diff --git a/R/occDetFunc.r b/R/occDetFunc.r
index f806a06..bdb26d4 100644
--- a/R/occDetFunc.r
+++ b/R/occDetFunc.r
@@ -1,11 +1,11 @@
 #' Occupancy detection Function
-#' 
+#'
 #' Run occupancy detection models using the output from \code{formatOccData}
-#' 
+#'
 #' This function requires both the R package R2jags and the program JAGS.
 #' These are not installed by default when sparta is loaded and so should be
 #' installed by the user. More details can be found in teh vignette.
-#' 
+#'
 #' @param taxa_name A character giving the name of the species to be modelled.
 #' @param occDetdata The 2nd element of the object returned by formatOccData.
 #' @param spp_vis The 1st element of the object returned by formatOccData.
@@ -24,13 +24,13 @@
 #' @param modeltype A character string or vector of strings that specifies the model to use. See details. If
 #' used then model.function is ignored.
 #' @param regional_codes A data.frame object detailing which site is associated with which region.
-#' each row desginates a site and each column represents a region. The first column represents the 
+#' each row desginates a site and each column represents a region. The first column represents the
 #' site name (as in \code{site}). Subsequent columns are named for each regions with 1 representing
 #' the site is in that region and 0 that it is not. NOTE a site should only be in one region
 #' @param region_aggs A named list giving aggregations of regions that you want trend
 #' estimates for. For example \code{region_aggs = list(GB = c('england', 'scotland', 'wales'))}
 #' will produced a trend for GB (Great Britain) as well as its constituent nations. Note that
-#' 'england', scotland' and 'wales' must appear as names of columns in \code{regional_codes}. 
+#' 'england', scotland' and 'wales' must appear as names of columns in \code{regional_codes}.
 #' More than one aggregate can be given, eg \code{region_aggs = list(GB = c('england', 'scotland',
 #'  'wales'), UK = c('england', 'scotland', 'wales', 'northern_ireland'))}.
 #' @param max_year numeric, final year to which analysis will be run, this can be set if it is beyond
@@ -40,45 +40,45 @@
 #' @param model.function optionally a user defined BUGS model coded as a function (see \code{?jags},
 #'        including the example there, for how this is done)
 #' @param additional.parameters A character vector of additional parameters to monitor
-#' @param additional.BUGS.elements A named list giving additioanl bugs elements passed 
+#' @param additional.BUGS.elements A named list giving additioanl bugs elements passed
 #' to \code{R2jags::jags} 'data' argument
-#' @param additional.init.values A named list giving user specified initial values to 
+#' @param additional.init.values A named list giving user specified initial values to
 #' be added to the defaults.
 #' @param return_data Logical, if \code{TRUE} (default) the BUGS data object is returned with the data
 #' @param saveMatrix Logical, if \code{FALSE} (default) the sims.matrix element of the jags object is omitted, in order to reduce the filesize.
 #' @param criterion Determines whether the model should be run. If an integer then this defines the threshold number of records (50 in Outhwaite et al 2019).
-#' Other options are `EqualWt` or `HighSpec`, which define the application of "rules of thumb" defined in Pocock et al 2019. 
+#' Other options are `EqualWt` or `HighSpec`, which define the application of "rules of thumb" defined in Pocock et al 2019.
 #' Defaults to 1, in which case the model is applied for so long there is a single record of the focal species.
 #' @param provenance An optional text string allowing the user to identify the dataset.
 #' @param rem_aggs_with_missing_regions An option which if TRUE will remove all aggregates which contain at least one region with no data.
 #' If `FALSE`, only aggregates where ALL regions in that aggregate contain no data, are dropped. Defaults to TRUE
 #' @param allowSitesMultiRegions An option that permits sites to be included in more than one region if `TRUE`. If `FALSE` then these sites are dropped. Defaults to `FALSE`
-#' 
+#'
 #' @details \code{modeltype} is used to choose the model as well as the associated initial values,
 #' and parameters to monitor. Elements to choose from can be separated into the following components:
-#' 
+#'
 #' A. Prior type: this has 3 options, each of which was tested in Outhwaite et al (in review):
 #'   1. sparta - This uses the same model as in Isaac et al (2014).
 #'   2. indran - This is the adaptive stationary model.
 #'   3. ranwalk - This is the random walk model.
-#' 
+#'
 #' B. Hyperprior type: This has 3 options, each of these are discussed in Outhwaite et al (in review):
 #'   1. halfuniform - the original formulation in Isaac et al (2014).
 #'   2. halfcauchy - preferred form, tested in Outhwaite et al (2018).
 #'   3. inversegamma - alternative form presented in the literature.
-#' 
+#'
 #' C. List length specification:  This has 3 options:
 #'   1. catlistlength - list length as a categorical variable.
 #'   2. contlistlength - list length as a continuous variable.
 #'   3. nolistlength - no list length variable.
-#' 
+#'
 #' D. Julian date: this is an additional option for including Julian date within the detection model:
 #'   1. jul_date.
-#' 
+#'
 #' Not all combinations are available in sparta. You will get an error if you try and use
 #' a combination that is not supported. There is usually a good reason why that
 #' combination is not a good idea. Here are the model elements available:
-#' 
+#'
 #' \itemize{
 #'  \item{\code{"sparta"}}{ - This uses the same model as in Isaac et al (2014)}
 #'  \item{\code{"indran"}}{ - Here the prior for the year effect of the state model is modelled as a random effect.  This allows the model to adapt to interannual variability.}
@@ -93,7 +93,7 @@
 #'  \item{\code{"centering"}}{ - No longer available. Includes hierarchical centering of the model parameters.   Centring does not change the model explicitly but writes it in a way that allows parameter estimates to be updated simultaneously.}
 #' }
 #' These options are provided as a vector of characters, e.g. \code{modeltype = c('indran', 'halfcauchy', 'catlistlength')}
-#' 
+#'
 #' @return A list including the model, JAGS model output, the path of the model file used and information on the number of iterations, first year, last year, etc.
 #' Key aspects of the model output include:
 #' \itemize{
@@ -131,33 +131,33 @@
 #'             Statistics for citizen science: extracting signals of change from noisy ecological data.
 #'             \emph{Methods in Ecology and Evolution}, 5: 1052-1060.
 #' @references Outhwaite, C.L., Chandler, R.E., Powney, G.D., Collen, B., Gregory, R.D. & Isaac, N.J.B. (2018).
-#'             Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data. 
+#'             Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data.
 #'             \emph{Ecological Indicators}, 93: 333-343.
 #' @references Pocock, Logie, Isaac, Outhwaite & August. Rapid assessment of the suitability of multi-species citizen science datasets for occupancy trend analysis. \emph{bioRxiv} 813626 (2019) doi:10.1101/813626.
-#'             
+#'
 #' @examples
 #' \dontrun{
-#' 
+#'
 #' set.seed(123)
-#' 
+#'
 #' # Create data
-#' n <- 15000 #size of dataset
+#' n <- 15000 # size of dataset
 #' nyear <- 20 # number of years in data
 #' nSamples <- 100 # set number of dates
 #' nSites <- 50 # set number of sites
-#' 
+#'
 #' # Create somes dates
-#' first <- as.Date(strptime("2010/01/01", format="%Y/%m/%d")) 
-#' last <- as.Date(strptime(paste(2010+(nyear-1),"/12/31", sep=''), format="%Y/%m/%d")) 
-#' dt <- last-first 
-#' rDates <- first + (runif(nSamples)*dt)
-#' 
+#' first <- as.Date(strptime("2010/01/01", format = "%Y/%m/%d"))
+#' last <- as.Date(strptime(paste(2010 + (nyear - 1), "/12/31", sep = ""), format = "%Y/%m/%d"))
+#' dt <- last - first
+#' rDates <- first + (runif(nSamples) * dt)
+#'
 #' # taxa are set as random letters
 #' taxa <- sample(letters, size = n, TRUE)
-#' 
+#'
 #' # sites are visited randomly
-#' site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)
-#' 
+#' site <- sample(paste("A", 1:nSites, sep = ""), size = n, TRUE)
+#'
 #' # the date of visit is selected at random from those created earlier
 #' survey <- sample(rDates, size = n, TRUE)
 #'
@@ -165,473 +165,522 @@
 #' visitData <- formatOccData(taxa = taxa, site = site, survey = survey)
 #'
 #' # run the model with these data for one species (very small number of iterations)
-#' results <- occDetFunc(taxa_name = taxa[1],
-#'                       n_iterations = 50,
-#'                       burnin = 15, 
-#'                       occDetdata = visitData$occDetdata,
-#'                       spp_vis = visitData$spp_vis,
-#'                       write_results = FALSE,
-#'                       provenance  = "sparta test dataset")
+#' results <- occDetFunc(
+#'   taxa_name = taxa[1],
+#'   n_iterations = 50,
+#'   burnin = 15,
+#'   occDetdata = visitData$occDetdata,
+#'   spp_vis = visitData$spp_vis,
+#'   write_results = FALSE,
+#'   provenance = "sparta test dataset"
+#' )
 #' }
 #' @export
 #' @importFrom reshape2 acast
 #' @import LearnBayes
 
-occDetFunc <- function (taxa_name, occDetdata, spp_vis, n_iterations = 5000, nyr = 2,
-                        burnin = 1500, thinning = 3, n_chains = 3, write_results = TRUE,
-                        output_dir = getwd(),  modeltype = 'sparta', max_year = NULL, 
-                        seed = NULL, model.function = NULL, regional_codes = NULL,
-                        region_aggs = NULL, additional.parameters = NULL,
-                        additional.BUGS.elements = NULL, additional.init.values = NULL,
-                        return_data = FALSE, criterion = 1, provenance = NULL, saveMatrix = FALSE,
-                        rem_aggs_with_missing_regions = TRUE, allowSitesMultiRegions = FALSE){
-  
+occDetFunc <- function(taxa_name, occDetdata, spp_vis, n_iterations = 5000, nyr = 2,
+                       burnin = 1500, thinning = 3, n_chains = 3, write_results = TRUE,
+                       output_dir = getwd(), modeltype = "sparta", max_year = NULL,
+                       seed = NULL, model.function = NULL, regional_codes = NULL,
+                       region_aggs = NULL, additional.parameters = NULL,
+                       additional.BUGS.elements = NULL, additional.init.values = NULL,
+                       return_data = FALSE, criterion = 1, provenance = NULL, saveMatrix = FALSE,
+                       rem_aggs_with_missing_regions = TRUE, allowSitesMultiRegions = FALSE) {
+
   ################## BASIC CHECKS
   # first run the error checks
-  errorChecks(n_iterations = n_iterations, burnin = burnin,
-              thinning = thinning, n_chains = n_chains, seed = seed)
-  
+  errorChecks(
+    n_iterations = n_iterations, burnin = burnin,
+    thinning = thinning, n_chains = n_chains, seed = seed
+  )
+
   # Set seed for repeatability
-  if(!is.null(seed)) set.seed(seed)
-  
+  if (!is.null(seed)) set.seed(seed)
+
   # Check the taxa_name is one of my species
-  if(!taxa_name %in% colnames(spp_vis)) stop('taxa_name is not the name of a taxa in spp_vis')
-  ################## 
+  if (!taxa_name %in% colnames(spp_vis)) stop("taxa_name is not the name of a taxa in spp_vis")
+  ##################
   min_year_original <- min_year <- min(occDetdata$TP)
-  
+
   # only include sites which have more than nyr of records
-  yps <- rowSums(acast(occDetdata, site ~ TP, length, value.var = 'L') > 0)
+  yps <- rowSums(acast(occDetdata, site ~ TP, length, value.var = "L") > 0)
   sites_to_include <- names(yps[yps >= nyr])
-  
+
   # strip out the visits to sites that were visited in just one year
   i <- occDetdata$site %in% sites_to_include
-  
-  if(sum(i) > 0){
-    occDetdata <- occDetdata[i,]
-    spp_vis <- spp_vis[i,]
-  } else stop(paste0("There are no sites visited in at least ", nyr, " years."))
 
