Merge pull request datashield#530 from datashield/v6.3.1-dev

V6.3.1 dev

Author: StuartWheater

.Rbuildignore

@@ -3,17 +3,20 @@
 ^\.Rproj\.user$
 ^LICENSE.md$
 ^azure-pipelines\.yml$
+^opal_azure-pipelines\.yml$
+^armadillo_azure-pipelines\.yml$
 ^azure-pipelines_site\.pp$
 ^azure-pipelines_site-dsdanger\.pp$
 ^azure-pipelines_check\.Rout$
 ^azure-pipelines_test\.Rout$
+^tests/docker/armadillo/standard/log/.gitkeep$
 ^checkDocumentationUpdated\.sh$
 ^docker-compose_armadillo\.yml$
 ^docker-compose_opal\.yml$
 ^docker-compose\.yml$
 ^R/secure.global.ranking.md$
 ^_pkgdown\.yml$
 ^docs$
-^dsBase_6.3.0.tar.gz$
-^dsBase_6.3.0-permissive.tar.gz$
+^dsBase_6.3.1.tar.gz$
+^dsBase_6.3.1-permissive.tar.gz$
 ^dsDanger_6.3.0.tar.gz$

.gitignore

@@ -10,3 +10,5 @@
 inst/tests/test-template.R
 azure-pipelines.Rout
 tests/testthat/connection_to_datasets/local_settings.csv
+tests/docker/armadillo/standard/logs/
+tests/docker/armadillo/standard/data/

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: dsBaseClient
 Title: DataSHIELD Client Functions
-Version: 6.3.0
+Version: 6.3.1.9000
 Author: DataSHIELD Developers <datashield@liverpool.ac.uk>
 Maintainer: DataSHIELD Developers <datashield@liverpool.ac.uk>
 Description: DataSHIELD client functions for the client side.
@@ -18,5 +18,7 @@ Imports:
     panelaggregation,
     methods,
     dplyr
-RoxygenNote: 7.2.3
+Suggests:
+    testthat
+RoxygenNote: 7.3.1
 Encoding: UTF-8

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(),