.
 -   `geometry`: The spatial information in the `sf` object.
 
-By default, `augment()` only returns the variables in the data used by the model. All variables from the original data are returned by setting `drop = FALSE`. We can write the augmented data to a shapefile by loading `sf` (which comes installed alongside `SSN2`) and running
+By default, `augment()` only returns the variables in the data used by the model. All variables from the original data are returned by setting `drop = FALSE`. We can write the augmented data to a geopackage by loading `sf` (which comes installed alongside `SSN2`) and running
 ```{r, results = "hide"}
 library(sf)
-st_write(aug_ssn_mod, paste0(tempdir(), "/aug_ssn_mod.shp"))
+st_write(aug_ssn_mod, paste0(tempdir(), "/aug_ssn_mod.gpkg"))
 ```
 
 Many of the model diagnostics returned by `augment()` can be visualized by running using `plot()`. For example, we plot the fitted values against the standardized residuals by running
@@ -471,11 +460,11 @@ We visualize these predictions (overlain onto the stream network) by running
 ggplot() +
   geom_sf(data = mf04p$edges) +
   geom_sf(data = aug_preds, aes(color = .fitted), size = 2) +
-  scale_color_viridis_c(limits = c(-1.5, 17), option = "H") +
+  scale_color_viridis_c(limits = c(0, 17), option = "H") +
   theme_bw()
 ```
 
-Previously we wrote out model diagnostics to a shapefile. Now we write out predictions to a geopackage (recall `sf` must be loaded) by running
+Previously we wrote out model diagnostics to a geopackage. Now we write out predictions to a geopackage (recall `sf` must be loaded) by running
 ```{r, results = "hide"}
 st_write(aug_preds, paste0(tempdir(), "/aug_preds.gpkg"))
 ```
@@ -488,7 +477,7 @@ predict(ssn_mod)
 predict(ssn_mod, newdata = "all")
 ```
 
-makes predictions for `pred1km`, `Knapp`, and `CapeHorn` (the names of `mf04p$preds`). Lastly, if there are observations (in the `obs` object) whose response is missing (`NA`), these observations are removed from model fitting and moved to a prediction data set named `.missing`. Then predictions can be obtained at these locations.
+makes predictions for `pred1km` and `CapeHorn` (the names of `mf04p$preds`). Lastly, if there are observations (in the `obs` object) whose response is missing (`NA`), these observations are removed from model fitting and moved to a prediction data set named `.missing`. Then predictions can be obtained at these locations.
 
 We can also predict the average value in a region using block Prediction (instead of making point predictions). We predict the average summer mean temperature throughout the Middle Fork stream network by running
 ```{r}
@@ -500,13 +489,13 @@ predict(ssn_mod, newdata = "pred1km", block = TRUE, interval = "prediction")
 
 There are several additional modeling tools available in `SSN2` that we discuss next: Fixing parameter values; non-spatial random effects; and partition factors. 
 
-Perhaps we want to assume a particular covariance parameter is known. This may be reasonable if information is known about the process or the desire is to perform model selection for nested models or create profile likelihood confidence intervals. Fixing covariance parameters in `SSN2` is accomplished via the `tailup_initial`, `taildown_initial`, `euclid_initial`, and `nugget_initial` arguments to `ssn_lm()`. These arguments are passed an appropriate initial value object created using the `tailup_initial()`, `taildown_initial()`, `euclid_initial()`, or `nugget_initial()` function, respectively. For example, suppose we want to fix the Euclidean dependent error variance parameter at 1, forcing this component to have a moderate effect on the covariance. First, we specify the appropriate object by running
+Perhaps we want to assume a particular covariance parameter is known. This may be reasonable if information is known about the process or the desire is to perform model selection for nested models or create profile likelihood confidence intervals. Fixing covariance parameters in `SSN2` is accomplished via the `tailup_initial`, `taildown_initial`, `euclid_initial`, and `nugget_initial` arguments to `ssn_lm()`. These arguments are passed an appropriate initial value object created using the `tailup_initial()`, `taildown_initial()`, `euclid_initial()`, or `nugget_initial()` function, respectively. For example, suppose we want to fix the Euclidean dependent error variance parameter (i.e., Euclidean partial sill, or parsill) at 1, forcing this component to have a moderate effect on the covariance. First, we specify the appropriate object by running
 ```{r}
 euclid_init <- euclid_initial("gaussian", de = 1, known = "de")
 euclid_init
 ```
 
-The `euclid_init` output shows that the `range` parameter has an initial value of 1 that is assumed known. The `range` parameter will still be estimated. Next the model is fit:
+The `euclid_init` output shows that the `de` parameter has an initial value of 1 that is assumed known. The `range` parameter will still be estimated. Next the model is fit:
 
 ```{r}
 ssn_init <- ssn_lm(
@@ -520,8 +509,6 @@ ssn_init <- ssn_lm(
 ssn_init
 ```
 
-Notice the Euclidean variance is 1.
-
 Random effects can be added to an SSN model to incorporate additional sources of variability separate from those on the stream network. Common additional sources of variability modeled include repeated observations at sites or network-specific effects. The random effects are modeled using similar syntax as for random effects in the `nlme` [@pinheiro2006mixed] and `lme4` [@bates2015lme4] **R** packages, being specified via a formula passed to the `random` argument. We model random intercepts for each of the two networks in the data by running
 ```{r}
 ssn_rand <- ssn_lm(
@@ -538,7 +525,9 @@ ssn_rand
 
 `random = ~ as.factor(netID)` is short-hand for `random = ~ (1 | as.factor(netID))`, which is the more familiar `lme4` or `nlme` syntax.
 