-  # calcluate a set of data metrics for this species
-  data_Metrics <- dataMetrics(sp = taxa_name, 
-                                formattedData = list(occDetdata=occDetdata, spp_vis=spp_vis))
+  if (sum(i) > 0) {
+    occDetdata <- occDetdata[i, ]
+    spp_vis <- spp_vis[i, ]
+  } else {
+    stop(paste0("There are no sites visited in at least ", nyr, " years."))
+  }
+
+  # calculate a set of data metrics for this species
+  data_Metrics <- dataMetrics(
+    sp = taxa_name,
+    formattedData = list(occDetdata = occDetdata, spp_vis = spp_vis)
+  )
 
   is.wholenumber <-
-    function(x, tol = .Machine$double.eps^0.5)  {
-      if(is.numeric(x)) abs(x - round(x)) < tol
-      else FALSE
+    function(x, tol = .Machine$double.eps^0.5) {
+      if (is.numeric(x)) {
+        abs(x - round(x)) < tol
+      } else {
+        FALSE
       }
+    }
 
   # check there is enough data to run a model. If so, proceed with the main event
-  if(is.wholenumber(criterion)) {
-      # the criterion is a whole number. this defines the number of records
-      # check whether the number of records meets this value
-      proceed <- sum(spp_vis[,taxa_name]) >= criterion
-  } else if(criterion == "EqualWt") {
-      proceed <- applyRuleOfThumb(data_Metrics, "EqualWt")
-  } else if(criterion == "HighSpec") {
-      proceed <- applyRuleOfThumb(data_Metrics, "HighSpec")
-  } else
-      stop("Criterion must be either an integer, `EqualWt` or `HighSpec`")
+  if (is.wholenumber(criterion)) {
+    # the criterion is a whole number. this defines the number of records
+    # check whether the number of records meets this value
+    proceed <- sum(spp_vis[, taxa_name]) >= criterion
+  } else if (criterion == "EqualWt") {
+    proceed <- applyRuleOfThumb(data_Metrics, "EqualWt")
+  } else if (criterion == "HighSpec") {
+    proceed <- applyRuleOfThumb(data_Metrics, "HighSpec")
+  } else {
+    stop("Criterion must be either an integer, `EqualWt` or `HighSpec`")
+  }
 
   # Add the focal column (was the species recorded on the visit?). Use the spp_vis dataframe to extract this info
   nrow1 <- nrow(occDetdata)
-  occDetdata <- merge(occDetdata, spp_vis[,c("visit", taxa_name)])
+  occDetdata <- merge(occDetdata, spp_vis[, c("visit", taxa_name)])
   names(occDetdata)[names(occDetdata) == taxa_name] <- "focal"
-  if(nrow1 != nrow(occDetdata)) stop('some visits have been lost')
-  
-  if(!proceed){
+  if (nrow1 != nrow(occDetdata)) stop("some visits have been lost")
+
+  if (!proceed) {
     # there is not enough data: set the outputs accordingly
-    bugs_data <- list(y=0,nsite=0,nvisit=0)
+    bugs_data <- list(y = 0, nsite = 0, nvisit = 0)
     BD_MD <- error_status <- site_match <- modelcode <- NA
-    warning(paste(taxa_name,
-                  "has insufficient data after site filtering. Either decrease nyr or change the criterion"))
-    out <- list(message = "No model run: insufficient data")    
+    warning(paste(
+      taxa_name,
+      "has insufficient data after site filtering. Either decrease nyr or change the criterion"
+    ))
+    out <- list(message = "No model run: insufficient data")
   } else {
     # There is enough data: we can proceed with the main event
-    
+
     # Check if R2jags is installed
     if (!requireNamespace("R2jags", quietly = TRUE)) {
       stop("Package 'R2jags' is needed for the 'occDetModel' function to work. Please insatll this from CRAN. You will also be required to install JAGS, which you can download from https://sourceforge.net/projects/mcmc-jags/files/JAGS/",
-           call. = FALSE)
+        call. = FALSE
+      )
     }
-    
+
     # If doing regional we take control of model specification
-    if(!is.null(regional_codes)){
+    if (!is.null(regional_codes)) {
       oldmodeltype <- modeltype
-      modeltype <-c('ranwalk', 'halfcauchy',
-                    c('jul_date', 'catlistlength')[c('jul_date', 'catlistlength') %in% modeltype])
-      if(!all(oldmodeltype %in% modeltype))
-        message('When using regional data the model specification will be set to ranwalk, halfcauchy. jul_date and catlistlength can still be specified by the user')
-    }  
-    
-    if(!'catlistlength' %in% modeltype & !'nolistlength' %in% modeltype){
-      modeltype <- c(modeltype, 'contlistlength')
-    } 
-    if(!any(c('catlistlength', 'nolistlength', 'contlistlength') %in% modeltype)){
+      modeltype <- c(
+        "ranwalk", "halfcauchy",
+        c("jul_date", "catlistlength")[c("jul_date", "catlistlength") %in% modeltype]
+      )
+      if (!all(oldmodeltype %in% modeltype)) {
+        message("When using regional data the model specification will be set to ranwalk, halfcauchy. jul_date and catlistlength can still be specified by the user")
+      }
+    }
+
+    if (!"catlistlength" %in% modeltype & !"nolistlength" %in% modeltype) {
+      modeltype <- c(modeltype, "contlistlength")
+    }
+    if (!any(c("catlistlength", "nolistlength", "contlistlength") %in% modeltype)) {
       stop('modeltype should contain one of "catlistlength", "nolistlength", "contlistlength",
            which specify the list-length effect to be included')
     }
-    
+
     # Do we have JAGS installed - this works only on windows
-    if(.Platform$OS.type == "windows"){
-      JAGS_test <- Sys.which(names = 'jags-terminal.exe')
-      if(JAGS_test[[1]] == '') stop('R cannot find jags-terminal.exe, check that you have installed JAGS')
+    if (.Platform$OS.type == "windows") {
+      JAGS_test <- Sys.which(names = "jags-terminal.exe")
+      if (JAGS_test[[1]] == "") stop("R cannot find jags-terminal.exe, check that you have installed JAGS")
     }
-    
+
     # If we are using regional codes do some checks
-    if(!is.null(regional_codes)){
-      if(!inherits(regional_codes, 'data.frame')) stop("regional_codes should be a data.frame")
-      
-      # check whether there is a column called "site". 
-      #If not, let's assume that the site column is the first in the dataframe
-      #NB previous behaviour was to assume *both* that it was column 1 and named 'site'
-      if(!"site" %in% names(regional_codes)) {
+    if (!is.null(regional_codes)) {
+      if (!inherits(regional_codes, "data.frame")) stop("regional_codes should be a data.frame")
+
+      # check whether there is a column called "site".
+      # If not, let's assume that the site column is the first in the dataframe
+      # NB previous behaviour was to assume *both* that it was column 1 and named 'site'
+      if (!"site" %in% names(regional_codes)) {
         warning(paste0("renaming ", names(regional_codes)[1], " as 'site'"))
-        names(regional_codes)[1] <- "site" 
+        names(regional_codes)[1] <- "site"
       }
       siteCol <- grep("site", names(regional_codes))
-      
+
       # remove locations that are not in the data
       abs_sites <- as.character(regional_codes$site)[!as.character(regional_codes$site) %in% as.character(occDetdata$site)]
-      if(length(abs_sites) > 0){
-        warning(paste(length(abs_sites), 'sites are in regional_codes but not in occurrence data'))
+      if (length(abs_sites) > 0) {
+        warning(paste(length(abs_sites), "sites are in regional_codes but not in occurrence data"))
       }
-      if(any(is.na(regional_codes))){
-        warning(paste(sum(is.na(regional_codes)),
-                      "NAs are present in regional_codes, these will be replaced with 0s"))
+      if (any(is.na(regional_codes))) {
+        warning(paste(
+          sum(is.na(regional_codes)),
+          "NAs are present in regional_codes, these will be replaced with 0s"
+        ))
         regional_codes[is.na(regional_codes)] <- 0
       }
-      
-      sites_no_region <- as.character(regional_codes$site[rowSums(regional_codes[,-siteCol]) == 0])
-      sites_multi_region <- as.character(regional_codes$site[rowSums(regional_codes[,-siteCol]) > 1])
+
+      sites_no_region <- as.character(regional_codes$site[rowSums(regional_codes[, -siteCol]) == 0])
+      sites_multi_region <- as.character(regional_codes$site[rowSums(regional_codes[, -siteCol]) > 1])
       site_counts <- table(regional_codes$site)
       sites_multi_row <- names(site_counts[site_counts > 1])
-      
-      if(length(sites_no_region) > 0) 
-        warning(paste(length(sites_no_region), 'sites are not assigned to a region in regional_codes and will be removed'))
-      if(length(sites_multi_row) > 0)
-        warning(paste(length(sites_multi_row), 'sites appear more than once in regional_codes. These will be removed'))
-      if(length(sites_multi_region) > 0) 
-        if(allowSitesMultiRegions)
-          warning(paste(length(sites_multi_region), 'sites are assigned to multiple region in regional_codes: you have specified this is allowed'))
-        else
-          warning(paste(length(sites_multi_region), 'sites are assigned to more than one region in regional_codes and will be removed'))
-      
+
+      if (length(sites_no_region) > 0) {
+        warning(paste(length(sites_no_region), "sites are not assigned to a region in regional_codes and will be removed"))
+      }
+      if (length(sites_multi_row) > 0) {
+        warning(paste(length(sites_multi_row), "sites appear more than once in regional_codes. These will be removed"))
+      }
+      if (length(sites_multi_region) > 0) {
+        if (allowSitesMultiRegions) {
+          warning(paste(length(sites_multi_region), "sites are assigned to multiple region in regional_codes: you have specified this is allowed"))
+        } else {
+          warning(paste(length(sites_multi_region), "sites are assigned to more than one region in regional_codes and will be removed"))
+        }
+      }
+
       # finally check that every site with species data also has a region
       sites_no_region2 <- setdiff(sites_to_include, as.character(regional_codes$site))
-      if(length(sites_no_region2) >= 1) 
-        warning(paste(length(sites_no_region2), 'sites are in occurrence data but not in regional_codes and will be removed'))
-      
+      if (length(sites_no_region2) >= 1) {
+        warning(paste(length(sites_no_region2), "sites are in occurrence data but not in regional_codes and will be removed"))
+      }
+
       # strip these same sites out of the occDetdata & the regional codes
       bad_sites <- c(abs_sites, sites_multi_row, sites_no_region, sites_no_region2)
-      if(!allowSitesMultiRegions) bad_sites <- c(bad_sites, sites_multi_region)
+      if (!allowSitesMultiRegions) bad_sites <- c(bad_sites, sites_multi_region)
       bad_sites <- unique(bad_sites)
       regional_codes <- subset(regional_codes, !site %in% bad_sites)
       occDetdata <- subset(occDetdata, !site %in% bad_sites)
     }
-        
+
     # If we are using regional aggregates do some checks
-    if(!is.null(region_aggs)){
-      if(is.null(regional_codes)) stop('Cannot use regional aggregates if regional_codes is not supplied')
-      stopifnot(inherits(region_aggs, 'list'))
-      if(!all(unique(unlist(region_aggs)) %in% colnames(regional_codes)[-siteCol])){
-        stop(paste0('Aggregate members [',
-                    paste(unique(unlist(region_aggs))[!unique(unlist(region_aggs)) %in% colnames(regional_codes)[-siteCol]],
-                          collapse = ', '),
-                    '] not in regional_codes column names [',
-                    paste(colnames(regional_codes)[-siteCol],
-                          collapse = ', '),
-                    ']')) 
+    if (!is.null(region_aggs)) {
+      if (is.null(regional_codes)) stop("Cannot use regional aggregates if regional_codes is not supplied")
+      stopifnot(inherits(region_aggs, "list"))
+      if (!all(unique(unlist(region_aggs)) %in% colnames(regional_codes)[-siteCol])) {
+        stop(paste0(
+          "Aggregate members [",
+          paste(unique(unlist(region_aggs))[!unique(unlist(region_aggs)) %in% colnames(regional_codes)[-siteCol]],
+            collapse = ", "
+          ),
+          "] not in regional_codes column names [",
+          paste(colnames(regional_codes)[-siteCol],
+            collapse = ", "
+          ),
+          "]"
+        ))
       }
     }
-    
+
     # look for missing years before time frame can be extended using max_year parameter
-    years <- (max(occDetdata$TP) - min(occDetdata$TP))+1
-    if(length(unique(occDetdata$TP)) != years) {
+    years <- (max(occDetdata$TP) - min(occDetdata$TP)) + 1
+    if (length(unique(occDetdata$TP)) != years) {
       # find out which years have no data
-      missing_yrs <-  with(occDetdata, setdiff(min(TP):max(TP), unique(TP)))
-      if(length(missing_yrs) ==1)
-        error_msg <- paste0('There are no visits in year ', missing_yrs,'. This means there is no data, for any species in this year. BUGS cannot run if there is a year with no data. Quitting...')
-      else 
-        error_msg <- paste0('There are ', length(missing_yrs),' years with no visits, including ', missing_yrs[1],'. This means there is no data, for any species in these years. BUGS cannot run if any year has no data. Quitting...')
+      missing_yrs <- with(occDetdata, setdiff(min(TP):max(TP), unique(TP)))
+      if (length(missing_yrs) == 1) {
+        error_msg <- paste0("There are no visits in year ", missing_yrs, ". This means there is no data, for any species in this year. BUGS cannot run if there is a year with no data. Quitting...")
+      } else {
+        error_msg <- paste0("There are ", length(missing_yrs), " years with no visits, including ", missing_yrs[1], ". This means there is no data, for any species in these years. BUGS cannot run if any year has no data. Quitting...")
+      }
       stop(error_msg)
     }
 
