Temporal fix for rworkflow bug

.github/workflows/rworkflows.yml

@@ -14,8 +14,7 @@ name: rworkflows
     - RELEASE_**
 jobs:
   rworkflows:
-    permissions:
-      contents: write
+    permissions: write-all
     runs-on: ${{ matrix.config.os }}
     name: ${{ matrix.config.os }} (${{ matrix.config.r }})
     container: ${{ matrix.config.cont }}
@@ -25,12 +24,12 @@ jobs:
         config:
         - os: ubuntu-latest
           bioc: devel
-          r: auto
+          r: 4.4.0
           cont: bioconductor/bioconductor_docker:devel
           rspm: https://packagemanager.rstudio.com/cran/__linux__/latest/release
         - os: macOS-latest
           bioc: devel
-          r: auto
+          r: 4.4.0
           cont: ~
           rspm: ~
         - os: windows-latest

R/estimateDiversity.R

@@ -46,20 +46,20 @@
 #'
 #' @param ... optional arguments:
 #' \itemize{
-#'   \item{threshold}{ A numeric value in the unit interval,
+#'   \item threshold: A numeric value in the unit interval,
 #'   determining the threshold for coverage index. By default,
-#'   \code{threshold} is 0.9.}
-#'   \item{quantile}{ Arithmetic abundance classes are evenly cut up to to
+#'   \code{threshold} is 0.9.
+#'   \item quantile: Arithmetic abundance classes are evenly cut up to to
 #'   this quantile of the data. The assumption is that abundances higher than
 #'   this are not common, and they are classified in their own group.
-#'   By default, \code{quantile} is 0.5.}
-#'   \item{num_of_classes}{ The number of arithmetic abundance classes
+#'   By default, \code{quantile} is 0.5.
+#'   \item num_of_classes: The number of arithmetic abundance classes
 #'   from zero to the quantile cutoff indicated by \code{quantile}. 
-#'   By default, \code{num_of_classes} is 50.}
-#'   \item{only.tips}{ A boolean value specifying whether to remove internal
+#'   By default, \code{num_of_classes} is 50.
+#'   \item only.tips: A boolean value specifying whether to remove internal
 #'   nodes when Faith's index is calculated. When \code{only.tips=TRUE}, those
 #'   rows that are not tips of tree are removed.
-#'   (By default: \code{only.tips=FALSE})}
+#'   (By default: \code{only.tips=FALSE})
 #' }
 #'
 #' @return \code{x} with additional \code{\link{colData}} named \code{*name*}
@@ -74,11 +74,11 @@
 #'
 #' \itemize{
 #' 
-#' \item{'coverage' }{Number of species needed to cover a given fraction of
+#' \item 'coverage': Number of species needed to cover a given fraction of
 #' the ecosystem (50 percent by default). Tune this with the threshold
-#' argument.}
+#' argument.
 #' 
-#' \item{'faith' }{Faith's phylogenetic alpha diversity index measures how
+#' \item 'faith': Faith's phylogenetic alpha diversity index measures how
 #' long the taxonomic distance is between taxa that are present in the sample.
 #' Larger values represent higher diversity. Using this index requires
 #' rowTree. (Faith 1992)
@@ -87,12 +87,12 @@
 #' internal nodes, there are two options. First, you can keep those features,
 #' and prune the tree to match features so that each tip can be found from
 #' the features. Other option is to remove all features that are not tips.
-#' (See \code{only.tips} parameter)}
+#' (See \code{only.tips} parameter)
 #' 
