Merge pull request datashield#528 from StuartWheater/v6.3.1-dev

Updates to fix R 4.4 manual processing

Author: StuartWheater

.gitignore

@@ -10,6 +10,5 @@
 inst/tests/test-template.R
 azure-pipelines.Rout
 tests/testthat/connection_to_datasets/local_settings.csv
-tests/docker/armadillo/standard/config/
 tests/docker/armadillo/standard/logs/
 tests/docker/armadillo/standard/data/

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} 

R/ds.names.R

@@ -6,7 +6,7 @@
 #' for example, it only works to extract names that already exist,
 #' not to create new names for objects. The function is restricted to objects of
 #' type list, but this includes objects that have a primary class other than list but which
-#' return TRUE to the native R function {is.list}. As an example this includes
+#' return TRUE to the native R function \code{is.list}. As an example this includes
 #' the multi-component object created by fitting a generalized linear model
 #' using ds.glmSLMA. The resultant object saved on each server separately
 #' is formally of class "glm" and "ls" but responds TRUE to is.list(),

R/ds.sample.R

@@ -4,8 +4,8 @@
 #' on the serverside
 #' or - as a special case - randomly permutes a vector, dataframe or matrix.
 #' @details Clientside function ds.sample calls serverside
-#' assign function sampleDS. Based on the native R function {sample()} but deals
-#' slightly differently with data.frames and matrices. Specifically the {sample()}
+#' assign function sampleDS. Based on the native R function \code{sample()} but deals
+#' slightly differently with data.frames and matrices. Specifically the \code{sample()}
 #' function in R identifies the length of an object and then samples n components
 #' of that length. But length(data.frame) in native R returns the number of columns
 #' not the number of rows. So if you have a data.frame with 71 rows and 10 columns,
@@ -63,11 +63,11 @@
 #' that is a randomly permuted sample of the vector 1:923, or (if [replace]
 #' = FALSE, a full random permutation of that same vector. For further details
 #' of using ds.sample with x set as an integer/numeric please see help for
-#' the {sample} function in native R. But if x is set as a character string
+#' the \code{sample} function in native R. But if x is set as a character string
 #' denoting a vector, matrix or data.frame on the serverside, please note
-#' that although {ds.sample} effectively calls {sample} on the serverside
-#' it behaves somewhat differently to {sample} - for the reasons identified
-#' at the top of 'details' and so help for {sample} should be used as a guide
+#' that although \code{ds.sample} effectively calls \code{sample} on the serverside
+#' it behaves somewhat differently to \code{sample} - for the reasons identified
+#' at the top of 'details' and so help for \code{sample} should be used as a guide
 #' only.
 #' @param size a numeric/integer scalar indicating the size of the sample to
 #' be drawn. If the [x] argument is a vector, matrix or data.frame on the
@@ -102,18 +102,18 @@
 #' @param replace a Boolean indicator (TRUE or FALSE) specifying whether the
 #' sample should be drawn with or without replacement. Default is FALSE so
 #' the sample is drawn without replacement. For further details see
-#' help for {sample} in native R.
+#' help for \code{sample} in native R.
 #' @param prob a character string containing the name of a numeric vector
 #' of probability weights on the serverside that is associated with each of the
 #' elements of the vector to be sampled enabling the drawing of a sample
 #' with some elements given higher probability of being drawn than others.
-#' For further details see help for {sample} in native R.
+#' For further details see help for \code{sample} in native R.
 #' @param newobj This a character string providing a name for the output
 #' data.frame which defaults to 'newobj.sample' if no name is specified.
 #' @param datasources specifies the particular opal object(s) to use. If the <datasources>
 #' argument is not specified the default set of opals will be used. The default opals
 #' are called default.opals and the default can be set using the function
-#' {ds.setDefaultOpals}. If the <datasources> is to be specified, it should be set without
+#' \code{ds.setDefaultOpals}. If the <datasources> is to be specified, it should be set without
 #' inverted commas: e.g. datasources=opals.em or datasources=default.opals. If you wish to
 #' apply the function solely to e.g. the second opal server in a set of three,
 #' the argument can be specified as: e.g. datasources=opals.em[2].