     # year and site need to be numeric starting from 1 to length of them.  This is due to the way the bugs code is written
     nsite <- length(unique(occDetdata$site))
     site_match <- data.frame(name = unique(occDetdata$site), id = 1:nsite)
-    occDetdata <- merge(occDetdata, site_match, by.x='site', by.y="name")
-    
+    occDetdata <- merge(occDetdata, site_match, by.x = "site", by.y = "name")
+
     # need to get a measure of whether the species was on that site in that year, unequivocally, in zst
-    zst <- acast(occDetdata, id ~ factor(TP), value.var = 'focal', max, fill = 0) # initial values for the latent state = observed state
+    zst <- acast(occDetdata, id ~ factor(TP), value.var = "focal", max, fill = 0) # initial values for the latent state = observed state
 
     # Calculate min year. We're doing it now as it's fine if we've dropped the first year(s) of data and nothing in the middle
     min_year <- min(occDetdata$TP)
-    if(min_year != min_year_original){
-      warning(paste0('\nThe first year of the data has changed, as the first years of data were dropped.\n',
-                    'The original first year was ',min_year_original,'. It is now ',min_year,'\n'))
+    if (min_year != min_year_original) {
+      warning(paste0(
+        "\nThe first year of the data has changed, as the first years of data were dropped.\n",
+        "The original first year was ", min_year_original, ". It is now ", min_year, "\n"
+      ))
     }
-    
+
     # if the max_year is not null, edit the zst table to add the additional years required
-    if(!is.null(max_year)){
-      
+    if (!is.null(max_year)) {
       # check that max_year is a numeric value
-      if(!is.numeric(max_year)) stop('max_year should be a numeric value')
-      
+      if (!is.numeric(max_year)) stop("max_year should be a numeric value")
+
       # check that max_year is greater than the final year of the dataset
-      if(max_year <= max(occDetdata$TP)) stop('max_year should be greater than the final year of available data')
-      
+      if (max_year <= max(occDetdata$TP)) stop("max_year should be greater than the final year of available data")
+
       nTP <- max_year - min_year + 1
-      
-      # if nyear is greater than the number of years due to different specification of max_year, 
+
+      # if nyear is greater than the number of years due to different specification of max_year,
       # add on additional columns to zst so that inital values can be create for these years.
-      
-      if(nTP > ncol(zst)){
+
+      if (nTP > ncol(zst)) {
         # work out how many columns need to be added
         to_add <- nTP - ncol(zst)
         zst <- cbind(zst, matrix(0, ncol = to_add, nrow = nrow(zst)))
         # add column names
-        colnames(zst) <- 1:nTP 
+        colnames(zst) <- 1:nTP
       }
-      
+
       # if a value has not been selected for max_year then continue as before
-    }else{
+    } else {
       # record the max year
       max_year <- max(occDetdata$TP)
       nTP <- max_year - min_year + 1
     }
-    
+
     # look for time periods with no data
-    if(length(unique(occDetdata$TP)) != nTP) stop('It looks like you have time periods with no data. This will crash BUGS')
-    
+    if (length(unique(occDetdata$TP)) != nTP) stop("It looks like you have time periods with no data. This will crash BUGS")
+
     # TP and site need to be numeric starting from 1 to length of them.  This is due to the way the bugs code is written
     occDetdata$TP <- occDetdata$TP - min(occDetdata$TP) + 1
-  
+
     # Parameter you wish to monitor, shown in the output
     parameters <- c("psi.fs", "tau2", "tau.lp", "alpha.p", "a")
-    
-    # If ranwalk + halfcauchy monitor mu.lp 
-    if(all(c('ranwalk', 'halfcauchy') %in% modeltype)){
-      if(!'centering' %in% tolower(modeltype) & !'intercept' %in% tolower(modeltype)){
+
+    # If ranwalk + halfcauchy monitor mu.lp
+    if (all(c("ranwalk", "halfcauchy") %in% modeltype)) {
+      if (!"centering" %in% tolower(modeltype) & !"intercept" %in% tolower(modeltype)) {
         parameters <- c(parameters, "mu.lp")
       }
     }
-    
-    # If sparta monitor mu.lp 
-    if('sparta' %in% tolower(modeltype)) {
+
+    # If sparta monitor mu.lp
+    if ("sparta" %in% tolower(modeltype)) {
       parameters <- c(parameters, "mu.lp")
     }
-    
+
     # Add user specified paramters if given
-    if(!is.null(additional.parameters)) parameters <- c(parameters, additional.parameters)
-    
+    if (!is.null(additional.parameters)) parameters <- c(parameters, additional.parameters)
+
     # Add parameters for each of the model types
-    for(ptype in modeltype){
+    for (ptype in modeltype) {
       parameters <- getParameters(parameters, modeltype = ptype)
     }
-    
+
     # add parameters for regions
-    if(!is.null(regional_codes)){
+    if (!is.null(regional_codes)) {
       # remove spaces from region names, then extract them
-      colnames(regional_codes) <- gsub(' ', '_', colnames(regional_codes))
-      region_names <- setdiff(colnames(regional_codes), "site")      
-      
-      parameters <- c(parameters,
-                      paste0("psi.fs.r_", region_names),
-                      paste0("a_", region_names))
+      colnames(regional_codes) <- gsub(" ", "_", colnames(regional_codes))
+      region_names <- setdiff(colnames(regional_codes), "site")
+
+      parameters <- c(
+        parameters,
+        paste0("psi.fs.r_", region_names),
+        paste0("a_", region_names)
+      )
       # ignore some parameters not used in regions model
-      parameters <- parameters[!parameters %in% c('a')]
+      parameters <- parameters[!parameters %in% c("a")]
     }
-    
+
     # add parameters for regional aggregates
-    if(!is.null(region_aggs)){
-      parameters <- c(parameters, paste0('psi.fs.r_', names(region_aggs)))
+    if (!is.null(region_aggs)) {
+      parameters <- c(parameters, paste0("psi.fs.r_", names(region_aggs)))
     }
-    
+
     # now assemble the bugs_data and related objects
     # HERE IS THE BUGS DATA
-    bugs_data <- with(occDetdata, 
-                      list(y = as.numeric(focal), Year = TP, Site = id, 
-                           nyear = nTP, nsite = nsite, nvisit = nrow(occDetdata)))
+    bugs_data <- with(
+      occDetdata,
+      list(
+        y = as.numeric(focal), Year = TP, Site = id,
+        nyear = nTP, nsite = nsite, nvisit = nrow(occDetdata)
+      )
+    )
 
     # temporary test
-    if(max(occDetdata$id) != bugs_data$nsite) stop(paste0("Site idenitifier exceeds nsite (",
-                                                          max(occDetdata$id), nsite,")"))
-    
-    
-    for(btype in modeltype){ # one call per element of modeltype: each adds a section
-      bugs_data <- getBugsData(bugs_data, modeltype = btype,
-                               occDetData = occDetdata)
+    if (max(occDetdata$id) != bugs_data$nsite) {
+      stop(paste0(
+        "Site idenitifier exceeds nsite (",
+        max(occDetdata$id), nsite, ")"
+      ))
+    }
+
+
+    for (btype in modeltype) { # one call per element of modeltype: each adds a section
+      bugs_data <- getBugsData(bugs_data,
+        modeltype = btype,
+        occDetData = occDetdata
+      )
     }
 
     # Add additional elements if specified
-    if(!is.null(additional.BUGS.elements)){
-      if(!is.list(additional.BUGS.elements)){
+    if (!is.null(additional.BUGS.elements)) {
+      if (!is.list(additional.BUGS.elements)) {
         stop("additional.BUGS.elements must be a list")
       } else {
         bugs_data <- c(bugs_data, additional.BUGS.elements)
       }
     }
-    
-    # Add regional elements to bugs data
-    if(!is.null(regional_codes)){
 
+    # Add regional elements to bugs data
+    if (!is.null(regional_codes)) {
       # removed unwanted bugs elements
-      bugs_data <- bugs_data[!names(bugs_data) %in% c('psi0.a', 'psi0.b')]
-      
+      bugs_data <- bugs_data[!names(bugs_data) %in% c("psi0.a", "psi0.b")]
+
       # expand the lookup table to include regions
-      regional_lookup <- merge(regional_codes, site_match, by.y="name", by.x="site")
-      
+      regional_lookup <- merge(regional_codes, site_match, by.y = "name", by.x = "site")
+
       zero_sites <- NULL
-      for(region in region_names){
-        if(sum(regional_codes[ , region]) != 0){
-          bugs_data[paste0('r_', region)] <- list(regional_lookup[order(regional_lookup$id),region])
-          bugs_data[paste0('nsite_r_', region)] <- sum(regional_codes[, region])
+      for (region in region_names) {
+        if (sum(regional_codes[, region]) != 0) {
+          bugs_data[paste0("r_", region)] <- list(regional_lookup[order(regional_lookup$id), region])
+          bugs_data[paste0("nsite_r_", region)] <- sum(regional_codes[, region])
         } else {
           zero_sites <- c(zero_sites, region)
         }
       }
-      
-      if(!is.null(zero_sites)){
-        warning(paste('The following regions have no data and should not be modelled:',
-                      paste(zero_sites, collapse = ', '),
-                      '- These regions will not be included in the model'))
+
+      if (!is.null(zero_sites)) {
+        warning(paste(
+          "The following regions have no data and should not be modelled:",
+          paste(zero_sites, collapse = ", "),
+          "- These regions will not be included in the model"
+        ))
         # remove parameters
-        parameters <- parameters[!parameters %in% c(paste0("psi.fs.r_", zero_sites),
-                                                    paste0("a_", zero_sites))]
+        parameters <- parameters[!parameters %in% c(
+          paste0("psi.fs.r_", zero_sites),
+          paste0("a_", zero_sites)
+        )]
         # remove regions for regions_codes
-        regional_codes <- regional_codes[ ,!colnames(regional_codes) %in% zero_sites]
+        regional_codes <- regional_codes[, !colnames(regional_codes) %in% zero_sites]
         region_names <- setdiff(region_names, zero_sites)
 
