touch up docs

Author: philchalmers

R/SimResults.R

@@ -5,7 +5,7 @@
 #' sub-directory as individual \code{.rds} files. While users could use \code{\link{readRDS}} directly
 #' to read these files in themselves, this convenience function will read the desired rows in
 #' automatically given the returned object
-#' from the simulation. Can be used to read in 1 or more .rds files at once (if more than 1 file
+#' from the simulation. Can be used to read in 1 or more \code{.rds} files at once (if more than 1 file
 #' is read in then the result will be stored in a list).
 #'
 #' @param results object returned from \code{\link{runSimulation}} where \code{save_results = TRUE}

R/postSummarise.R

@@ -3,7 +3,7 @@
 #' When \code{runSimulation()} uses the option \code{save_results = TRUE}
 #' the R replication results from the Generate-Analyse functions are
 #' stored to the hard drive. As such, additional summarise components
-#' may be required at a later time, whereby the respective .rds files
+#' may be required at a later time, whereby the respective \code{.rds} files
 #' must be read back into R to be summarised. This function performs
 #' the reading of these files, application of a provided summarise function,
 #' and final collection of the respective results.

R/rgenerate.R

@@ -838,7 +838,7 @@ rmvnorm <- function (n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)))
 #' @param df degrees of freedom. \code{df = 0} and \code{df = Inf}
 #'   corresponds to the multivariate normal distribution
 #'
-#' @param delta the vector of noncentrality parameters of length \code{n}
+#' @param delta the vector of non-centrality parameters of length \code{n}
 #'   which specifies the either the modes (default) or non-centrality parameters
 #'
 #' @param Kshirsagar logical; triggers whether to generate data with non-centrality parameters

R/runSimulation.R

@@ -235,8 +235,8 @@
 #' @param load_seed a character object indicating which file to load from when the \code{.Random.seed}s have
 #'   be saved (after a call with \code{save_seeds = TRUE}), or an integer vector indicating the actual
 #'   \code{.Random.seed} values. E.g., \code{load_seed = 'design-row-2/seed-1'}
-#'   will load the first seed in the second row of the \code{design} input, or explicilty passing the 626 long
-#'   elements from \code{.Random.seed} (see \code{extract_error_seed()} to extract the seeds associated explicilty
+#'   will load the first seed in the second row of the \code{design} input, or explicitly passing the 626 long
+#'   elements from \code{.Random.seed} (see \code{extract_error_seed()} to extract the seeds associated explicitly
 #'   with errors during the simulation, where each column represents a unique seed).
 #'   If the input is a character vector then it is important NOT
 #'   to modify the \code{design} input object, otherwise the path may not point to the correct saved location, while

R/summary_functions.R

@@ -20,12 +20,12 @@
 #'   (average difference between sample and population), \code{'relative'} computes
 #'   the relative bias statistic (i.e., divide the bias by the value
 #'   in \code{parameter}; note that multiplying this by 100 gives the "percent bias" measure),
-#'   \code{'abs_relative'} computes the relative bias but the absoluate values of the parameters
+#'   \code{'abs_relative'} computes the relative bias but the absolute values of the parameters
 #'   are used in the denominator rather than the (potentially) signed input values,
 #'   and \code{'standardized'} computes the standardized bias estimate
 #'   (standard bias divided by the standard deviation of the sample estimates)
 #'
-#' @param abs logical; find the absoluate bias between the parameters and estimates? This effectively
+#' @param abs logical; find the absolute bias between the parameters and estimates? This effectively
 #'   just applies the \code{\link{abs}} transformation to the returned result. Default is FALSE
 #'
 #' @param percent logical; change returned result to percentage by multiplying by 100?
@@ -478,7 +478,7 @@ RE <- function(x, MSE = FALSE, percent = FALSE){
 
 #' Compute the relative absolute of multiple estimators
 #'
-#' Computes the relative absoluate bias given the bias estimates for multiple estimators.
+#' Computes the relative absolute bias given the bias estimates for multiple estimators.
 #'
 #' @param x a \code{numeric} vector of bias estimates (see \code{\link{bias}}),
 #'  where the first element will be used as the reference