v0.3.2 final prep for CRAN release

NEWS.md

@@ -8,7 +8,7 @@ New instructional helpers:
 * `random_normal_factory()`
 
 
-## Bugfixes
+## Bug fixes
 
 Console output for `ttestGC()` and similar functions now shows correctly in
 R Markdown notebooks.
@@ -46,7 +46,7 @@ Addition of vignette "Teaching with Tigerstats."
 ## Major changes
 
 * Addition of `qnormGC()`
-* Addition of check argument to `lmGC()`.  Diagnostics are no longer done with the diag argument; instead one calls the `plot()` function.
+* Addition of check argument to `lmGC()`.  Diagnostics are no longer done with the `diag` argument; instead one calls the `plot()` function.
 * Addition of `polyfitGC()`
 * Addition of `henderson` data frame; the `seals` data frame has been re-named to `sealsO2` to avoid name conflict with `seals` in the ggplot2 package.
 * Addition of function `helpGC()`, a convenience function to view package vignettes in the R Studio Viewer pane.

R/BinomSkew.R

@@ -1,7 +1,8 @@
 #' @title Skewness in the Binomial Family of Distributions
 
-#' @description An app to investigate how skewness in a binomial distribtution vanishes
-#' when np is large enough.  Sample size is set at n = 50, but the user can vary p with a slider.
+#' @description An app to investigate how skew-ness in a binomial distribution vanishes
+#' when np is large enough.  Sample size is set at n = 50, but the user can vary p with a 
+#' slider.
 #' 
 #' @rdname BinomSkew
 #' @usage BinomSkew()

R/CIProp.R

@@ -12,7 +12,7 @@
 #' @return Graphical and numerical output
 #' @export
 #' @author Rebekah Robinson \email{rebekah_robinson@@georgetowncollege.edu}
-#' @note Uses manipulate from RStudio
+#' @note Uses manipulate from R Studio
 #' @examples
 #' \dontrun{
 #' if (require(manipulate)) CIProp()

R/ChisqSimSlow.R

@@ -12,12 +12,12 @@
 #' fixed effects (see below for explanation), x should be the
 #' variable that is considered the predictor variable.
 #' @param data A data frame from which x and y are drawn.
-#' @param effects When effects="fixed", the resampling is performed under
-#' the conditon that the row sums in the resampled two-way table (with x
-#' for rows) are the same as the row sums in the twoway table based on the
+#' @param effects When effects="fixed", the re-sampling is performed under
+#' the condition that the row sums in the re-sampled two-way table (with x
+#' for rows) are the same as the row sums in the two-way table based on the
 #' original data.  When effects="random", then both row and column sums
-#' in the resampled table may vary: only the sum of the counts is
-#' constant.  (Note:  in the resampling procedure for chisq.test
+#' in the re-sampled table may vary: only the sum of the counts is
+#' constant.  (Note:  in the re-sampling procedure for chisq.test
 #' in the stats package of R, both row and column sums are
 #' required to equal the corresponding sums for the original data.)
 #' @return Graphical and numerical output

R/DtrellHist.R

@@ -1,12 +1,13 @@
 #' @title Dynamic Trellising (Histogram)
 
-#' @description A manipulative app that facilitates exploration of the distribution of a single numerical
-#' variable, conditoned upon the values of either a numerical variable or a factor.
+#' @description A manipulative app that facilitates exploration of the distribution 
+#' of a single numerical variable, conditioned upon the values of either a 
+#' numerical variable or a factor.
 #' 
 #' @rdname DtrellHist
 #' @usage DtrellHist(form,data)
 #' @param form a formula of the form \code{~var|cond}.  \code{var} must be numeric; \code{cond} may be either numeric or factor.
-#' @param data A data frame fromm \code{var} and \code{cond} are drawn.
+#' @param data A data frame from \code{var} and \code{cond} are drawn.
 #' @return Graphical output.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}

R/DtrellScat.R

@@ -1,7 +1,7 @@
 #' @title Dynamic Trellising (Scatterplot)
 