         # remove region aggregates
-        if(rem_aggs_with_missing_regions){
+        if (rem_aggs_with_missing_regions) {
           rem_aggs <- unlist(lapply(region_aggs, FUN = function(x) any(zero_sites %in% x)))
           rem_aggs_names <- names(region_aggs)[rem_aggs]
-          
+
           # remove aggs if you need to
-          if(length(rem_aggs_names) > 0){
-            warning(paste('The following region aggregates have to be removed as they contain a region with no data:',
-                          paste(rem_aggs_names, collapse = ', '),
-                          '- These region aggregates will not be included in the model\n',
-                          'If you want to keep aggregates with one or more missing regions,',
-                          'set rem_aggs_with_missing_regions=FALSE'))
+          if (length(rem_aggs_names) > 0) {
+            warning(paste(
+              "The following region aggregates have to be removed as they contain a region with no data:",
+              paste(rem_aggs_names, collapse = ", "),
+              "- These region aggregates will not be included in the model\n",
+              "If you want to keep aggregates with one or more missing regions,",
+              "set rem_aggs_with_missing_regions=FALSE"
+            ))
             region_aggs <- region_aggs[!names(region_aggs) %in% rem_aggs_names]
-            parameters <- parameters[!parameters %in% paste0('psi.fs.r_', rem_aggs_names)]
+            parameters <- parameters[!parameters %in% paste0("psi.fs.r_", rem_aggs_names)]
           }
         } else {
           rem_aggs <- unlist(lapply(region_aggs, FUN = function(x) all(x %in% zero_sites)))
           rem_aggs_names <- names(region_aggs)[rem_aggs]
           edit_aggs <- unlist(lapply(region_aggs, FUN = function(x) any(zero_sites %in% x)))
           edit_aggs_names <- names(region_aggs)[edit_aggs & !(rem_aggs)]
-          
+
           # edit aggs if you need to
-          if(length(edit_aggs_names) > 0){
-            warning(paste('The following region aggregates have to be edited as they contain regions with no data:',
-                          paste(edit_aggs_names, collapse = ', '),
-                          '\n- These region aggregates will still be included in the model\n'))
+          if (length(edit_aggs_names) > 0) {
+            warning(paste(
+              "The following region aggregates have to be edited as they contain regions with no data:",
+              paste(edit_aggs_names, collapse = ", "),
+              "\n- These region aggregates will still be included in the model\n"
+            ))
             # Recreate aggs, removing regions without data
-            region_aggs_new <- lapply(region_aggs, FUN = function(agg){
+            region_aggs_new <- lapply(region_aggs, FUN = function(agg) {
               agg[!(agg %in% zero_sites)]
             })
             names(region_aggs_new) <- names(region_aggs)
             region_aggs <- region_aggs_new
           }
-          
+
           # remove aggs completely if you need to
-          if(length(rem_aggs_names) > 0){
-            warning(paste('The following region aggregates have to be removed as all regions within them have no data:',
-                          paste(rem_aggs_names, collapse = ', '),
-                          '- These region aggregates will not be included in the model'))
+          if (length(rem_aggs_names) > 0) {
+            warning(paste(
+              "The following region aggregates have to be removed as all regions within them have no data:",
+              paste(rem_aggs_names, collapse = ", "),
+              "- These region aggregates will not be included in the model"
+            ))
             region_aggs <- region_aggs[!names(region_aggs) %in% rem_aggs_names]
-            parameters <- parameters[!parameters %in% paste0('psi.fs.r_', rem_aggs_names)]
+            parameters <- parameters[!parameters %in% paste0("psi.fs.r_", rem_aggs_names)]
           }
         }
-      } 
+      }
 
