removing itemize from value in documentation

R/DPMGibbsN.R

@@ -37,7 +37,6 @@
 #'  is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{ a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -58,7 +57,6 @@
 #'      \item{\code{nb_mcmcit}:}{ the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{the parametric distribution of the mixture component - \code{"gaussian"}}
 #'      \item{\code{hyperG0}:}{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMGibbsN_SeqPrior.R

@@ -37,7 +37,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{ a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -57,7 +56,6 @@
 #'      \item{\code{nb_mcmcit}:}{ the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{ the parametric distribution of the mixture component - \code{"gaussian"}}
 #'      \item{\code{hyperG0}:}{ the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum, Chariff Alkhassim
 #'

R/DPMGibbsN_parallel.R

@@ -50,7 +50,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{ a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -71,7 +70,6 @@
 #'      \item{\code{nb_mcmcit}:}{ the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{the parametric distribution of the mixture component - \code{"gaussian"}}
 #'      \item{\code{hyperG0}:}{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMGibbsSkewN.R

@@ -37,7 +37,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -53,7 +52,6 @@
 #'      \item{\code{nb_mcmcit}:}{the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{the parametric distribution of the mixture component - \code{"skewnorm"}}
 #'      \item{\code{hyperG0}:}{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMGibbsSkewN_parallel.R

@@ -53,7 +53,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -69,7 +68,6 @@
 #'      \item{\code{nb_mcmcit}:}{the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{the parametric distribution of the mixture component - \code{"skewnorm"}}
 #'      \item{\code{hyperG0}:}{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMGibbsSkewT.R

@@ -60,7 +60,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}: }{ a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -77,7 +76,6 @@
 #'      \item{\code{nb_mcmcit}: }{the number of MCMC iterations}
 #'      \item{\code{clust_distrib}: }{the parametric distribution of the mixture component - \code{"skewt"}}
 #'      \item{\code{hyperG0}: }{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMGibbsSkewT_SeqPrior.R

@@ -35,7 +35,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -52,7 +51,6 @@
 #'      \item{\code{nb_mcmcit}:}{the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{the parametric distribution of the mixture component - \code{"skewt"}}
 #'      \item{\code{hyperG0}:}{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMGibbsSkewT_SeqPrior_parallel.R

@@ -46,7 +46,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -63,7 +62,6 @@
 #'      \item{\code{nb_mcmcit}:}{the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{the parametric distribution of the mixture component - \code{"skewt"}}
 #'      \item{\code{hyperG0}:}{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMGibbsSkewT_parallel.R

@@ -49,7 +49,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -66,7 +65,6 @@
 #'      \item{\code{nb_mcmcit}:}{the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{the parametric distribution of the mixture component - \code{"skewt"}}
 #'      \item{\code{hyperG0}:}{the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/DPMpost.R

@@ -57,7 +57,6 @@
 #'Only used if \code{doPlot} is \code{TRUE}.
 #'
 #'@return a object of class \code{DPMclust} with the following attributes:
-#'  \itemize{
 #'      \item{\code{mcmc_partitions}:}{ a list of length \code{N}. Each
 #'       element \code{mcmc_partitions[n]} is a vector of length
 #'       \code{n} giving the partition of the \code{n} observations.}
@@ -74,7 +73,6 @@
 #'      \item{\code{nb_mcmcit}:}{ the number of MCMC iterations}
 #'      \item{\code{clust_distrib}:}{ the parametric distribution of the mixture component}
 #'      \item{\code{hyperG0}:}{ the prior on the cluster location}
-#'  }
 #'
 #'@author Boris Hejblum
 #'

R/RcppExports.R

@@ -49,9 +49,8 @@ FmeasureC_no0 <- function(pred, ref) {
 #'@param c a matrix where each column is one MCMC partition
 #'
 #'@return a list with the following elements:
-#'\itemize{
 #'  \item{\code{Fmeas:}}{ TODO}
-#'}
+#'  \item{\code{cost:}}{ TODO}
 #'@export
 #'
 #'@examples
@@ -382,11 +381,9 @@ mmvtpdfC <- function(x, mean, varcovM, df, Log = TRUE) {
 #'@param loglik logical flag or returning the log-likelihood instead of the likelihood.
 #'Default is \code{TRUE}.
 #'@return a list:
-#'\itemize{
 #'\item{\code{"indiv"}:}{ vector of likelihood of length n;}
 #'\item{\code{"clust"}:}{ vector of likelihood of length K;}
 #'\item{\code{"total"}:}{ total (log)-likelihood;}
-#'}
 #'
 #'@author Boris Hejblum
 #'
@@ -447,11 +444,9 @@ mvnpdfC <- function(x, mean, varcovM, Log = TRUE) {
 #'@param loglik logical flag or returning the log-likelihood instead of the likelihood.
 #'Default is \code{TRUE}.
 #'@return a list:
-#'\itemize{
 #'\item{\code{"indiv"}:}{ vector of likelihood of length n;}
 #'\item{\code{"clust"}:}{ vector of likelihood of length K;}
 #'\item{\code{"total"}:}{ total (log)-likelihood;}
-#'}
 #'
 #'@author Boris Hejblum
 #'
@@ -475,11 +470,9 @@ mvsnlikC <- function(x, c, clustval, xi, psi, sigma, loglik = TRUE) {
 #'@param loglik logical flag or returning the log-likelihood instead of the likelihood.
 #'Default is \code{TRUE}.
 #'@return a list:
-#'\itemize{
 #'\item{\code{"indiv"}:}{ vector of likelihood of length n;}
 #'\item{\code{"clust"}:}{ vector of likelihood of length K;}
 #'\item{\code{"total"}:}{ total (log)-likelihood;}
-#'}
 #'
 #'@author Boris Hejblum
 #'