-#' \item{'fisher' }{Fisher's alpha; as implemented in
-#' \code{\link[vegan:diversity]{vegan::fisher.alpha}}. (Fisher et al. 1943)}
+#' \item 'fisher': Fisher's alpha; as implemented in
+#' \code{\link[vegan:diversity]{vegan::fisher.alpha}}. (Fisher et al. 1943)
 #' 
-#' \item{'gini_simpson' }{Gini-Simpson diversity i.e. \eqn{1 - lambda},
+#' \item 'gini_simpson': Gini-Simpson diversity i.e. \eqn{1 - lambda},
 #' where \eqn{lambda} is the
 #' Simpson index, calculated as the sum of squared relative abundances.
 #' This corresponds to the diversity index
@@ -101,25 +101,25 @@
 #' psychology and management studies. The Gini-Simpson index (1-lambda)
 #' should not be
 #' confused with Simpson's dominance (lambda), Gini index, or
-#' inverse Simpson index (1/lambda).}
+#' inverse Simpson index (1/lambda).
 #' 
-#' \item{'inverse_simpson' }{Inverse Simpson diversity:
+#' \item 'inverse_simpson': Inverse Simpson diversity:
 #' \eqn{1/lambda} where \eqn{lambda=sum(p^2)} and p refers to relative
 #' abundances.
 #' This corresponds to the diversity index
 #' 'invsimpson' in vegan::diversity. Don't confuse this with the
-#' closely related Gini-Simpson index}
+#' closely related Gini-Simpson index
 #'
-#' \item{'log_modulo_skewness' }{The rarity index characterizes the
+#' \item 'log_modulo_skewness': The rarity index characterizes the
 #' concentration of species at low abundance. Here, we use the skewness of
 #' the frequency 
 #' distribution of arithmetic abundance classes (see Magurran & McGill 2011).
 #' These are typically right-skewed; to avoid taking log of occasional
 #' negative skews, we follow Locey & Lennon (2016) and use the log-modulo
 #' transformation that adds a value of one to each measure of skewness to
-#' allow logarithmization.}
+#' allow logarithmization.
 #'
-#' \item{'shannon' }{Shannon diversity (entropy).}
+#' \item 'shannon': Shannon diversity (entropy).
 #' 
 #' }
 #'
@@ -158,11 +158,11 @@
 #' @seealso
 #' \code{\link[scater:plotColData]{plotColData}}
 #' \itemize{
-#'   \item{\code{\link[mia:estimateRichness]{estimateRichness}}}
-#'   \item{\code{\link[mia:estimateEvenness]{estimateEvenness}}}
-#'   \item{\code{\link[mia:estimateDominance]{estimateDominance}}}
-#'   \item{\code{\link[vegan:diversity]{diversity}}}
-#'   \item{\code{\link[vegan:specpool]{estimateR}}}
+#'   \item \code{\link[mia:estimateRichness]{estimateRichness}}
+#'   \item \code{\link[mia:estimateEvenness]{estimateEvenness}}
+#'   \item \code{\link[mia:estimateDominance]{estimateDominance}}
+#'   \item \code{\link[vegan:diversity]{diversity}}
+#'   \item \code{\link[vegan:specpool]{estimateR}}
 #' }
 #'
 #' @name estimateDiversity
@@ -258,7 +258,7 @@ setMethod("estimateDiversity", signature = c(x="SummarizedExperiment"),
                              "inverse_simpson", "log_modulo_skewness", "shannon")
         index_string <- paste0("'", paste0(supported_index, collapse = "', '"), "'")
         if ( !all(index %in% supported_index) || !(length(index) > 0)) {
-            stop("'", paste0("'index' must be from the following options: '",supported_types), "'", call. = FALSE)
+            stop("'", paste0("'index' must be from the following options: '",index_string), "'", call. = FALSE)
         }
 