       regions_years <- list()
       temp <- bugs_data[grepl("^r_", names(bugs_data))]
       regions_nobs <- sapply(temp, function(x) sum(x[bugs_data$Site] * bugs_data$y))
       regions_sites <- unlist(bugs_data[grepl("nsite_r", names(bugs_data))])
-  
+
       # make a copy of the bugs_data to calculate metadata from
       bugs_data_copy <- with(occDetdata, data.frame(y = as.numeric(focal), year = TP, site = site))
       bugs_data_copy <- merge(bugs_data_copy, regional_codes, all.x = TRUE)
-      
+
       # add regional codes to this copy and get max and min years and year gaps for each region
-      for(region_name in region_names){
-        
-        #regions_nobs[paste0('n_obs_','r_', region_name)] <- sum(bugs_data_copy$y * bugs_data_copy[,region_name])
-        #regions_sites[paste0('n_sites_','r_', region_name)] <- sum(bugs_data_copy[,region_name])
-        current_r <- bugs_data_copy$y * bugs_data_copy[,region_name] * bugs_data_copy$year
-        current_r <- subset(current_r,current_r !=0)
-        
-        if(length(current_r) > 2){
-          
-          current_rmin <- (min_year-1) + min(current_r)
-          current_rmax <- (min_year-1) + max(current_r)
-          regions_years[paste0('min_year_data_','r_', region_name)] <- current_rmin
-          regions_years[paste0('max_year_data_','r_', region_name)] <- current_rmax
+      for (region_name in region_names) {
+        # regions_nobs[paste0('n_obs_','r_', region_name)] <- sum(bugs_data_copy$y * bugs_data_copy[,region_name])
+        # regions_sites[paste0('n_sites_','r_', region_name)] <- sum(bugs_data_copy[,region_name])
+        current_r <- bugs_data_copy$y * bugs_data_copy[, region_name] * bugs_data_copy$year
+        current_r <- subset(current_r, current_r != 0)
+
+        if (length(current_r) > 2) {
+          current_rmin <- (min_year - 1) + min(current_r)
+          current_rmax <- (min_year - 1) + max(current_r)
+          regions_years[paste0("min_year_data_", "r_", region_name)] <- current_rmin
+          regions_years[paste0("max_year_data_", "r_", region_name)] <- current_rmax
           current_datagaps <- dataGaps(current_r, min_year, max_year, current_rmin, current_rmax)
-          regions_years[paste0('gap_start_','r_', region_name)] <- current_datagaps$gap_start
-          regions_years[paste0('gap_end_','r_', region_name)] <- current_datagaps$gap_end
-          regions_years[paste0('gap_middle_','r_', region_name)] <- current_datagaps$gap_middle
-        
-        } else if(length(current_r) == 1) {
-          
-          current_rmin <- (min_year-1) + min(current_r)
-          current_rmax <- (min_year-1) + max(current_r)
-          regions_years[paste0('min_year_data_','r_', region_name)] <- current_rmin
-          regions_years[paste0('max_year_data_','r_', region_name)] <- current_rmax
+          regions_years[paste0("gap_start_", "r_", region_name)] <- current_datagaps$gap_start
+          regions_years[paste0("gap_end_", "r_", region_name)] <- current_datagaps$gap_end
+          regions_years[paste0("gap_middle_", "r_", region_name)] <- current_datagaps$gap_middle
+        } else if (length(current_r) == 1) {
+          current_rmin <- (min_year - 1) + min(current_r)
+          current_rmax <- (min_year - 1) + max(current_r)
+          regions_years[paste0("min_year_data_", "r_", region_name)] <- current_rmin
+          regions_years[paste0("max_year_data_", "r_", region_name)] <- current_rmax
           current_datagaps <- dataGaps(current_r, min_year, max_year, current_rmin, current_rmax)
-          regions_years[paste0('gap_start_','r_', region_name)] <- current_datagaps$gap_start
-          regions_years[paste0('gap_end_','r_', region_name)] <- current_datagaps$gap_end
-          regions_years[paste0('gap_middle_','r_', region_name)] <- NA
-          
-        } else if(length(current_r) < 1){
-          
-          regions_years[paste0('min_year_data_','r_', region_name)] <- NA
-          regions_years[paste0('max_year_data_','r_', region_name)] <- NA
-          regions_years[paste0('gap_start_','r_', region_name)] <- NA
-          regions_years[paste0('gap_end_','r_', region_name)] <- NA
-          regions_years[paste0('gap_middle_','r_', region_name)] <- NA
-          
+          regions_years[paste0("gap_start_", "r_", region_name)] <- current_datagaps$gap_start
+          regions_years[paste0("gap_end_", "r_", region_name)] <- current_datagaps$gap_end
+          regions_years[paste0("gap_middle_", "r_", region_name)] <- NA
+        } else if (length(current_r) < 1) {
+          regions_years[paste0("min_year_data_", "r_", region_name)] <- NA
+          regions_years[paste0("max_year_data_", "r_", region_name)] <- NA
+          regions_years[paste0("gap_start_", "r_", region_name)] <- NA
+          regions_years[paste0("gap_end_", "r_", region_name)] <- NA
+          regions_years[paste0("gap_middle_", "r_", region_name)] <- NA
         }
       }
     }
-    
+
     # start preparing metadata
     BD_MD <- list()
-    
+
     # add max and min data years for the whole dataset
     all_years_data <- bugs_data$y * bugs_data$Year
-    all_years_data <- subset(all_years_data, all_years_data !=0)
-    BD_MD$min_year_data <- (min_year-1) + min(all_years_data)
-    BD_MD$max_year_data <- (min_year-1) + max(all_years_data)
-    
+    all_years_data <- subset(all_years_data, all_years_data != 0)
+    BD_MD$min_year_data <- (min_year - 1) + min(all_years_data)
+    BD_MD$max_year_data <- (min_year - 1) + max(all_years_data)
+
     # use these to find year gap data
-    BD_MD$yeargaps<-dataGaps(all_years_data, min_year, max_year, BD_MD$min_year_data, BD_MD$max_year_data)
-    
-    
+    BD_MD$yeargaps <- dataGaps(all_years_data, min_year, max_year, BD_MD$min_year_data, BD_MD$max_year_data)
+
+
     initiate <- function(z, nTP, bugs_data) {
-      init <- list (z = z,
-                    alpha.p = rep(runif(1, -2, 2),
-                                  nTP),
-                    a = rep(runif(1, -2, 2), nTP),
-                    eta = rep(runif(1, -2, 2), bugs_data$nsite))
-      
+      init <- list(
+        z = z,
+        alpha.p = rep(
+          runif(1, -2, 2),
+          nTP
+        ),
+        a = rep(runif(1, -2, 2), nTP),
+        eta = rep(runif(1, -2, 2), bugs_data$nsite)
+      )
+
       # add extra init values if needed
-      for(itype in modeltype){
+      for (itype in modeltype) {
         init <- getInitValues(init, modeltype = itype)
       }
-      
+
       # if ranwalk + centreing a -> aa
-      if(all(c('ranwalk', 'centering') %in% modeltype)){
-        names(init)[names(init) == 'a'] <- 'aa'
+      if (all(c("ranwalk", "centering") %in% modeltype)) {
+        names(init)[names(init) == "a"] <- "aa"
       }
-      
+
       # add user specified values
-      if(!is.null(additional.init.values)){
-        if(!is.list(additional.init.values)){
+      if (!is.null(additional.init.values)) {
+        if (!is.list(additional.init.values)) {
           stop("additional.BUGS.elements must be a list")
         } else {
           init <- c(init, additional.init.values)
@@ -639,157 +688,169 @@ occDetFunc <- function (taxa_name, occDetdata, spp_vis, n_iterations = 5000, nyr
       }
       return(init)
     }
-    
-    # set the initial values... 
-    init.vals <- replicate(n_chains, initiate(z = zst,
-                                              nTP = nTP,
-                                              bugs_data = bugs_data),
-                           simplify = F)
-    
+
+    # set the initial values...
+    init.vals <- replicate(n_chains, initiate(
+      z = zst,
+      nTP = nTP,
+      bugs_data = bugs_data
+    ),
+    simplify = F
+    )
+
     # modify initial values for regional model
-    if(!is.null(regional_codes)){
+    if (!is.null(regional_codes)) {
       # remove initial values for a and psi
-      init.vals <- lapply(init.vals, FUN = function(x){
-        x[!names(x) %in% c('psi0', 'a')]
+      init.vals <- lapply(init.vals, FUN = function(x) {
+        x[!names(x) %in% c("psi0", "a")]
       })
     }
-    
+
     # Select the correct model file
-    if(is.null(model.function)){
+    if (is.null(model.function)) {
       model.file <- getModelFile(modeltype,
-                                 regional_codes = regional_codes,
-                                 region_aggs = region_aggs)
+        regional_codes = regional_codes,
+        region_aggs = region_aggs
+      )
     } else {
-      cat('Using user model.function')
+      cat("Using user model.function")
       model.file <- model.function
     }
-    
-    modelcode <- paste(readLines(model.file), collapse = '\n')
-    
+
+    modelcode <- paste(readLines(model.file), collapse = "\n")
+
     ### REVIEW CODE
-    cat('#### PLEASE REVIEW THE BELOW ####\n\n')
-    cat('Your model settings:', paste(modeltype, collapse = ', '))
-    cat('\n\nModel File:\n\n')
+    cat("#### PLEASE REVIEW THE BELOW ####\n\n")
+    cat("Your model settings:", paste(modeltype, collapse = ", "))
+    cat("\n\nModel File:\n\n")
     cat(modelcode)
-    cat('\n\nbugs_data:\n\n')
+    cat("\n\nbugs_data:\n\n")
     cat(str(bugs_data))
-    cat('\n\ninit.vals:\n\n')
+    cat("\n\ninit.vals:\n\n")
     cat(str(init.vals))
-    cat('\n\nparameters:\n\n')
+    cat("\n\nparameters:\n\n")
     cat(parameters)
     ###
-    
-    if(identical(model.file, occDetBUGScode)){
-      warning("You have selected a formulation with potentially informative priors that are subject to boundary effects (See Outhwaite et al 2018 for details). 
-      This option is retained within sparta for backwards compatibility only: we strongly recommend that you do 
+
+    if (identical(model.file, occDetBUGScode)) {
+      warning("You have selected a formulation with potentially informative priors that are subject to boundary effects (See Outhwaite et al 2018 for details).
+      This option is retained within sparta for backwards compatibility only: we strongly recommend that you do
               not use this option for inferring changes in species' distributions")
     }
-    
-    error_status <- try(    
-      out <- R2jags::jags(bugs_data, init.vals, parameters, model.file = model.file,
-                          n.chains = n_chains, n.iter = n_iterations, n.thin = thinning,
-                          n.burnin = burnin, DIC = TRUE)
+
+    error_status <- try(
+      out <- R2jags::jags(bugs_data, init.vals, parameters,
+        model.file = model.file,
+        n.chains = n_chains, n.iter = n_iterations, n.thin = thinning,
+        n.burnin = burnin, DIC = TRUE
+      )
     )
-    
+
     dir.create(path = output_dir, showWarnings = FALSE) # create the top results folder
-    if (class(error_status) == "try-error" ){
-        
-      warning('JAGS returned an error when modelling', taxa_name, 'error:', error_status[1])
+    if (class(error_status) == "try-error") {
+      warning("JAGS returned an error when modelling", taxa_name, "error:", error_status[1])
       return(NULL)
-    
-    }
-  }  # end of "if(proceed)"
-  
-    ########################################## Add metadata
-    
-    # calcluate number of site:year combinations with repeat visits (across the whole dataset)
-    temp <- as.data.frame(with(occDetdata, table(site, TP)))$Freq
-    prop_visits_repeated <- mean(temp[temp>0] > 1)
-
-    if(is.null(attributes(occDetdata))){
-      metadata <- list()
-    } else if('metadata' %in% names(attributes(occDetdata))){
-      metadata <- attr(occDetdata, 'metadata')
-    } else {
-      metadata <- list()
     }
-    
-    # get the sessionInfo and coerce into a useable format
-    session.info <- sessionInfo()
-    
-    packages <- c(sapply(session.info$otherPkgs, function(x) x$Version),
-                  sapply(session.info$loadedOnly, function(x) x$Version))
-    
-    MD <- list(method = 'sparta::occDetFunc',
-               call =   call <- match.call(),
-               date = Sys.Date(),
-               user = Sys.info()['user'],
-               summary = list(species = taxa_name,
-                              n_sites = length(unique(occDetdata$site)),
-                              n_years = length(unique(occDetdata$TP)),
-                              n_visits = nrow(occDetdata),
-                              n_obs = sum(occDetdata$focal),
-                              n_species_sites = length(unique(subset(occDetdata, focal==TRUE)$site)),
-                              min_year_model = min_year,
-                              max_year_model = max_year),
-                gaps = ifelse(is.na(BD_MD), NA, list( 
-                              min_year_data = BD_MD$min_year_data,
-                              max_year_data = BD_MD$max_year_data,
-                              gap_start = BD_MD$yeargaps$gap_start,
-                              gap_end = BD_MD$yeargaps$gap_end,
-                              gap_middle = BD_MD$yeargaps$gap_middle)),
-               spp_Metrics = as.list(data_Metrics),
-               dataset_Metrics = list(# dataset properties
-                              totalObservations = sum(occDetdata$L),
-                              propRepeats = prop_visits_repeated),
-               provenance = provenance,
-               output_path = ifelse(test = write_results,
-                                    file.path(getwd(), output_dir, paste(taxa_name, ".rdata", sep = "")),
-                                    NA),
-               session_info = list(session.info[-c(7:8)],
-                                   packages)
-               )
-    
-    # add regional elements if applicable
-    if(!is.null(regional_codes) & proceed){
-      MD$summary$region_nobs <- regions_nobs
-      MD$summary$region_years <- unlist(regions_years)
-      MD$summary$region_nsite <- regions_sites
-    }else{
-      MD$summary$region_nobs <- NA
-      MD$summary$region_years <- NA
-      MD$summary$region_nsite <- NA
-    }
-    
-    # If the data coming in is the result of analysis we want to
-    # append this data
-    name <- 'analysis'
-    i = 1
-    while(name %in% names(metadata)){
-      name <- paste0(name, i)
-      i = i + 1 
-    }
-    
-    metadata[name] <- list(MD)
-    attr(out, 'metadata') <- metadata
-
-    if(!saveMatrix) out$BUGSoutput$sims.matrix <- NULL
-    
-    out$SPP_NAME <- taxa_name
-    out$min_year <- min_year
-    out$max_year <- max_year
-    out$sites_included <- ifelse(test = proceed, yes = site_match, no = NA)
-    out$nsites <- bugs_data$nsite
-    out$nvisits <- bugs_data$nvisit
-    out$species_observations <- sum(bugs_data$y)
-    out$sparta_version <- packages["sparta"]
-    if(!is.null(regional_codes)) out$regions <- region_names
-    if(!is.null(region_aggs)) out$region_aggs <- region_aggs
-    if(return_data) out$bugs_data <- bugs_data
-    attr(out, 'modeltype') <- modeltype
-    attr(out, 'modelcode') <- modelcode
-    class(out) <- 'occDet'
-    if(write_results) save(out, file = file.path(output_dir, paste(taxa_name, ".rdata", sep = "")))  
-    return(out)
-  }  	
-  
\ No newline at end of file
+  } # end of "if(proceed)"
+
+  ########################################## Add metadata
+
+  # calcluate number of site:year combinations with repeat visits (across the whole dataset)
+  temp <- as.data.frame(with(occDetdata, table(site, TP)))$Freq
+  prop_visits_repeated <- mean(temp[temp > 0] > 1)
+
+  if (is.null(attributes(occDetdata))) {
+    metadata <- list()
+  } else if ("metadata" %in% names(attributes(occDetdata))) {
+    metadata <- attr(occDetdata, "metadata")
+  } else {
+    metadata <- list()
+  }
+
+  # get the sessionInfo and coerce into a useable format
+  session.info <- sessionInfo()
+  packages <- c(
+    sapply(session.info$basePkgs, packageVersion, simplify = FALSE),
+    sapply(session.info$otherPkgs, function(x) x$Version)
+  )
+
+  MD <- list(
+    method = "sparta::occDetFunc",
+    call = call <- match.call(),
+    date = Sys.Date(),
+    user = Sys.info()["user"],
+    summary = list(
+      species = taxa_name,
+      n_sites = length(unique(occDetdata$site)),
+      n_years = length(unique(occDetdata$TP)),
+      n_visits = nrow(occDetdata),
+      n_obs = sum(occDetdata$focal),
+      n_species_sites = length(unique(subset(occDetdata, focal == TRUE)$site)),
+      min_year_model = min_year,
+      max_year_model = max_year
+    ),
+    gaps = ifelse(is.na(BD_MD), NA, list(
+      min_year_data = BD_MD$min_year_data,
+      max_year_data = BD_MD$max_year_data,
+      gap_start = BD_MD$yeargaps$gap_start,
+      gap_end = BD_MD$yeargaps$gap_end,
+      gap_middle = BD_MD$yeargaps$gap_middle
+    )),
+    spp_Metrics = as.list(data_Metrics),
+    dataset_Metrics = list( # dataset properties
+      totalObservations = sum(occDetdata$L),
+      propRepeats = prop_visits_repeated
+    ),
+    provenance = provenance,
+    output_path = ifelse(test = write_results,
+      file.path(getwd(), output_dir, paste(taxa_name, ".rdata", sep = "")),
+      NA
+    ),
+    session_info = list(
+      session.info[1:2],
+      packages
+    )
+  )
+
+  # add regional elements if applicable
+  if (!is.null(regional_codes) & proceed) {
+    MD$summary$region_nobs <- regions_nobs
+    MD$summary$region_years <- unlist(regions_years)
+    MD$summary$region_nsite <- regions_sites
+  } else {
+    MD$summary$region_nobs <- NA
+    MD$summary$region_years <- NA
+    MD$summary$region_nsite <- NA
+  }
+
+  # If the data coming in is the result of analysis we want to
+  # append this data
+  name <- "analysis"
+  i <- 1
+  while (name %in% names(metadata)) {
+    name <- paste0(name, i)
+    i <- i + 1
+  }
+
+  metadata[name] <- list(MD)
+  attr(out, "metadata") <- metadata
+
+  if (!saveMatrix) out$BUGSoutput$sims.matrix <- NULL
+
+  out$SPP_NAME <- taxa_name
+  out$min_year <- min_year
+  out$max_year <- max_year
+  out$sites_included <- ifelse(test = proceed, yes = site_match, no = NA)
+  out$nsites <- bugs_data$nsite
+  out$nvisits <- bugs_data$nvisit
+  out$species_observations <- sum(bugs_data$y)
+  out$sparta_version <- packages["sparta"]
+  if (!is.null(regional_codes)) out$regions <- region_names
+  if (!is.null(region_aggs)) out$region_aggs <- region_aggs
+  if (return_data) out$bugs_data <- bugs_data
+  attr(out, "modeltype") <- modeltype
+  attr(out, "modelcode") <- modelcode
+  class(out) <- "occDet"
+  if (write_results) save(out, file = file.path(output_dir, paste(taxa_name, ".rdata", sep = "")))
+  return(out)
+}
diff --git a/tests/testthat/helper-errorChecks.R b/tests/testthat/helper-errorChecks.R
new file mode 100644
index 0000000..8f99d65
--- /dev/null
+++ b/tests/testthat/helper-errorChecks.R
@@ -0,0 +1,49 @@
+# Create data
+n <- 3000 # size of dataset
+nyr <- 10 # number of years in data
+nSamples <- 30 # set number of dates
+nSites <- 15 # set number of sites
+
+# Create somes dates
+first <- as.POSIXct(strptime("2010/01/01", "%Y/%m/%d"))
+last <- as.POSIXct(strptime(paste(2010 + (nyr - 1), "/12/31", sep = ""), "%Y/%m/%d"))
+dt <- last - first
+rDates <- first + (runif(nSamples) * dt)
+
+# taxa are set as random letters
+taxa <- sample(letters, size = n, TRUE)
+
+# three sites are visited randomly
+site <- sample(paste("A", 1:nSites, sep = ""), size = n, TRUE)
+
+# the date of visit is selected at random from those created earlier
+time_period <- sample(rDates, size = n, TRUE)
+time_period_missing <- sample(c(rDates, NA), size = n, TRUE)
+
+dist_sub <- rnorm(n, 10, 1)
+sim_sub <- rnorm(n, 10, 1)
+
+dist_sub_fac <- as.factor(rnorm(n, 10, 1))
+sim_sub_fac <- as.factor(rnorm(n, 10, 1))
+
+
+dist_sub_chr <- as.character(rnorm(n, 10, 1))
+sim_sub_chr <- as.character(rnorm(n, 10, 1))
+
+dist_sub_fac <- as.factor(rnorm(n, 10, 1))
+sim_sub_fac <- as.factor(rnorm(n, 10, 1))
+
+
+# combine this to a dataframe
+df <- data.frame(
+  taxa = taxa,
+  site = site,
+  time_period = as.character(time_period),
+  time_period_missing = time_period_missing,
+  dist_sub = dist_sub,
+  sim_sub = sim_sub,
+  dist_sub_chr = dist_sub_chr,
+  sim_sub_chr = sim_sub_chr,
+  dist_sub_fac = dist_sub_fac,
+  sim_sub_fac = sim_sub_fac
+)
diff --git a/tests/testthat/test-errorChecks.R b/tests/testthat/test-errorChecks.R
new file mode 100644
index 0000000..320b5fd
--- /dev/null
+++ b/tests/testthat/test-errorChecks.R
@@ -0,0 +1,272 @@
+# Test error for startDate not in date format
+test_that("Does it detect when startDate is not in a date format?", {
+  expect_error(
+    errorChecks(startDate = df$time_period),
+    'startDate is not in a date format. This should be of class "Date" or "POSIXct"'
+  )
+})
+
+# Test error for startDate containing NAs
+test_that("Does it detect when startDate contains NAs?", {
+  expect_error(
+    errorChecks(startDate = df$time_period_missing),
+    "startDate must not contain NAs"
+  )
+})
+
+# Test error for Date not being a data.frame or date vector
+test_that("Does it detect when date is not a dataframe or date vector?", {
+  expect_error(
+    errorChecks(Date = df$time_period),
+    "Date must be a data.frame or date vector"
+  )
+})
+
+# Test error for Date containing NAs
+test_that("Does it detect when time_period_missing contains NAs?", {
+  expect_error(
+    errorChecks(Date = df$time_period_missing),
+    "Date must not contain NAs"
+  )
+})
+
+# Test error for endDate not in date format
+test_that("Does it detect when time_period is not in a date format?", {
+  expect_error(
+    errorChecks(endDate = df$time_period),
+    'endDate is not in a date format. This should be of class "Date" or "POSIXct"'
+  )
+})
+
+# Test error for endDate containing NAs
+test_that("Does it detect when endDate contains NAs?", {
+  expect_error(
+    errorChecks(endDate = df$time_period_missing),
+    "endDate must not contain NAs"
+  )
+})
+
+# Test error for dist_sub must be integer or numeric
+test_that("Does it detect when dist_sub is not an integer or numeric value?", {
+  expect_error(
+    errorChecks(dist_sub = df$dist_sub_chr, sim_sub = df$sim_sub),
+    "dist_sub must be integer or numeric"
+  )
+  expect_error(
+    errorChecks(dist_sub = df$dist_sub_fac, sim_sub = df$sim_sub),
+    "dist_sub must be integer or numeric"
+  )
+})
+
+test_that("Does it detect when sim_sub is not an integer or numeric value?", {
+  expect_error(
+    errorChecks(dist_sub = df$dist_sub, sim_sub = df$sim_sub_fac),
+    "sim_sub must be integer or numeric"
+  )
+
+  expect_error(
+    errorChecks(dist_sub = df$dist_sub, sim_sub = df$sim_sub_chr),
+    "sim_sub must be integer or numeric"
+  )
+})
+
+# Test error for useIterations must be logical
+test_that("Does it detect when iterations are not logical?", {
+  useIterations_num <- 1
+  useIterations_chr <- "TRUE"
+
+
+  expect_error(
+    errorChecks(useIterations = useIterations_chr),
+    "useIterations must be logical"
+  )
+  expect_error(
+    errorChecks(useIterations = useIterations_num),
+    "useIterations must be logical"
+  )
+})
+
+# Test error for iterations, family, thinning, and numeric parameters
+test_that("Does it detect when iterations are not an integer?", {
+  expect_error(
+    errorChecks(iterations = "1000"),
+    "iterations must be numeric or integer"
+  )
+})
+
+test_that("Does it detect when Family is not Binomial or Bernoulli?", {
+  expect_error(
+    errorChecks(family = "Poisson"),
+    "family must be either Binomial or Bernoulli"
+  )
+})
+
+test_that("Does it detect when thinning is larger than the number of iterations?", {
+  expect_error(
+    errorChecks(n_iterations = 1000, burnin = 500, thinning = 1100, n_chains = 3),
+    "thinning must not be larger that the number of iteration (n_iterations)",
+    fixed = TRUE
+  )
+})
+
+test_that("Does it detect when n_iterations is not an integer", {
+  expect_error(
+    errorChecks(n_iterations = "1000", burnin = 500, thinning = 5, n_chains = 3),
+    "n_iterations should be numeric"
+  )
+})
+
+test_that("Does it detect when burnin is not an integer?", {
+  expect_error(
+    errorChecks(n_iterations = 1000, burnin = "500", thinning = 5, n_chains = 3),
+    "burnin should be numeric"
+  )
+})
+
+test_that("Does it detect when thinning is not an integer?", {
+  expect_error(
+    errorChecks(n_iterations = 1000, burnin = 500, thinning = "5", n_chains = 3),
+    "thinning should be numeric"
+  )
+})
+
+test_that("Does it detect when n_chains is not an integer?", {
+  expect_error(
+    errorChecks(n_iterations = 1000, burnin = 500, thinning = 5, n_chains = "3"),
+    "n_chains should be numeric"
+  )
+})
+
+test_that("Does it detect when The seed is not an integer?", {
+  expect_error(
+    errorChecks(seed = "1"),
+    "seed muct be numeric"
+  )
+})
+
+# Test for detecting missing year_col when start_col and end_col are NA
+test_that("can it detect when year_col is missing with start_col and end_col as NA?", {
+  expect_error(
+    errorChecks(year_col = NA, start_col = NA, end_col = df$time_period[1]),
+    "year_col or start_col and end_col must be given"
+  )
+})
+
+# Test for detecting missing end_col when year_col is NA
+test_that("can it detect when end_col is missing with year_col as NA?", {
+  expect_error(
+    errorChecks(year_col = NA, start_col = df$time_period[1], end_col = NA),
+    "year_col or start_col and end_col must be given"
+  )
+})
+
+# Test for detecting simultaneous use of year_col with start_col and end_col
+test_that("can it detect the simultaneous use of year_col with start_col and end_col?", {
+  expect_error(
+    errorChecks(year_col = NA, start_col = df$time_period[1], end_col = df$time_period[1]),
+    "year_col cannot be used at the same time as start_col and end_col"
+  )
+})
+
+# Test for detecting phi value below the permitted range
+test_that("can it detect when phi is below the permitted range?", {
+  expect_error(
+    errorChecks(phi = 0.1),
+    "phi is outside permitted range of 0.50 to 0.95"
+  )
+})
+
+# Test for detecting phi value above the permitted range
+test_that("can it detect when phi is above the permitted range?", {
+  expect_error(
+    errorChecks(phi = 0.99),
+    "phi is outside permitted range of 0.50 to 0.95"
+  )
+})
+
+# Test for detecting alpha value below the permitted range
+test_that("can it detect when alpha is below the permitted range?", {
+  expect_error(
+    errorChecks(alpha = 0.05),
+    "alpha is outside permitted range of 0.08 to 0.50"
+  )
+})
+
+# Test for detecting alpha value above the permitted range
+test_that("can it detect when alpha is above the permitted range?", {
+  expect_error(
+    errorChecks(alpha = 0.99),
+    "alpha is outside permitted range of 0.08 to 0.50"
+  )
+})
+
+# Test for detecting fres_site_filter not being a character vector
+test_that("can it detect when fres_site_filter is not a character vector?", {
+  expect_error(
+    errorChecks(fres_site_filter = c(1, 5, 10, 12)),
+    "fres_site_filter must be a character vector"
+  )
+})
+
+# Test for detecting non_benchmark_sp not being a character vector
+test_that("can it detect when non_benchmark_sp is not a character vector?", {
+  expect_error(
+    errorChecks(non_benchmark_sp = c(1, 5, 10, 12)),
+    "non_benchmark_sp must be a character vector"
+  )
+})
+
+# Test for detecting fres_site_filter not being a character vector
+test_that("can it detect when fres_site_filter is not a character vector and is a single number?", {
+  expect_error(
+    errorChecks(fres_site_filter = 1),
+    "fres_site_filter must be a character vector"
+  )
+})
+
+# Test for detecting fres_site_filter not being a character vector
+test_that("can it detect when fres_site_filter is not a character vector and is a single number?", {
+  expect_error(
+    errorChecks(non_benchmark_sp = 1),
+    "non_benchmark_sp must be a character vector"
+  )
+})
+
+# Test for detecting incorrect time_periods format
+test_that("can it detect when time_periods format is incorrect?", {
+  expect_error(
+    errorChecks(time_periods = as.matrix(df$time_period)),
+    'time_periods should be a data.frame. e.g. "data.frame(start=c(1980,1990),end=c(1989,1999))"',
+    fixed = TRUE
+  )
+})
+
+# Test for detecting incorrect filepath for a .exe file
+test_that("can it detect when the filepath is not to a '.exe' file?", {
+  temp <- tempfile()
+
+  dir.create(temp)
+
+
+  expect_error(
+    errorChecks(frespath = temp),
+    "filepath is not the path to a '.exe' file"
+  )
+})
+
+# Test for detecting non-existent .exe file
+test_that("can it detect when a .exe file does not exist?", {
+  expect_error(
+    errorChecks(frespath = "file.exe"),
+    "file.exe does not exist"
+  )
+})
+
+# Test error for site must not contain empty values
+test_that("Does it detect empty string values?", {
+  expect_error(
+    errorChecks(site = c("a", "b", "")),
+    "site must not contain empty values (i.e. '')",
+    fixed = TRUE
+  )
+})
diff --git a/tests/testthat/testoccDetFunc.r b/tests/testthat/testoccDetFunc.r
index ff0a200..228e011 100755
--- a/tests/testthat/testoccDetFunc.r
+++ b/tests/testthat/testoccDetFunc.r
@@ -1,39 +1,45 @@
 context("Test occDetFunc")
 