R/cluster_est_Fmeasure.R

@@ -10,15 +10,13 @@
 #'partition from \code{c} used to break ties when minimizing the cost function
 #'
 #'@return a \code{list}:
-#'  \itemize{
 #'      \item{\code{c_est}:}{ a vector of length \code{n}. Point estimate of the partition}
 #'      \item{\code{cost}:}{ a vector of length \code{N}. \code{cost[j]} is the cost
 #'      associated to partition \code{c[[j]]}}
 #'      \item{\code{similarity}:}{  matrix of size \code{n x n}. Similarity matrix
 #'      (see \code{\link{similarityMat}})}
 #'      \item{\code{opt_ind}:}{ the index of the optimal partition
 #'      among the MCMC iterations.}
-#'  }
 #'
 #'
 #'@author Francois Caron, Boris Hejblum

R/cluster_est_Mbinder_norm.R

@@ -31,15 +31,13 @@
 #'partition from \code{c} used to break ties when minimizing the cost function
 #'
 #'@return a \code{list}:
-#'  \itemize{
 #'      \item{\code{c_est}:}{ a vector of length \code{n}. Point estimate of the partition}
 #'      \item{\code{cost}:}{ a vector of length \code{N}. \code{cost[j]} is the cost
 #'      associated to partition \code{c[[j]]}}
 #'      \item{\code{similarity}:}{  matrix of size \code{n x n}. Similarity matrix
 #'      (see \code{\link{similarityMat}})}
 #'      \item{\code{opt_ind}:}{ the index of the optimal partition
 #'      among the MCMC iterations.}
-#'  }
 #'
 #'
 #'@author Chariff Alkhassim

R/cluster_est_pear.R

@@ -7,15 +7,13 @@
 #'
 #'
 #'@return a \code{list}:
-#'  \itemize{
 #'      \item{\code{c_est}:}{ a vector of length \code{n}. Point estimate of the partition}
 #'      \item{\code{pear}:}{ a vector of length \code{N}. \code{pear[j]} is the
 #'      posterior expected adjusted Rand index associated to partition \code{c[[j]]}}
 #'      \item{\code{similarity}:}{  matrix of size \code{n x n}. Similarity matrix
 #'      (see \code{\link{similarityMat}})}
 #'      \item{\code{opt_ind}:}{ the index of the optimal partition
 #'      among the MCMC iterations.}
-#'  }
 #'
 #'
 #'@author Chariff Alkhassim

R/evalClustLoss.R

@@ -12,10 +12,10 @@
 #'Either "F-measure" or "Binder" (see Details). Default is "F-measure".
 #'
 #'@param a only relevant if \code{lossFn} is "Binder". Penalty for wrong
-#'co-clustering in \code{c} compared to code{gs}. Defaults is 1.
+#'co-clustering in \code{c} compared to \code{gs}. Defaults is 1.
 #'
 #'@param b only relevant if \code{lossFn} is "Binder". Penalty for missed
-#'co-clustering in \code{c} compared to code{gs}. Defaults is 1.
+#'co-clustering in \code{c} compared to \code{gs}. Defaults is 1.
 #'
 #'@return the cost of the point estimate \code{c} in regard of the
 #'gold standard \code{gs} for a given loss function.

R/postProcess.DPMMclust.R

@@ -19,14 +19,12 @@
 #'@param ... further arguments passed to or from other methods
 #'
 #'@return a \code{list}:
-#'  \itemize{
 #'      \item{\code{burnin}:}{an integer passing along the \code{burnin} argument}
 #'      \item{\code{thin}:}{an integer passing along the \code{thin} argument}
 #'      \item{\code{lossFn}:}{a character string passing along the \code{lossFn} argument}
 #'      \item{\code{point_estim}:}{}
 #'      \item{\code{loss}:}{}
 #'      \item{\code{index_estim}:}{}
-#'  }
 #'
 #'@details The cost of a point estimate partition is calculated using either a pairwise
 #' coincidence loss function (Binder), or 1-Fmeasure (F-measure).

R/summary.DPMMclust.R

@@ -21,7 +21,6 @@
 #'@param ... further arguments passed to or from other methods
 #'
 #'@return a \code{list} containing the following elements:
-#'  \itemize{
 #'      \item{\code{nb_mcmcit}:}{ an integer giving the value of \code{m}, the number of retained 
 #'      sampled partitions, i.e. \code{(N - burnin)/thin}}
 #'      \item{\code{burnin}:}{ an integer passing along the \code{burnin} argument}
@@ -45,7 +44,6 @@
 #'      \item{\code{U_SS_list}:}{ a list of length \code{m} containing the containing the lists of sufficient statistics for all the mixture components,
 #'      for each sampled partition}
 #'      \item{\code{data}: a \code{d x n} matrix containing the clustered data}
-#'  }
 #'  
 #'
 #'@details The cost of a point estimate partition is calculated using either a pairwise

cran-comments.md

@@ -2,7 +2,7 @@
 
 ## Test environments  
  * local R installation, R 4.3.2 on macOS 13.5.2
- * Linux (Ubuntu 20.04), macOS (11.6) and Windows (Server 2019 10.0), R devel and release (through GitHub Actions)
+ * Linux (Ubuntu 22.04), macOS (12.7) and Windows (Server 2022 10.0), R devel and release (through GitHub Actions)
 
 ## R CMD check results  
 0 errors | 0 warnings | 0 note