-#' @description An app to facilitate exploration of the relationship between two numerical variables, conditonal upon
-#' the values of a third variable.
+#' @description An app to facilitate exploration of the relationship between two numerical 
+#' variables, conditional upon the values of a third variable.
 #' 
 #' @rdname DtrellScat
 #' @usage DtrellScat(form,data)

R/EmpRuleGC.R

@@ -11,7 +11,7 @@
 #' @return Returns no value.  Used for the plotting side-effects.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
-#' @note  Uses \code{manipulate} in RStudio
+#' @note  Uses \code{manipulate} in R Studio
 #' @examples
 #' \dontrun{
 #' if(require(manipulate)) EmpRuleGC(mean=70,sd=3,xlab="Height (inches)")

R/FindRegLine.R

@@ -1,17 +1,22 @@
 #' @title Find the Regression Line
 
-#' @description The regression minimizes the residual sum of squares (RSS).  In this game, the player chooses slope and y-intercept
-#' of a line so as to approximate the regression line.  The moveable line is set initially as a horizontal line with height equal to
-#' the mean of the y-coordinates of the scatterplot, so initially the residual sum of squares equals the total sum of squares (TSS).
-#' The player's score is the sum of the number of turns taken and the difference between the current RSS
-#' and the regression line's RSS (as a percentage of  TSS-RSS for regression line).  The aim is to lower one's score.
+#' @description The regression minimizes the residual sum of squares (RSS).  In this 
+#' game, the player chooses slope and y-intercept
+#' of a line so as to approximate the regression line.  The move-able 
+#' line is set initially as a horizontal line with height equal to
+#' the mean of the y-coordinates of the scatter plot, so initially 
+#' the residual sum of squares equals the total sum of squares (TSS).
+#' The player's score is the sum of the number of turns taken and the 
+#' difference between the current RSS
+#' and the regression line's RSS (as a percentage of  TSS-RSS for regression 
+#' line).  The aim is to lower one's score.
 #' 
 #' @rdname FindRegLine
 #' @usage FindRegLine  
 #' @return Graphical and numerical output.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
-#' @note Requires package \code{manipulate}, available only in RStudio.
+#' @note Requires package \code{manipulate}, available only in R Studio.
 #' @examples
 #' \dontrun{
 #' if (require(manipulate)) FindRegLine()

R/Points2Watch.R

@@ -8,7 +8,7 @@
 #' @return Graphical output.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
-#' @note Requires package \code{manipulate}, available only in RStudio.
+#' @note Requires package \code{manipulate}, available only in R Studio.
 #' Uses \code{mvrnorm} from package \code{MASS}.
 #' @examples
 #' \dontrun{

R/SampDist2Means.R

@@ -11,7 +11,7 @@
 #' @return Graphical and numerical output.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
-#' @note  Uses \code{manipulate} in RStudio.  Also requires package \code{lattice}.
+#' @note  Uses \code{manipulate} in R Studio.  Also requires package \code{lattice}.
 #' @examples
 #' \dontrun{
 #' data(imagpop)

R/SampDistMean.R

@@ -10,7 +10,7 @@
 #' @return Graphical and numerical output.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
-#' @note  Uses \code{manipulate} in RStudio.
+#' @note  Uses \code{manipulate} in R Studio.
 #' @examples
 #' \dontrun{
 #' data(imagpop)

R/ShallowReg.R

@@ -1,19 +1,24 @@
 #' @title Regression Line Too Shallow?
 