 # Create data
-n <- 15000 #size of dataset
+n <- 15000 # size of dataset
 nyr <- 20 # number of years in data
 nSamples <- 100 # set number of dates
 nSites <- 50 # set number of sites
 set.seed(125)
 
 # Create somes dates
-first <- as.Date(strptime("2010/01/01", "%Y/%m/%d")) 
-last <- as.Date(strptime(paste(2010+(nyr-1),"/12/31", sep=''), "%Y/%m/%d")) 
-dt <- last-first 
-rDates <- first + (runif(nSamples)*dt)
+first <- as.Date(strptime("2010/01/01", "%Y/%m/%d"))
+last <- as.Date(strptime(paste(2010 + (nyr - 1), "/12/31", sep = ""), "%Y/%m/%d"))
+dt <- last - first
+rDates <- first + (runif(nSamples) * dt)
 
 # taxa are set as random letters
 taxa <- sample(letters, size = n, TRUE)
 
 # three sites are visited randomly
-site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)
+site <- sample(paste("A", 1:nSites, sep = ""), size = n, TRUE)
 
 # the date of visit is selected at random from those created earlier
 survey <- sample(rDates, size = n, TRUE)
 
 # set up regions for some testing
-regionsDF <- data.frame(site = unique(site),
-                      region1 = c(rep(1, 20), rep(0, 30)),
-                      region2 = c(rep(0, 20), rep(1, 15), rep(0, 15)),
-                      region3 = c(rep(0, 20), rep(0, 15), rep(1, 15)))
+regionsDF <- data.frame(
+  site = unique(site),
+  region1 = c(rep(1, 20), rep(0, 30)),
+  region2 = c(rep(0, 20), rep(1, 15), rep(0, 15)),
+  region3 = c(rep(0, 20), rep(0, 15), rep(1, 15))
+)
 
 # format data
-suppressWarnings({visitData <- formatOccData(taxa = taxa, site = site,
-                                             survey = survey)})
+suppressWarnings({
+  visitData <- formatOccData(
+    taxa = taxa, site = site,
+    survey = survey
+  )
+})
 
 ## additional data for testing missing years
-# remove one years 
+# remove one years
 time_period_missing <- sub("2018-", "2019-", survey)
 time_period_missing <- as.Date(time_period_missing)
 
@@ -42,415 +48,617 @@ time_period_missing2 <- sub("2017-", "2019-", time_period_missing)
 time_period_missing2 <- as.Date(time_period_missing2)
 
 # format data
-suppressWarnings({visitData_missing <- formatOccData(taxa = taxa, site = site,
-                                                     survey = time_period_missing)})
-suppressWarnings({visitData_missing2 <- formatOccData(taxa = taxa, site = site,
-                                                     survey = time_period_missing2)})
+suppressWarnings({
+  visitData_missing <- formatOccData(
+    taxa = taxa, site = site,
+    survey = time_period_missing
+  )
+})
+suppressWarnings({
+  visitData_missing2 <- formatOccData(
+    taxa = taxa, site = site,
+    survey = time_period_missing2
+  )
+})
 