-A partition factor is a variable that allows observations to be uncorrelated when they from different levels of the partition factors. For example, one may want to partition the model into two networks despite their adjacency because of a significant land mass or similar obstruction. Partition factors are modeled using a formula that contains a single variable passed to the `partition_factor` argument. We model the two networks as uncorrelated by running
+A partition factor is a variable that allows observations to be uncorrelated when they do not share the same value. For example, one may want to partition the model into two networks (despite their adjacency) because of a significant land mass (or similar obstruction). In this case, it may be relevant to assume observations upstream of the land mass are not correlated with observations downstream of the land mass. Incorporating an effect as a partition factor is different from incorporating the effect as fixed (i.e., on the right-hand side of the `formula` argument). Partition factors affect the covariance portion of the model while fixed effects affect the mean portion. 
+
+Partition factors are modeled using a formula that contains a single variable passed to the `partition_factor` argument:
 ```{r}
 ssn_part <- ssn_lm(
   formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
@@ -552,7 +541,9 @@ ssn_part <- ssn_lm(
 ssn_part
 ```
 
-In short, the partition factor enables model fitting that builds independence in places not typical on a stream network but deemed relevant by the researcher.
+Here, the partition factor ensures that observations from separate stream networks are completely uncorrelated.
+
+<!-- resulting from Euclidean distance (these observations already do not share any tail-up or tail-down covariance because they are from separate stream networks). -->
 
 ## Generalized Linear SSN Models {#sec:glm}
 
@@ -563,9 +554,9 @@ Generalized linear spatial stream network models for a response vector $\mathbf{
 g(\boldsymbol{\mu}) = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon},
 \end{equation*}
 ```
-where $\boldsymbol{\mu}$ is the mean of $\mathbf{y}$, $g(\cdot)$ is a link function that "links" $\mathbf{\mu}$ to a linear function of the predictor variables and random errors, and all other terms are the same as those defined for linear spatial stream network models. Rather than assuming $y$ is normally (Gaussian) distributed as is often the case with linear spatial stream network models, generalized linear spatial stream network models assume $\mathbf{y}$ follows one of many distributions and has a corresponding link function. Below we summarize the families of generalized linear spatial stream network models supported by `SSN2` their link functions, and the type of data typically associated with these families. For more on generalized linear models more generally, see @mccullagh1989generalized, @myers2012generalized, and @faraway2016extending.
+where $\boldsymbol{\mu}$ is the mean of $\mathbf{y}$, $g(\cdot)$ is a link function that "links" $\boldsymbol{\mu}$ to a linear function of the predictor variables and random errors, and all other terms are the same as those defined for linear spatial stream network models. Rather than assuming $y$ is normally (Gaussian) distributed as is often the case with linear spatial stream network models, generalized linear spatial stream network models assume $\mathbf{y}$ follows one of many distributions and has a corresponding link function. Below we summarize the families of generalized linear spatial stream network models supported by `SSN2` their link functions, and the type of data typically associated with these families. For more on generalized linear models more generally, see @mccullagh1989generalized, @myers2012generalized, and @faraway2016extending.
 
-The `ssn_glm()` function is used to fit generalized linear spatial stream network models and bears many similarities to base-**R**'s `glm()` function for non-spatial generalized linear models. The family (i.e., resposne distribution) is controlled by the `family` argument. When `family` is `Gaussian()`, the model fit is equivalent to one fit using `ssn_lm()`. Note that parameters are estimated on the relevant link scale and should be interpreted accordingly.
+The `ssn_glm()` function is used to fit generalized linear spatial stream network models and bears many similarities to base-**R**'s `glm()` function for non-spatial generalized linear models. The family (i.e., response distribution) is controlled by the `family` argument. When `family` is `Gaussian()`, the model fit is equivalent to one fit using `ssn_lm()`. Note that parameters are estimated on the relevant link scale and should be interpreted accordingly.
 
 | Family            | Link Function                                               | Link Name | Data Type                    | `SSN2` Function         |
 |---------------|---------------|---------------|---------------|---------------|
@@ -627,7 +618,7 @@ loocv_nb <- loocv(ssn_nb)
 loocv_nb$RMSPE
 ```
 
-The Poisson has a lower `RMSPE`, which suggests no evidence of overdispersion. 
+The negative binomial model has a lower `RMSPE`, which suggests some evidence of overdispersion. Other ways to assess the impact of overdispersion include 1) comparing the models using a likelihood-based fit statistic like AIC; if the AIC values are similar there is little evidence of overdispersion -- and 2) inspecting the dispersion parameter estimates of the negative binomial model; if the dispersion parameter is very large, there is little evidence of overdispersion.
 
 All advanced modeling features discussed for linear spatial stream network models (e.g., fixing covariance parameter values, random effects, partition factors) are also available for generalized linear spatial stream network models.
 
@@ -742,7 +733,7 @@ All functions that manipulate `SSN` objects have an `ssn_` prefix, which makes t
 
 Here we list some commonly used miscellaneous `SSN2` functions:
 
--   `ssn_simulate()`: Simulate spatially correlated random variables on a stream network.
+-   `ssn_simulate()`: Simulate spatially autocorrelated random variables on a stream network.
 
 For a full list of `SSN2` functions alongside their documentation, see the documentation manual.
 
@@ -797,5 +788,4 @@ labs <- setdiff(labs, c("setup", "get-labels"))
 
 # References {.unnumbered}
 
-::: {#refs}
-:::
+<div id="refs"></div>