-#' @description The regession line is not as steep as the SD Line (line through point of averages, with slope = sd(y)/sd(x)).  The difference
-#' is especially noticeable when the scatterplot is the result of a sample from a bivariate normal distribution.  This app explains
-#' why we use the regression line to predict y from x, even though the SD line appears to be a better linear summary of the
-#' scatterplot.  Can be used as a starting-point for a discussion of "regression to the mean."
+#' @description The regression line is not as steep as the SD Line (line 
+#' through point of averages, with slope = sd(y)/sd(x)).  The difference
+#' is especially noticeable when the scatter plot is the result of a sample 
+#' from a bivariate normal distribution.  This app explains
+#' why we use the regression line to predict y from x, even though the 
+#' SD line appears to be a better linear summary of the
+#' scatter plot.  Can be used as a starting-point for a discussion of 
+#' "regression to the mean."
 #' 
 #' @rdname ShallowReg
 #' @usage ShallowReg(n=900,rho=0.5)
-#' @param n Number of points in the scatterplot.
-#' @param rho Target correlation for the scatterplot.  Points are selected from a standardized bivariate normal distribtuion, with
+#' @param n Number of points in the scatter plot.
+#' @param rho Target correlation for the scatter plot.  Points are selected from 
+#' a standardized bivariate normal distribution, with
 #' correlation rho.  
 #' @return Graphical output.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
-#' @note Uses \code{manipulate}, available only in RStudio, and \code{mvrnorm} from package \code{MASS}.
+#' @note Uses \code{manipulate}, available only in R Studio, and \code{mvrnorm} from package \code{MASS}.
 #' @examples
 #' \dontrun{
 #' if (require(manipulate)) ShallowReg()

R/Skewer.R

@@ -1,4 +1,4 @@
-#' @title SkewR
+#' @title Skewness and Box Plots
 
 #' @description An app to illustrate the effect of skewness on the shape of a boxplot.
 #' 

R/Type12Errors.R

@@ -2,7 +2,7 @@
 
 #' @description An app to explore the concepts of Type I and Type II errors, and the concept of
 #' power.  We take samples from a population that is imagined to be normal, and perform the t-procedures
-#' for one mean.  The Null Hypotheis is H0:  mu=170.  A slider allows us to vary the true mean mu.
+#' for one mean.  The Null Hypothesis is H0:  mu=170.  A slider allows us to vary the true mean mu.
 #' 
 #' @rdname Type12Errors
 #' @usage Type12Errors() 

R/VaryCorrelation.R

@@ -9,7 +9,7 @@
 #' @return Graphical output.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
-#' @note  Uses \code{manipulate} in RStudio, and \code{mvrnorm} from package \code{MASS}.
+#' @note  Uses \code{manipulate} in R Studio, and \code{mvrnorm} from package \code{MASS}.
 #' @examples
 #' \dontrun{
 #' if(require(manipulate)) VaryCorrelation(n=500)