 # additional data for testing species lost after nyr filter
-taxa_filterError <- c(taxa,"aa")
-site_filterError <- c(site,"B1")
-survey_filterError <- c(survey,sample(rDates, size = 1, TRUE)) 
+taxa_filterError <- c(taxa, "aa")
+site_filterError <- c(site, "B1")
+survey_filterError <- c(survey, sample(rDates, size = 1, TRUE))
 
-suppressWarnings({visitData_filterError <- formatOccData(taxa = taxa_filterError, site = site_filterError,
-                                                      survey = survey_filterError)})
+suppressWarnings({
+  visitData_filterError <- formatOccData(
+    taxa = taxa_filterError, site = site_filterError,
+    survey = survey_filterError
+  )
+})
 
 test_that("Test occDetFunc errors", {
-  
- expect_error(results <- occDetFunc(taxa_name = 'a',
-                       n_iterations = 50,
-                       burnin = 500, 
-                       occDetdata = visitData$occDetdata,
-                       spp_vis = visitData$spp_vis,
-                       write_results = FALSE,
-                       seed = 111),
-              'must not be larger that the number of iteration')
-  
-  expect_error(results <- occDetFunc(taxa_name = 'a',
-                                     n_iterations = 50,
-                                     burnin = 15, 
-                                     occDetdata = visitData$occDetdata,
-                                     spp_vis = visitData$spp_vis,
-                                     write_results = FALSE,
-                                     criterion = "OnlyGoodData",
-                                     seed = 111),
-               "Criterion must be either an integer, `EqualWt` or `HighSpec`")
-  
-  
-  expect_error(results <- occDetFunc(taxa_name = 'apple',
-                                    n_iterations = 50,
-                                    burnin = 15, 
-                                    occDetdata = visitData$occDetdata,
-                                    spp_vis = visitData$spp_vis,
-                                    write_results = FALSE,
-                                    criterion = "EqualWt",
-                                    seed = 111),
-              'taxa_name is not the name of a taxa in spp_vis')
-
- expect_error(results <- occDetFunc(taxa_name = 'a',
-                                    n_iterations = 50,
-                                    burnin = 15, 
-                                    occDetdata = visitData_missing$occDetdata,
-                                    spp_vis = visitData_missing$spp_vis,
-                                    write_results = FALSE,
-                                    criterion = "HighSpec",
-                                    seed = 111),
-              'There are no visits in year 2018. This means there is no data, for any species in this year. BUGS cannot run if there is a year with no data. Quitting...')
-
-  expect_error(results <- occDetFunc(taxa_name = 'a',
-                                    n_iterations = 50,
-                                    burnin = 15, 
-                                    occDetdata = visitData_missing2$occDetdata,
-                                    spp_vis = visitData_missing2$spp_vis,
-                                    write_results = FALSE,
-                                    seed = 111),
-              'There are 2 years with no visits, including 2017. This means there is no data, for any species in these years. BUGS cannot run if any year has no data. Quitting...')
- 
-  
- expect_error(results <- occDetFunc(taxa_name = 'a',
-                                    n_iterations = 50,
-                                    burnin = 15, 
-                                    occDetdata = visitData$occDetdata,
-                                    spp_vis = visitData$spp_vis,
-                                    max_year = "2028",
-                                    write_results = FALSE,
-                                    seed = 111),
-              'max_year should be a numeric value')
- 
- expect_error(results <- occDetFunc(taxa_name = 'a',
-                                    n_iterations = 50,
-                                    burnin = 15, 
-                                    occDetdata = visitData$occDetdata,
-                                    spp_vis = visitData$spp_vis,
-                                    max_year = 2028,
-                                    write_results = FALSE,
-                                    seed = 111),
-              'max_year should be greater than the final year of available data')
- 
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 500,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111
+    ),
+    "must not be larger that the number of iteration"
+  )
+
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      criterion = "OnlyGoodData",
+      seed = 111
+    ),
+    "Criterion must be either an integer, `EqualWt` or `HighSpec`"
+  )
+
+
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "apple",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      criterion = "EqualWt",
+      seed = 111
+    ),
+    "taxa_name is not the name of a taxa in spp_vis"
+  )
+
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData_missing$occDetdata,
+      spp_vis = visitData_missing$spp_vis,
+      write_results = FALSE,
+      criterion = "HighSpec",
+      seed = 111
+    ),
+    "There are no visits in year 2018. This means there is no data, for any species in this year. BUGS cannot run if there is a year with no data. Quitting..."
+  )
+
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData_missing2$occDetdata,
+      spp_vis = visitData_missing2$spp_vis,
+      write_results = FALSE,
+      seed = 111
+    ),
+    "There are 2 years with no visits, including 2017. This means there is no data, for any species in these years. BUGS cannot run if any year has no data. Quitting..."
+  )
+
+
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      max_year = "2028",
+      write_results = FALSE,
+      seed = 111
+    ),
+    "max_year should be a numeric value"
+  )
+
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      max_year = 2028,
+      write_results = FALSE,
+      seed = 111
+    ),
+    "max_year should be greater than the final year of available data"
+  )
 })
 
 test_that("Test occDetFunc with defaults", {
-  
-  sink(file=ifelse(Sys.info()["sysname"] == "Windows",
-                   "NUL",
-                   "/dev/null"))
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111)
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
+    "NUL",
+    "/dev/null"
+  ))
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111
+  )
   sink()
 
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits","species_observations","sparta_version"))
-  
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
 })
 
 test_that("Test occDetFunc with model types", {
-  
-  sink(file=ifelse(Sys.info()["sysname"] == "Windows",
-                   "NUL",
-                   "/dev/null"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('indran', 'centering',
-                                                  'halfcauchy'))
-  expect_identical(results$SPP_NAME, 'a')
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
+    "NUL",
+    "/dev/null"
+  ))
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "indran", "centering",
+      "halfcauchy"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations","sparta_version"))
-
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('indran', 'centering',
-                                                  'inversegamma'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "indran", "centering",
+      "inversegamma"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('indran', 'intercept'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c("indran", "intercept")
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('indran', 'intercept',
-                                                  'halfcauchy'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "indran", "intercept",
+      "halfcauchy"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('indran', 'intercept',
-                                                  'inversegamma'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "indran", "intercept",
+      "inversegamma"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk', 'centering',
-                                                  'halfcauchy'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "ranwalk", "centering",
+      "halfcauchy"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk', 'centering',
-                                                  'inversegamma'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "ranwalk", "centering",
+      "inversegamma"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk', 'intercept'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c("ranwalk", "intercept")
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk', 'intercept',
-                                                  'halfcauchy'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "ranwalk", "intercept",
+      "halfcauchy"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk', 'intercept',
-                                                  'inversegamma'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "ranwalk", "intercept",
+      "inversegamma"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk',
-                                                  'halfcauchy'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "ranwalk",
+      "halfcauchy"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+
   sink()
-  
 })
 
 test_that("Test occDetFunc with julian date", {
-  
-  sink(file=ifelse(Sys.info()["sysname"] == "Windows",
-                   "NUL",
-                   "/dev/null"))
-  
-  suppressWarnings({visitData <- formatOccData(taxa = taxa, site = site,
-                                               survey = survey,
-                                               includeJDay = TRUE)})
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk', 'intercept',
-                                                  'inversegamma', 'jul_date'))
-  expect_identical(results$SPP_NAME, 'a')
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
+    "NUL",
+    "/dev/null"
+  ))
+
+  suppressWarnings({
+    visitData <- formatOccData(
+      taxa = taxa, site = site,
+      survey = survey,
+      includeJDay = TRUE
+    )
+  })
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "ranwalk", "intercept",
+      "inversegamma", "jul_date"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  expect_true('beta1' %in% row.names(results$BUGSoutput$summary))
-  expect_true('beta2' %in% row.names(results$BUGSoutput$summary))
-  expect_true('beta3' %in% row.names(results$BUGSoutput$summary))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('indran', 'centering',
-                                                  'halfcauchy', 'jul_date'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+  expect_true("beta1" %in% row.names(results$BUGSoutput$summary))
+  expect_true("beta2" %in% row.names(results$BUGSoutput$summary))
+  expect_true("beta3" %in% row.names(results$BUGSoutput$summary))
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "indran", "centering",
+      "halfcauchy", "jul_date"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  expect_true('beta1' %in% row.names(results$BUGSoutput$summary))
-  expect_true('beta2' %in% row.names(results$BUGSoutput$summary))
-  expect_true('beta3' %in% row.names(results$BUGSoutput$summary))
-  
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+  expect_true("beta1" %in% row.names(results$BUGSoutput$summary))
+  expect_true("beta2" %in% row.names(results$BUGSoutput$summary))
+  expect_true("beta3" %in% row.names(results$BUGSoutput$summary))
+
   sink()
-  
-})  
+})
 
 test_that("Test occDetFunc with catagorical list length", {
-  
-  sink(file=ifelse(Sys.info()["sysname"] == "Windows",
-                   "NUL",
-                   "/dev/null"))
-  
-  suppressWarnings({visitData <- formatOccData(taxa = taxa, site = site,
-                                               survey = survey,
-                                               includeJDay = TRUE)})
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('ranwalk', 'intercept',
-                                                  'inversegamma', 'catlistlength'))
-  expect_identical(results$SPP_NAME, 'a')
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
+    "NUL",
+    "/dev/null"
+  ))
+
+  suppressWarnings({
+    visitData <- formatOccData(
+      taxa = taxa, site = site,
+      survey = survey,
+      includeJDay = TRUE
+    )
+  })
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "ranwalk", "intercept",
+      "inversegamma", "catlistlength"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  expect_true('dtype2.p' %in% row.names(results$BUGSoutput$summary))
-  expect_true('dtype3.p' %in% row.names(results$BUGSoutput$summary))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111, modeltype = c('indran', 'centering',
-                                                  'halfcauchy', 'catlistlength'))
-  expect_identical(results$SPP_NAME, 'a')
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+  expect_true("dtype2.p" %in% row.names(results$BUGSoutput$summary))
+  expect_true("dtype3.p" %in% row.names(results$BUGSoutput$summary))
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111, modeltype = c(
+      "indran", "centering",
+      "halfcauchy", "catlistlength"
+    )
+  )
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations", "sparta_version"))
-  expect_true('dtype2.p' %in% row.names(results$BUGSoutput$summary))
-  expect_true('dtype3.p' %in% row.names(results$BUGSoutput$summary))
-  
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+  expect_true("dtype2.p" %in% row.names(results$BUGSoutput$summary))
+  expect_true("dtype3.p" %in% row.names(results$BUGSoutput$summary))
+
   sink()
-  
-})  
+})
 
