update doc with describe and markdown to avoid errors

R/R6Class-BipartiteSBM_fit.R

@@ -67,16 +67,7 @@ BipartiteSBM_fit <-
       },
       #' @description function to perform optimization
       #' @param estimOptions a list of parameters controlling the inference algorithm and model selection. See details.
-      #' @details The list of parameters \code{estimOptions} essentially tunes the optimization process and the variational EM algorithm, with the following parameters
-      #'  \itemize{
-      #'  \item{"nbCores"}{integer for number of cores used. Default is 2}
-      #'  \item{"verbosity"}{integer for verbosity (0, 1). Default is 1}
-      #'  \item{"plot"}{boolean, should the ICL by dynamically plotted or not. Default is TRUE}
-      #'  \item{"exploreFactor"}{control the exploration of the number of groups}
-      #'  \item{"exploreMin"}{explore at least until exploreMin even if the exploration factor rule is achieved. Default 4. See the package blockmodels for details.}
-      #'  \item{"exploreMin"}{Stop exploration at exploreMax  even if the exploration factor rule is not achieved. Default Inf. See the package blockmodels for details.}
-      #'  \item{"fast"}{logical: should approximation be used for Bernoulli model with covariates. Default to \code{TRUE}}
-      #' }
+      #' @inherit estimateSimpleSBM details
       optimize = function(estimOptions = list()){
 
         if(private$model == 'ZIgaussian') stop("Inference not  yet  implemented for Bipartite ZI gaussian network")

R/R6Class-MultipartiteSBM_fit.R

@@ -103,15 +103,7 @@ MultipartiteSBM_fit <-
       },
       #' @description estimation of multipartiteSBM via GREMLINS
       #' @param estimOptions options for MultipartiteBM
-      #' @details The list of parameters \code{estimOptions} essentially tunes the optimization process and the variational EM algorithm, with the following parameters
-      #'  \itemize{
-      #'  \item{"nbCores"}{integer for number of cores used.  Default is 2}
-      #'  \item{"verbosity"}{integer for verbosity (0, 1). Default is 1}
-      #'  \item{"nbBlocksRange"}{List of length the number of functional groups, each element supplying the minimal and maximal number of blocks to be explored. The names of the list must be the names of the functional groups.  Default value is from 1 to 10)}
-      #'  \item{"initBM"}{Boolean. True if using simple and bipartite SBM as initialisations. Default value  = TRUE}
-      #'  \item{"maxiterVEM"}{Number of max. number of iterations in  the VEM. Default value  = 100}
-      #'  \item{"maxiterVE"}{Number of max. number of iterations in  the VE. Default value  = 100}
-      #'}
+      #' @inherit estimateSimpleSBM details
       optimize = function(estimOptions) {
 
         currentOptions <- list(

R/R6Class-MultiplexSBM_fit.R

@@ -75,14 +75,12 @@ MultiplexSBM_fit <-
       #' @description estimation of multipartiteSBM via GREMLINS
       #' @param estimOptions options for MultipartiteBM
       #' @details The list of parameters \code{estimOptions} essentially tunes the optimization process and the variational EM algorithm, with the following parameters
-      #'  \itemize{
-      #'  \item{"nbCores"}{integer for number of cores used.  Default is 2}
-      #'  \item{"verbosity"}{integer for verbosity (0, 1). Default is 1}
-      #'  \item{"nbBlocksRange"}{List of length the number of functional groups, each element supplying the minimal and maximal number of blocks to be explored. The names of the list must be the names of the functional groups.  Default value is from 1 to 10)}
-      #'  \item{"initBM"}{Boolean. True if using simple and bipartite SBM as initialisations. Default value  = TRUE}
-      #'  \item{"maxiterVEM"}{Number of max. number of iterations in  the VEM. Default value  = 100}
-      #'  \item{"maxiterVE"}{Number of max. number of iterations in  the VE. Default value  = 100}
-      #'}
+      #'  * "nbCores" integer for number of cores used.  Default is 2
+      #'  * "verbosity" integer for verbosity (0, 1). Default is 1
+      #'  * "nbBlocksRange" List of length the number of functional groups, each element supplying the minimal and maximal number of blocks to be explored. The names of the list must be the names of the functional groups.  Default value is from 1 to 10)
+      #'  * "initBM" Boolean. True if using simple and bipartite SBM as initialisations. Default value  = TRUE
+      #'  * "maxiterVEM" Number of max. number of iterations in  the VEM. Default value  = 100
+      #'  * "maxiterVE" Number of max. number of iterations in  the VE. Default value  = 100
       optimize = function(estimOptions) {
 
