Fixed \itemize{XX} in code, update manual and documentation

Author: StuartWheater

R/ds.glm.R

@@ -46,7 +46,7 @@
 #' 
 #' In the \code{family} argument can be specified three types of models to fit:
 #' 
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{"gaussian"}}{: conventional linear model with normally distributed errors} 
 #'    \item{\code{"binomial"}}{: conventional unconditional logistic regression model}
 #'    \item{\code{"poisson"}}{: Poisson regression model which is the most used in survival analysis. 
@@ -190,7 +190,7 @@
 #' @return \code{family}: error family and link function.
 #' @return \code{formula}: model formula, see description of formula as an input parameter (above).
 #' @return \code{coefficients}: a matrix with 5 columns:
-#'    \itemize{
+#'    \describe{
 #'    \item{First}{: the names of all of the regression parameters (coefficients) in the model}
 #'    \item{second}{: the estimated values}
 #'    \item{third}{: corresponding standard errors of the estimated values}

R/ds.glmSLMA.R

@@ -91,7 +91,7 @@
 #' relatively widely.
 #' 
 #' The standard models include:
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{"gaussian"}}{: conventional linear model with normally distributed errors} 
 #'    \item{\code{"binomial"}}{: conventional unconditional logistic regression model}
 #'    \item{\code{"poisson"}}{: Poisson regression model which is often used in epidemiological
@@ -199,7 +199,7 @@
 #' a series of other list objects that represent inferences aggregated across studies.
 #' @return the study specific items include: 
 #' @return \code{coefficients}: a matrix with 5 columns:
-#'    \itemize{
+#'    \describe{
 #'    \item{First}{: the names of all of the regression parameters (coefficients) in the model} 
 #'    \item{second}{: the estimated values} 
 #'    \item{third}{: corresponding standard errors of the estimated values} 

R/ds.glmerSLMA.R

@@ -44,7 +44,7 @@
 #' 
 #' 
 #' In the \code{family} argument can be specified two types of models to fit:
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{"binomial"}}{: logistic regression models}
 #'    \item{\code{"poisson"}}{: poisson regression models}
 #' }
@@ -133,7 +133,7 @@
 #' The list of elements returned by \code{ds.glmerSLMA} is mentioned below: 
 #' 
 #' @return \code{coefficients}: a matrix with 5 columns:
-#'    \itemize{
+#'    \describe{
 #'    \item{First}{: the names of all of the regression parameters (coefficients) in the model} 
 #'    \item{second}{: the estimated values} 
 #'    \item{third}{: corresponding standard errors of the estimated values} 

R/ds.heatmapPlot.R

@@ -11,19 +11,19 @@
 #' minimum and maximum value. This was done to reduce the risk of potential disclosure.
 #' 
 #' In the argument \code{type} can be specified two types of graphics to display:
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{'combine'}}{: a combined heat map plot is displayed} 
 #'    \item{\code{'split'}}{: each heat map is plotted separately}
 #'     }
 #'
 #' In the argument \code{show} can be specified two options:
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{'all'}}{: the ranges of the variables are used as plot limits} 
 #'    \item{\code{'zoomed'}}{: the plot is zoomed to the region where the actual data are}
 #'     }
 #' 
 #' In the argument \code{method} can be specified 3 different heat map to be created:
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{'smallCellsRule'}}{: the heat map of the actual variables is
 #'     created but grids with low counts are replaced with grids with zero counts} 
 #'    \item{\code{'deterministic'}}{: the heat map of the scaled centroids of each 

R/ds.histogram.R

@@ -6,13 +6,13 @@
 #' the single plots.
 #' 
 #' In the argument \code{type} can be specified two types of graphics to display:
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{'combine'}}{: a histogram that merges the single plot is displayed.} 
 #'    \item{\code{'split'}}{: each histogram is plotted separately.}
 #'     }
 #'     
 #' In the argument \code{method} can be specified 3 different histograms to be created:
-#'  \itemize{
+#'  \describe{
 #'    \item{\code{'smallCellsRule'}}{: the histogram of the actual variable is
 #'           created but bins with low counts are removed.} 
 #'    \item{\code{'deterministic'}}{: the histogram of the scaled centroids of each 
@@ -48,7 +48,7 @@
 #' By default the value of noise is set to be equal to 0.25.
 #' 
 #' In the argument  \code{vertical.axis} can be specified two types of histograms:
-#' \itemize{
+#' \describe{
 #'    \item{\code{'Frequency'}}{: the histogram of the frequencies
 #'     is returned.} 
 #'    \item{\code{'Density'}}{: the histogram of the densities

R/ds.lmerSLMA.R

@@ -124,7 +124,7 @@
 #' @return \code{ds.lmerSLMA} returns a list of  elements mentioned 
 #' below separately for each study.  
 #' @return \code{coefficients}: a matrix with 5 columns:
-#'    \itemize{
+#'    \describe{
 #'    \item{First}{: the names of all of the regression parameters (coefficients) in the model} 
 #'    \item{second}{: the estimated values} 
 #'    \item{third}{: corresponding standard errors of the estimated values} 

docs/pkgdown.yml

@@ -2,5 +2,5 @@ pandoc: 2.9.2.1
 pkgdown: 2.0.9
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-06-06T15:27Z
+last_built: 2024-06-10T09:16Z