 test_that("Test occDetFunc using regions and region aggregates", {
-  
-  sink(file=ifelse(Sys.info()["sysname"] == "Windows",
-                   "NUL",
-                   "/dev/null"))
-  
-  results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111,
-                        modeltype = c("ranwalk", "halfcauchy"),
-                        regional_codes = regionsDF,
-                        region_aggs = list(agg1 = c('region1', 'region2')))
-  
-  expect_identical(results$SPP_NAME, 'a')
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
+    "NUL",
+    "/dev/null"
+  ))
+
+  results <- occDetFunc(
+    taxa_name = "a",
+    n_iterations = 50,
+    burnin = 15,
+    occDetdata = visitData$occDetdata,
+    spp_vis = visitData$spp_vis,
+    write_results = FALSE,
+    seed = 111,
+    modeltype = c("ranwalk", "halfcauchy"),
+    regional_codes = regionsDF,
+    region_aggs = list(agg1 = c("region1", "region2"))
+  )
+
+  expect_identical(results$SPP_NAME, "a")
   expect_identical(results$n.iter, 50)
-  expect_identical(names(results),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations","sparta_version",
-                     "regions", "region_aggs"))
-  expect_identical(results$regions,
-                   c("region1", "region2", "region3"))
+  expect_identical(
+    names(results),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version",
+      "regions", "region_aggs"
+    )
+  )
+  expect_identical(
+    results$regions,
+    c("region1", "region2", "region3")
+  )
   expect_identical(names(results$region_aggs), "agg1")
   RNs <- row.names(results$BUGSoutput$summary)
   expect_true("a_region1[1]" %in% RNs)
@@ -460,143 +668,183 @@ test_that("Test occDetFunc using regions and region aggregates", {
   expect_true("psi.fs.r_region2[1]" %in% RNs)
   expect_true("psi.fs.r_region3[1]" %in% RNs)
   expect_true("psi.fs.r_agg1[1]" %in% RNs)
-  
+
   # test with a region with no data
-  regionsempty<-regionsDF
+  regionsempty <- regionsDF
   regionsempty$region4 <- 0
-  
-  expect_warning(results <- occDetFunc(taxa_name = 'a',
-                        n_iterations = 50,
-                        burnin = 15, 
-                        occDetdata = visitData$occDetdata,
-                        spp_vis = visitData$spp_vis,
-                        write_results = FALSE,
-                        seed = 111,
-                        modeltype = c("ranwalk", "halfcauchy"),
-                        regional_codes = regionsempty,
-                        region_aggs = list(agg1 = c('region1', 'region2'))),
-                 'The following regions have no data and should not be modelled: region4 - These regions will not be included in the model')
-  
+
+  expect_warning(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = regionsempty,
+      region_aggs = list(agg1 = c("region1", "region2"))
+    ),
+    "The following regions have no data and should not be modelled: region4 - These regions will not be included in the model"
+  )
+
   # test with regional_codes not as a dataframe
-  expect_error(results <- occDetFunc(taxa_name = 'a',
-                                       n_iterations = 50,
-                                       burnin = 15, 
-                                       occDetdata = visitData$occDetdata,
-                                       spp_vis = visitData$spp_vis,
-                                       write_results = FALSE,
-                                       seed = 111,
-                                       modeltype = c("ranwalk", "halfcauchy"),
-                                       regional_codes = as.matrix(regionsDF),
-                                       region_aggs = list(agg1 = c('region1', 'region2'))),
-                 'regional_codes should be a data.frame')
-  
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = as.matrix(regionsDF),
+      region_aggs = list(agg1 = c("region1", "region2"))
+    ),
+    "regional_codes should be a data.frame"
+  )
+
   # test with NAs in regional_codes
   regionsNA <- regionsDF
-  regionsNA[1,3] <- NA
-  
-  expect_warning(results <- occDetFunc(taxa_name = 'a',
-                                     n_iterations = 50,
-                                     burnin = 15, 
-                                     occDetdata = visitData$occDetdata,
-                                     spp_vis = visitData$spp_vis,
-                                     write_results = FALSE,
-                                     seed = 111,
-                                     modeltype = c("ranwalk", "halfcauchy"),
-                                     regional_codes = regionsNA,
-                                     region_aggs = list(agg1 = c('region1', 'region2'))),
-               "NAs are present in regional_codes, these will be replaced with 0s")
-  
+  regionsNA[1, 3] <- NA
+
+  expect_warning(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = regionsNA,
+      region_aggs = list(agg1 = c("region1", "region2"))
+    ),
+    "NAs are present in regional_codes, these will be replaced with 0s"
+  )
+
   # test with sites in multiple regions
   regionsmulti <- regionsDF
-  regionsmulti[1,3] <- 1
-  
-  expect_warning(results <- occDetFunc(taxa_name = 'a',
-                                       n_iterations = 50,
-                                       burnin = 15, 
-                                       occDetdata = visitData$occDetdata,
-                                       spp_vis = visitData$spp_vis,
-                                       write_results = FALSE,
-                                       seed = 111,
-                                       modeltype = c("ranwalk", "halfcauchy"),
-                                       regional_codes = regionsmulti,
-                                       region_aggs = list(agg1 = c('region1', 'region2'))),
-                  '1 sites are assigned to more than one region in regional_codes and will be removed')
-  
+  regionsmulti[1, 3] <- 1
+
+  expect_warning(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = regionsmulti,
+      region_aggs = list(agg1 = c("region1", "region2"))
+    ),
+    "1 sites are assigned to more than one region in regional_codes and will be removed"
+  )
+
   # test with sites in no regions
   regionsmulti <- regionsDF
-  regionsmulti[1,2] <- 0
-  
-  expect_warning(results <- occDetFunc(taxa_name = 'a',
-                                     n_iterations = 50,
-                                     burnin = 15, 
-                                     occDetdata = visitData$occDetdata,
-                                     spp_vis = visitData$spp_vis,
-                                     write_results = FALSE,
-                                     seed = 111,
-                                     modeltype = c("ranwalk", "halfcauchy"),
-                                     regional_codes = regionsmulti,
-                                     region_aggs = list(agg1 = c('region1', 'region2'))),
-               '1 sites are not assigned to a region in regional_codes and will be removed')
-  
+  regionsmulti[1, 2] <- 0
+
+  expect_warning(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = regionsmulti,
+      region_aggs = list(agg1 = c("region1", "region2"))
+    ),
+    "1 sites are not assigned to a region in regional_codes and will be removed"
+  )
+
   # test for sites in occurence data but not regions
-  regionsmissing <- regionsDF[2:50,]
-  
-  expect_warning(results <- occDetFunc(taxa_name = 'a',
-                                     n_iterations = 50,
-                                     burnin = 15, 
-                                     occDetdata = visitData$occDetdata,
-                                     spp_vis = visitData$spp_vis,
-                                     write_results = FALSE,
-                                     seed = 111,
-                                     modeltype = c("ranwalk", "halfcauchy"),
-                                     regional_codes = regionsmissing,
-                                     region_aggs = list(agg1 = c('region1', 'region2'))),
-               '1 sites are in occurrence data but not in regional_codes and will be removed')
-  
+  regionsmissing <- regionsDF[2:50, ]
+
+  expect_warning(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = regionsmissing,
+      region_aggs = list(agg1 = c("region1", "region2"))
+    ),
+    "1 sites are in occurrence data but not in regional_codes and will be removed"
+  )
+
   # test for regional aggregate containing unknown region
-  expect_error(results <- occDetFunc(taxa_name = 'a',
-                                       n_iterations = 50,
-                                       burnin = 15, 
-                                       occDetdata = visitData$occDetdata,
-                                       spp_vis = visitData$spp_vis,
-                                       write_results = FALSE,
-                                       seed = 111,
-                                       modeltype = c("ranwalk", "halfcauchy"),
-                                       regional_codes = regionsDF,
-                                       region_aggs = list(agg1 = c('region1', 'region2','region4'))),
-               'Aggregate members [region4] not in regional_codes column names [region1, region2, region3]',fixed=TRUE)
-  
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = regionsDF,
+      region_aggs = list(agg1 = c("region1", "region2", "region4"))
+    ),
+    "Aggregate members [region4] not in regional_codes column names [region1, region2, region3]",
+    fixed = TRUE
+  )
+
   # test for regional aggregate without regional_codes
-  expect_error(results <- occDetFunc(taxa_name = 'a',
-                                     n_iterations = 50,
-                                     burnin = 15, 
-                                     occDetdata = visitData$occDetdata,
-                                     spp_vis = visitData$spp_vis,
-                                     write_results = FALSE,
-                                     seed = 111,
-                                     modeltype = c("ranwalk", "halfcauchy"),
-                                     regional_codes = NULL,
-                                     region_aggs = list(agg1 = c('region1', 'region2'))),
-               'Cannot use regional aggregates if regional_codes is not supplied')
+  expect_error(
+    results <- occDetFunc(
+      taxa_name = "a",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData$occDetdata,
+      spp_vis = visitData$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy"),
+      regional_codes = NULL,
+      region_aggs = list(agg1 = c("region1", "region2"))
+    ),
+    "Cannot use regional aggregates if regional_codes is not supplied"
+  )
   sink()
-  
-}) 
+})
 
 test_that("Test occDetFunc with empty species post nyr filter", {
-  
-  sink(file=ifelse(Sys.info()["sysname"] == "Windows",
-                   "NUL",
-                   "/dev/null"))
-  
-  expect_warning(results <- occDetFunc(taxa_name = 'aa',
-                                     n_iterations = 50,
-                                     burnin = 15, 
-                                     occDetdata = visitData_filterError$occDetdata,
-                                     spp_vis = visitData_filterError$spp_vis,
-                                     write_results = FALSE,
-                                     seed = 111,
-                                     modeltype = c("ranwalk", "halfcauchy")),
-                 'aa has insufficient data after site filtering. Either decrease nyr or change the criterion')
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
+    "NUL",
+    "/dev/null"
+  ))
+
+  expect_warning(
+    results <- occDetFunc(
+      taxa_name = "aa",
+      n_iterations = 50,
+      burnin = 15,
+      occDetdata = visitData_filterError$occDetdata,
+      spp_vis = visitData_filterError$spp_vis,
+      write_results = FALSE,
+      seed = 111,
+      modeltype = c("ranwalk", "halfcauchy")
+    ),
+    "aa has insufficient data after site filtering. Either decrease nyr or change the criterion"
+  )
   sink()
-  
 })
diff --git a/tests/testthat/testoccDetModel.r b/tests/testthat/testoccDetModel.r
index 99c39b8..7885ddc 100644
--- a/tests/testthat/testoccDetModel.r
+++ b/tests/testthat/testoccDetModel.r
@@ -1,23 +1,23 @@
 context("Test occDetModel")
 
 # Create data
-n <- 15000 #size of dataset
+n <- 15000 # size of dataset
 nyr <- 20 # number of years in data
 nSamples <- 100 # set number of dates
 nSites <- 50 # set number of sites
 set.seed(125)
 
 # Create somes dates
-first <- as.Date(strptime("2010/01/01", "%Y/%m/%d")) 
-last <- as.Date(strptime(paste(2010+(nyr-1),"/12/31", sep=''), "%Y/%m/%d")) 
-dt <- last-first 
-rDates <- first + (runif(nSamples)*dt)
+first <- as.Date(strptime("2010/01/01", "%Y/%m/%d"))
+last <- as.Date(strptime(paste(2010 + (nyr - 1), "/12/31", sep = ""), "%Y/%m/%d"))
+dt <- last - first
+rDates <- first + (runif(nSamples) * dt)
 
 # taxa are set as random letters
 taxa <- sample(letters, size = n, TRUE)
 
 # three sites are visited randomly
-site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)
+site <- sample(paste("A", 1:nSites, sep = ""), size = n, TRUE)
 
 # the date of visit is selected at random from those created earlier
 survey <- sample(rDates, size = n, TRUE)
@@ -25,25 +25,35 @@ survey <- sample(rDates, size = n, TRUE)
 df <- data.frame(taxa, site, survey)
 
 test_that("Test occDetModel", {
-  
-  sink(file=ifelse(Sys.info()["sysname"] == "Windows",
-                   "NUL",
-                   "/dev/null"))
-  suppressWarnings(results <- occDetModel(taxa = df$taxa,
-                         write_results = FALSE,
-                         site = df$site,
-                         survey = df$survey,
-                         species_list = c('a','m','g'),
-                         n_iterations = 100,
-                         burnin = 10,
-                         thinning = 2,
-                         seed = 111))
+  # skip the test if jagsUI is not installed
+  if ((!requireNamespace("jagsUI", quietly = TRUE))) {
+    skip("jagsUI software not available")
+  }
+
+  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
+    "NUL",
+    "/dev/null"
+  ))
+  suppressWarnings(results <- occDetModel(
+    taxa = df$taxa,
+    write_results = FALSE,
+    site = df$site,
+    survey = df$survey,
+    species_list = c("a", "m", "g"),
+    n_iterations = 100,
+    burnin = 10,
+    thinning = 2,
+    seed = 111
+  ))
   sink()
 
-  expect_identical(names(results), c('a','m','g'))
-  expect_identical(names(results[[1]]),
-                   c("model", "BUGSoutput", "parameters.to.save", "model.file", 
-                     "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
-                     "nsites", "nvisits", "species_observations","sparta_version"))
-  
-})
\ No newline at end of file
+  expect_identical(names(results), c("a", "m", "g"))
+  expect_identical(
+    names(results[[1]]),
+    c(
+      "model", "BUGSoutput", "parameters.to.save", "model.file",
+      "n.iter", "DIC", "SPP_NAME", "min_year", "max_year", "sites_included",
+      "nsites", "nvisits", "species_observations", "sparta_version"
+    )
+  )
+})