         if(!.is_non_empty_character(name) || length(name) != length(index)){

R/estimateDominance.R

@@ -60,11 +60,11 @@
 #'
 #' \itemize{
 #' 
-#' \item{'absolute' }{Absolute index equals to the absolute abundance of the
+#' \item 'absolute': Absolute index equals to the absolute abundance of the
 #' most dominant n species of the sample (specify the number with the argument
-#' \code{ntaxa}). Index gives positive integer values.}
+#' \code{ntaxa}). Index gives positive integer values.
 #' 
-#' \item{'dbp' }{Berger-Parker index (See Berger & Parker 1970) calculation
+#' \item 'dbp': Berger-Parker index (See Berger & Parker 1970) calculation
 #' is a special case of the 'relative' index. dbp is the relative abundance of
 #' the most
 #' abundant species of the sample. Index gives values in interval 0 to 1,
@@ -73,9 +73,9 @@
 #' \deqn{dbp = \frac{N_1}{N_{tot}}}{%
 #' dbp = N_1/N_tot} where \eqn{N_1} is the absolute abundance of the most
 #' dominant species and \eqn{N_{tot}} is the sum of absolute abundances of all
-#' species.}
+#' species.
 #' 
-#' \item{'core_abundance' }{ Core abundance index is related to core species.
+#' \item 'core_abundance': Core abundance index is related to core species.
 #' Core species are species that are most abundant in all samples, i.e., in
 #' whole data set. Core species are defined as those species that have
 #' prevalence over 50\%. It means that in order to belong to core species,
@@ -87,9 +87,9 @@
 #' \deqn{core_abundance = \frac{N_{core}}{N_{tot}}}{%
 #' core_abundance = N_core/N_tot} where \eqn{N_{core}} is the sum of absolute
 #' abundance of the core species and \eqn{N_{tot}} is the sum of absolute
-#' abundances of all species.}
+#' abundances of all species.
 #' 
-#' \item{'gini' }{ Gini index is probably best-known from socio-economic
+#' \item 'gini':  Gini index is probably best-known from socio-economic
 #' contexts (Gini 1921). In economics, it is used to measure, for example, how
 #' unevenly income is distributed among population. Here, Gini index is used
 #' similarly, but income is replaced with abundance. 
@@ -98,19 +98,19 @@
 #' that represent large portion of total abundance of microbes, the inequality
 #' is large and Gini index closer to 1. If all species has equally large
 #' abundances, the equality is perfect and Gini index equals 0. This index
-#' should not be confused with Gini-Simpson index, which quantifies diversity.}
+#' should not be confused with Gini-Simpson index, which quantifies diversity.
 #'
-#' \item{'dmn' }{McNaughton’s index is the sum of relative abundances of the two
+#' \item 'dmn': McNaughton’s index is the sum of relative abundances of the two
 #' most abundant species of the sample (McNaughton & Wolf, 1970). Index gives
 #' values in the unit interval:
 #'
 #' \deqn{dmn = (N_1 + N_2)/N_tot}
 #'
 #' where \eqn{N_1} and \eqn{N_2} are the absolute
 #' abundances of the two most dominant species and \eqn{N_{tot}} is the sum of
-#' absolute abundances of all species.}
+#' absolute abundances of all species.
 #'
-#' \item{'relative' }{ Relative index equals to the relative abundance of the
+#' \item 'relative': Relative index equals to the relative abundance of the
 #' most dominant n species of the sample (specify the number with the
 #' argument \code{ntaxa}).
 #' This index gives values in interval 0 to 1.
@@ -119,9 +119,9 @@
 #'
 #' where \eqn{N_1} is the absolute abundance of the most
 #' dominant species and \eqn{N_{tot}} is the sum of absolute abundances of all
-#' species.}
+#' species.
 #'
-#' \item{'simpson_lambda' }{ Simpson's (dominance) index or Simpson's lambda is
+#' \item 'simpson_lambda': Simpson's (dominance) index or Simpson's lambda is
 #' the sum of squared relative abundances. This index gives values in the unit interval.
 #' This value equals the probability that two randomly chosen individuals
 #' belongs to the
@@ -136,7 +136,7 @@
 #' However, this is not provided and the simpler squared sum of relative
 #' abundances is used instead as the alternative index is not in the unit
 #' interval and it is highly
-#' correlated with the simpler variant implemented here.}
+#' correlated with the simpler variant implemented here.
 #' 
 #' }
 #'
@@ -163,9 +163,9 @@
 #'
 #' @seealso
 #' \itemize{
-#'   \item{\code{\link[mia:estimateRichness]{estimateRichness}}}
-#'   \item{\code{\link[mia:estimateEvenness]{estimateEvenness}}}
-#'   \item{\code{\link[mia:estimateDiversity]{estimateDiversity}}}
+#'   \item \code{\link[mia:estimateRichness]{estimateRichness}}
+#'   \item \code{\link[mia:estimateEvenness]{estimateEvenness}}
+#'   \item \code{\link[mia:estimateDiversity]{estimateDiversity}}
 #' }
 #'
 #' @name estimateDominance