R/ds.table.R

@@ -36,7 +36,7 @@
 #' table returned to the clientside, it means that the true count
 #' in that same cell is held as the 13th element of the true count
 #' vector saved on the serverside. This means that a data analyst
-#' can still make use of the counts from a call to the {ds.table}
+#' can still make use of the counts from a call to the \code{ds.table}
 #' function to drive their ongoing analysis even when one or
 #' more non-zero cell counts fall below the specified threshold
 #' for potential disclosure risk. 
@@ -55,7 +55,7 @@
 #' 
 #' In creating a 3-dimensional table the
 #' <stvar> ('separate tables') argument identifies the variable that
-#' indexes the set of two dimensional tables in the output {ds.table}.
+#' indexes the set of two dimensional tables in the output \code{ds.table}.
 #' 
 #' As a minor technicality, it should be noted that
 #' if a 1-dimensional table is required, one only need specify a value
@@ -67,7 +67,7 @@
 #' for one dimensional tables are actually two dimensional: with
 #' rows defined by <rvar> and with one column for each of the studies.
 #' 
-#' The output list generated by {ds.table} contains tables based on counts
+#' The output list generated by \code{ds.table} contains tables based on counts
 #' named "table.name_counts" and other tables reporting corresponding
 #' column proportions ("table.name_col.props") or row proportions
 #' ("table.name_row.props"). In one dimensional tables in the output the
@@ -102,14 +102,14 @@
 #' @param report.chisq.tests if TRUE, chi-squared tests
 #' are applied to every 2 dimensional table in the output and reported
 #' as "chisq.test_table.name". Default = FALSE.
-#' @param exclude this argument is passed through to the {table} function in
-#' native R which is called by {tableDS}. The help for {table} in native R
+#' @param exclude this argument is passed through to the \code{table} function in
+#' native R which is called by \code{tableDS}. The help for \code{table} in native R
 #' indicates that 'exclude' specifies any levels that should be deleted for
 #' all factors in rvar, cvar or stvar. If the <exclude> argument
 #' does not include NA and if the <useNA> argument is not specified,
-#' it implies <useNA> = "always" in DataSHIELD. If you read the help for {table} in native R
+#' it implies <useNA> = "always" in DataSHIELD. If you read the help for \code{table} in native R
 #' including the 'details' and the 'examples' (particularly 'd.patho') you
-#' will see that the response of {table} to different combinations of the
+#' will see that the response of \code{table} to different combinations of the
 #' <exclude> and <useNA> arguments can be non-intuitive. This is particularly
 #' so if there is more than one type of missing (e.g. missing by observation
 #' as well as missing because of an NaN response to a mathematical
@@ -118,24 +118,24 @@
 #' you cannot interpret the output that has been approached
 #' you might try: (1) making sure that the variable producing the strange results
 #' is of class factor rather than integer or numeric - although integers and
-#' numerics are coerced to factors by {ds.table} they can occasionally behave less
+#' numerics are coerced to factors by \code{ds.table} they can occasionally behave less
 #' well when the NA setting is complex; (2) specify both an <exclude> argument
 #' e.g. exclude = c("NaN","3") and a <useNA> argument e.g. useNA= "no";
 #' (3) if you are excluding multiple levels e.g exclude = c("NA","3")
 #' then you can reduce this to one e.g. exclude = c("NA") and then remove
 #' the 3s by deleting rows of data, or converting the 3s to a different value.
-#' @param useNA this argument is passed through to the {table} function in
-#' native R which is called by {tableDS}. In DataSHIELD, this argument can take 
+#' @param useNA this argument is passed through to the \code{table} function in
+#' native R which is called by \code{tableDS}. In DataSHIELD, this argument can take 
 #' two values: "no" or "always" which indicate whether to include NA values in the table.
 #' For further information, please see the help for the <exclude> argument (above)
-#' and/or the help for the {table} function in native R. Default value is set to "always".
+#' and/or the help for the \code{table} function in native R. Default value is set to "always".
 #' @param suppress.chisq.warnings if set to TRUE, the default warnings are
-#' suppressed that would otherwise be produced by the {table} function in
+#' suppressed that would otherwise be produced by the \code{table} function in
 #' native R whenever an expected cell count in one or more cells is less than 5.
 #' Default is FALSE. Further details can be found under 'details' and the
 #' help provided for the <report.chisq.tests> argument (above).
 #' @param table.assign is a Boolean argument set by default to FALSE. If it is
-#' FALSE the {ds.table} function acts as a standard aggregate function -
+#' FALSE the \code{ds.table} function acts as a standard aggregate function -
 #' it returns the table that is specified in its call to the clientside
 #' where it can be visualised and worked with by the analyst. But if
 #' <table.assign> is TRUE, the same table object is also written to
@@ -161,7 +161,7 @@
 #' a positive integer represented as a character string: e.g. "173". This
 #' the has the effect of the standard value of 'nfilter.tab' (often 1, 3, 5 or 10
 #' depending what value the data custodian has selected for this particular
-#' data set), to this new value (here, 173). CRUCIALLY, the {ds.table} function
+#' data set), to this new value (here, 173). CRUCIALLY, the \code{ds.table} function
 #' only allows the standard value to be INCREASED. So if the standard value has
 #' been set as 5 (as one of the R options set in the serverside connection), "6" and
 #' "4981" would be allowable values for the <force.nfilter> argument but "4" or
@@ -174,9 +174,9 @@
 #' there is an error for some other reason). 
 #' 
 #' The clientside output from
-#' {ds.table} includes error messages that identify when the creation of a
+#' \code{ds.table} includes error messages that identify when the creation of a
 #' table from a particular study has failed and why. If table.assign=TRUE,
-#' {ds.table} also writes the requested table as an object named by
+#' \code{ds.table} also writes the requested table as an object named by
 #' the <newobj> argument or set to 'newObj' by default. 
 #' 
 #' Further information