         if (self$dependentNetwork == FALSE) {

R/R6Class-SBM.R

@@ -200,7 +200,7 @@ predict.SBM <- function(object, covarList = object$covarList, theta_p0 = 0, ...)
 #' @param plotOptions list with parameters for 'meso' type plot and data type plot. Details are given below
 #' @param ... additional parameters for S3 compatibility. Not used
 #' @details The list of parameters \code{plotOptions}  for the mesoscopic plot is:
-#'  \itemize{
+#'  \describe{
 #'  \item{"seed": }{seed to control the layout}
 #'  \item{"title": }{character string for the title. Default value is NULL}
 #'  \item{"layout": }{Default value = NULL}
@@ -224,7 +224,7 @@ predict.SBM <- function(object, covarList = object$covarList, theta_p0 = 0, ...)
 #'  \item{"edge.curved": }{Default value is = 0.3.}
 #' }
 #' For type = 'data' or 'expected plot', the list of parameters \code{plotOptions} is
-#' \itemize{
+#' \describe{
 #'  \item{"legend": }{Boolean. Set TRUE if you want to see the legend. Default value is FALSE}
 #'  \item{"legend.title":}{Boolean. Set TRUE if you want to print the title of the legend. Default value is FALSE}
 #'  \item{"legend.position":}{Position of the legend. Possible values are 'bottom', 'top','left,'right'. Default value is 'bottom'}

R/R6Class-SimpleSBM_fit.R

@@ -68,17 +68,7 @@ SimpleSBM_fit <-
       #--------------------------------------------
       #' @description function to perform optimization
       #' @param estimOptions a list of parameters controlling the inference algorithm and model selection. See details.
-      #'
-      #' @details The list of parameters \code{estimOptions} essentially tunes the optimization process and the variational EM algorithm, with the following parameters
-      #'  \itemize{
-      #'  \item{"nbCores"}{integer for number of cores used. Default is 2}
-      #'  \item{"verbosity"}{integer for verbosity (0, 1). Default is 1}
-      #'  \item{"plot"}{boolean, should the ICL by dynamically plotted or not. Default is TRUE}
-      #'  \item{"exploreFactor"}{control the exploration of the number of groups}
-      #'  \item{"exploreMin"}{explore at least until exploreMin even if the exploration factor rule is achieved. Default 4. See the package blockmodels for details.}
-      #'  \item{"exploreMin"}{Stop exploration at exploreMax  even if the exploration factor rule is not achieved. Default Inf. See the package blockmodels for details.}
-      #'  \item{"fast"}{logical: should approximation be used for Bernoulli model with covariates. Default to \code{TRUE}}
-      #' }
+      #' @inherit estimateSimpleSBM details
       optimize = function(estimOptions = list()){
 
         if(private$model == 'ZIgaussian') stop("Inference not yet implemented for ZI gaussian network")

R/estimate.R

@@ -130,7 +130,7 @@ estimateSimpleSBM <- function(netMat,
 #' @param covariates a list of matrices with same dimension as mat describing covariates at the edge level. No covariate per Default.
 #' @param estimOptions a list of parameters controlling the inference algorithm and model selection. See details.
 #'
-#' @inheritSection estimateSimpleSBM details
+#' @inherit estimateSimpleSBM details
 #'
 #' @return  a list with the estimated parameters. See details...
 #'