R/estimateEvenness.R

@@ -28,8 +28,8 @@
 #'
 #' @param ... optional arguments:
 #' \itemize{
-#'   \item{threshold}{ a numeric threshold. assay values below or equal
-#'     to this threshold will be set to zero.}
+#'   \item threshold:  a numeric threshold. assay values below or equal
+#'     to this threshold will be set to zero.
 #' }
 #'
 #' @return \code{x} with additional \code{\link{colData}} named \code{*name*}
@@ -42,13 +42,13 @@
 #'
 #' The available evenness indices include the following (all in lowercase):
 #' \itemize{
-#'   \item{'camargo' }{Camargo's evenness (Camargo 1992)}
-#'   \item{'simpson_evenness' }{Simpson’s evenness is calculated as inverse Simpson diversity (1/lambda) divided by
-#'   observed species richness S: (1/lambda)/S.}
-#'   \item{'pielou' }{Pielou's evenness (Pielou, 1966), also known as Shannon or Shannon-Weaver/Wiener/Weiner
-#'     evenness; H/ln(S). The Shannon-Weaver is the preferred term; see Spellerberg and Fedor (2003).}
-#'   \item{'evar' }{Smith and Wilson’s Evar index (Smith & Wilson 1996).}
-#'   \item{'bulla' }{Bulla’s index (O) (Bulla 1994).}
+#'   \item 'camargo': Camargo's evenness (Camargo 1992)
+#'   \item 'simpson_evenness': Simpson’s evenness is calculated as inverse Simpson diversity (1/lambda) divided by
+#'   observed species richness S: (1/lambda)/S.
+#'   \item 'pielou': Pielou's evenness (Pielou, 1966), also known as Shannon or Shannon-Weaver/Wiener/Weiner
+#'     evenness; H/ln(S). The Shannon-Weaver is the preferred term; see Spellerberg and Fedor (2003).
+#'   \item 'evar': Smith and Wilson’s Evar index (Smith & Wilson 1996).
+#'   \item 'bulla': Bulla’s index (O) (Bulla 1994).
 #' }
 #'   
 #' Desirable statistical evenness metrics avoid strong bias towards very

R/estimateRichness.R

@@ -55,7 +55,7 @@
 #'
 #' \itemize{
 #'   
-#'   \item{'ace' }{Abundance-based coverage estimator (ACE) is another
+#'   \item 'ace': Abundance-based coverage estimator (ACE) is another
 #'   nonparametric richness
 #'   index that uses sample coverage, defined based on the sum of the
 #'   probabilities
@@ -72,9 +72,9 @@
 #'   \code{\link[vegan:specpool]{estimateR}}.
 #'   For an exact formulation, see \code{\link[vegan:specpool]{estimateR}}.
 #'   Note that this index comes with an additional column with standard
-#'   error information.}
+#'   error information.
 #'   
-#'   \item{'chao1' }{This is a nonparametric estimator of species richness. It
+#'   \item 'chao1': This is a nonparametric estimator of species richness. It
 #'   assumes that rare species carry information about the (unknown) number
 #'   of unobserved species. We use here the bias-corrected version
 #'   (O'Hara 2005, Chiu et al. 2014) implemented in
@@ -85,21 +85,21 @@
 #'   This estimator uses only the singleton and doubleton counts, and
 #'   hence it gives more weight to the low abundance species.
 #'   Note that this index comes with an additional column with standard
-#'   error information.}
+#'   error information.
 #'   
-#'   \item{'hill' }{Effective species richness aka Hill index
+#'   \item 'hill': Effective species richness aka Hill index
 #'   (see e.g. Chao et al. 2016).
 #'   Currently only the case 1D is implemented. This corresponds to the exponent
 #'   of Shannon diversity. Intuitively, the effective richness indicates the
 #'   number of
 #'   species whose even distribution would lead to the same diversity than the
 #'   observed
-#'   community, where the species abundances are unevenly distributed.}
+#'   community, where the species abundances are unevenly distributed.
 #'   
-#'   \item{'observed' }{The _observed richness_ gives the number of species that
+#'   \item 'observed': The _observed richness_ gives the number of species that
 #'   is detected above a given \code{detection} threshold in the observed sample
 #'   (default 0). This is conceptually the simplest richness index. The
-#'   corresponding index in the \pkg{vegan} package is "richness".}
+#'   corresponding index in the \pkg{vegan} package is "richness".
 #'   
 #' }
 #'
@@ -129,7 +129,7 @@
 #' @seealso
 #' \code{\link[scater:plotColData]{plotColData}}
 #' \itemize{
-#'   \item{\code{\link[vegan:specpool]{estimateR}}}
+#'   \item \code{\link[vegan:specpool]{estimateR}}
 #' }
 #'
 #' @name estimateRichness