R/barchartGC.R

@@ -1,9 +1,9 @@
-#' @title Easy Barcharts
+#' @title Easy Bar Charts
 
 #' @description Wrapper for \code{\link{barchart}} in package \code{lattice}.  Creates a 
-#' barchart from raw data using formula-data syntax similar to that of \code{\link{xtabs}},
+#' bar chart from raw data using formula-data syntax similar to that of \code{\link{xtabs}},
 #' or from a table.  Defaults to a "standard"
-#' barchart in which the bars are vertical and unstacked.  Supports percentage barcharts.
+#' bar chart in which the bars are vertical and un-stacked.  Supports percentage bar charts.
 #' 
 #' @rdname barchartGC
 #' @usage barchartGC(x,data=parent.frame(),type="frequency",flat=FALSE,auto.key=TRUE,
@@ -13,33 +13,33 @@
 #' @param data Usually a data frame that supplies the variables in \code{x}.  Variables not in the data
 #' argument are searched for in the parent environment.
 #' @param type Possible values are "frequency" and "percent".
-#' @param flat If set to TRUE, will produce barchart that resembles the layout of \code{xtabs}
+#' @param flat If set to TRUE, will produce bar chart that resembles the layout of \code{xtabs}
 #' @param auto.key Provides a simple key
-#' @param horizontal Determines orientation of the bars (overrriden by flat)
-#' @param stack Determines whether bars for tallies are stacked on eac other or placed
-#' next to one another (overrriden by flat)
+#' @param horizontal Determines orientation of the bars (overridden by flat)
+#' @param stack Determines whether bars for tallies are stacked on each other or placed
+#' next to one another (overriden by flat)
 #' @param ... other arguments passed to \code{barchart}:  these include main, sub, and
 #' xlab, which are likely to be familiar to students from other \code{lattice} graphical
 #' functions.  An error is possible if other arguments
 #' pertaining to legends are passed (hopefully anyone interested in such will have moved on
 #' to \code{barchart}).
-#' @return A trellis object describing the barchart.
+#' @return A trellis object describing the bar chart.
 #' @export
 #' @author Homer White \email{hwhite0@@georgetowncollege.edu}
 #' @examples
-#' #barchart of counts for one factor variable:
+#' #bar chart of counts for one factor variable:
 #' barchartGC(~sex,data=m111survey)
 #' 
-#' #barchart with percentages and title:
+#' #bar chart with percentages and title:
 #' barchartGC(~sex,data=m111survey,
 #'    type="percent",
 #'    main="Distribution of Sex")
 #' 
-#' #barchart of counts, to study the relationship between
+#' #bar chart of counts, to study the relationship between
 #' #two factor variables:
 #' barchartGC(~sex+seat,data=m111survey)
 #' 
-#' #percentage barchart, two factor variables:
+#' #percentage bar chart, two factor variables:
 #' barchartGC(~sex+seat,data=m111survey,type="percent")
 #' 
 #' #From tabulated data:
@@ -50,10 +50,10 @@
 #' dieTosses <- c(one=8,two=18,three=11,four=7,five=9,six=7)
 #' barchartGC(dieTosses,main="60 Rolls of a Die")
 #' 
-#' # a "flat" barchart, pictorial version of xtabs() 
+#' # a "flat" bar chart, pictorial version of xtabs() 
 #' barchartGC(~sex+seat,data=m111survey,flat=TRUE,ylab="Sex")
 #' 
-#' # a "flat" barchart, pictorial version of xtabs() 
+#' # a "flat" bar chart, pictorial version of xtabs() 
 #' barchartGC(~sex+seat,data=m111survey,type="percent",flat=TRUE,ylab="Sex")
 barchartGC <-
   function(x,data=parent.frame(),
@@ -77,7 +77,7 @@ barchartGC <-
       type <- "percent"
     }
 
-    # handle arugments when user wants a barchart that looks like xtabs()
+    # handle arugments when user wants a bar chart that looks like xtabs()
     if (flat==TRUE) {
       stack <- TRUE
       horizontal <- TRUE

R/chisqtestGC.R

@@ -8,14 +8,14 @@
 #'              simulate.p.value = FALSE, B = 2000, verbose = TRUE)
 #' @param x Could be a formula.  If so, either ~var (for goodness of fit) or ~var1+var2 (for test for association).
 #' Otherwise either a table, matrix or vector of summary data.
-#' @param data dataframe supplying variables for formula x.  If variables in x ar enot found in the data,
+#' @param data dataframe supplying variables for formula x.  If variables in x are not found in the data,
 #' then they will be searched for in the parent environment.
 #' @param p For goodness of fit, a vector of probabilities.  This will be automatically scaled so as to sum
 #' to 1.  Negative elements result in an error message.
 #' @param graph produce relevant graph for P-value (chi-square curve or histogram of simulation results).
 #' @param simulate.p.value If FALSE, use a chi-square distribution to estimate the P-value.  Other possible
 #' values are "random" and "fixed" and TRUE.  Random effects are suitable for resampling when the data are a random
-#' sample from a poulation.  Fixed effects assume that the values of the explanatory variable (row variable for table,
+#' sample from a population.  Fixed effects assume that the values of the explanatory variable (row variable for table,
 #' var1 in formula ~var1+var2) remain fixed in resampling, and values of response variable are random with null
 #' distribution estimated from the data.  When set to TRUE, we implement an equivalent to R's routine.  
 #' In our view procedure is

R/colPerc.R

@@ -1,6 +1,6 @@
 #' @title Column Percents
 
-#' @description Computes column percentages for a given twoway table.
+#' @description Computes column percentages for a given two-way table.
 #' 
 #' @rdname colPerc
 #' @usage colPerc(tab)