fixed various bugs at CRAN check

NAMESPACE

@@ -13,7 +13,6 @@ export(ridgeSIR)
 export(sfcb)
 export(sparseSIR)
 export(tune.ridgeSIR)
-import(adjclust)
 import(doParallel)
 import(foreach)
 import(glmnet)
@@ -22,6 +21,8 @@ importFrom(Boruta,Boruta)
 importFrom(CORElearn,attrEval)
 importFrom(Matrix,forceSymmetric)
 importFrom(RSpectra,eigs)
+importFrom(adjclust,adjClust)
+importFrom(adjclust,cutree_chac)
 importFrom(dplyr,arrange)
 importFrom(dplyr,filter)
 importFrom(dplyr,inner_join)
@@ -34,10 +35,14 @@ importFrom(mixOmics,pls)
 importFrom(parallel,detectCores)
 importFrom(purrr,map_dfc)
 importFrom(ranger,ranger)
+importFrom(rlang,.data)
+importFrom(stats,as.hclust)
 importFrom(stats,cor)
 importFrom(stats,cov)
 importFrom(stats,predict)
 importFrom(stats,quantile)
+importFrom(stats,sd)
 importFrom(tidyr,gather)
 importFrom(tidyr,spread)
+importFrom(tidyselect,starts_with)
 importFrom(utils,head)

R/data.R

@@ -4,6 +4,7 @@
 
 #' @title Dataset "Truffles"
 #' @name truffles
+#' @aliases rainfall beta
 #' 
 #' @description Yearly truffles production and corresponding monthly rainfall 
 #' information of the Perigord black truffle in the Vaucluse (France) between

R/group_methods.R

 #' @importFrom stats cor
-#' @import adjclust
+#' @importFrom adjclust adjClust cutree_chac
 
 group_adjclust <- function(dataset) {
   # Description #####
@@ -15,6 +15,7 @@ group_adjclust <- function(dataset) {
   hclust_res <- adjclust::adjClust(corr_inputs, type = "similarity")
   groups <- lapply(1:ncol(dataset), function(k) 
     adjclust::cutree_chac(hclust_res, k = k))
+  hclust_res <- as.hclust(hclust_res)
   names(groups) <- 1:ncol(dataset)
   groups <- groups[ncol(dataset):1]
   

R/helpers_sfcb.R

@@ -60,8 +60,7 @@ predict_rf <- function(summaries, target, input_groups, selection = "none",
   
   ## compute random forests
   quality_criteria <- lapply(summaries, compute_rfs, target = target, 
-                             importance = importance, repeats = repeats,
-                             seed = seed)
+                             repeats = repeats, seed = seed)
   quality_criteria <- Reduce(c, quality_criteria)
   
   ## MSE
@@ -98,7 +97,7 @@ predict_rf <- function(summaries, target, input_groups, selection = "none",
   return(out)
 }
 
-compute_rfs <- function(summaries, target, importance, repeats, seed) {
+compute_rfs <- function(summaries, target, repeats, seed) {
   if (!any(is.na(summaries)) && ncol(summaries) > 0) {
     
     curdf <- data.frame(y = target, summaries)

R/summary_methods.R

 #' @import magrittr
 #' @importFrom dplyr mutate filter arrange select summarise ungroup inner_join
+#' @importFrom tidyselect starts_with
 #' @importFrom tidyr gather spread
 #' @importFrom purrr map_dfc
 #' @importFrom mixOmics pls
+#' @importFrom stats sd as.hclust
+#' @importFrom rlang .data
 
 summary_pls <- function(dataset, target, groups) {
   # Description #####
@@ -23,7 +26,7 @@ summary_pls <- function(dataset, target, groups) {
   
   summary_data <- lapply(groups, function(cur_clust) {
     cur_data <- dataset %>% dplyr::mutate("sim" = 1:nrow(dataset)) %>% 
-      tidyr::gather("Var", "value", -sim)
+      tidyr::gather("Var", "value", - .data$sim)
     var_number <- as.numeric(gsub("Var", "", cur_data$Var))
     cur_data$clust <- cur_clust[var_number]
     
@@ -37,9 +40,9 @@ summary_pls <- function(dataset, target, groups) {
 }
 
 compute_pls_summary <- function(aclust, dataset, target) {
-  tmp <- dataset %>% dplyr::filter(clust == aclust) %>% 
-    tidyr::spread(Var, value) %>% dplyr::arrange(sim) %>% 
-    dplyr::select(starts_with("Var"))
+  tmp <- dataset %>% dplyr::filter(.data$clust == aclust) %>% 
+    tidyr::spread(.data$Var, .data$value) %>% dplyr::arrange(.data$sim) %>% 
+    dplyr::select(tidyselect::starts_with("Var"))
   if (length(unique(dataset$Var[dataset$clust == aclust])) > 1) {
     out <- mixOmics::pls(tmp, target, ncomp = 1)
     return(unlist(out$variates$X))
@@ -65,22 +68,22 @@ summary_basics <- function(dataset, groups) {
   
   summary_data <- lapply(groups, function(cur_clust) {
     cur_data <- dataset %>% dplyr::mutate("sim" = 1:nrow(dataset)) %>% 
-      tidyr::gather("Var", "value", -sim)
+      tidyr::gather("Var", "value", - .data$sim)
     var_number <- as.numeric(gsub("Var", "", cur_data$Var))
     cur_data$clust <- cur_clust[var_number]
     
-    mean_summary <- cur_data %>% dplyr::group_by(sim, clust) %>%
-      dplyr::summarise(VarMean = mean(value)) %>%
-      tidyr::spread(clust, value = VarMean, sep = "mean") %>%
+    mean_summary <- cur_data %>% dplyr::group_by(.data$sim, .data$clust) %>%
+      dplyr::summarise(VarMean = mean(.data$value)) %>%
+      tidyr::spread(.data$clust, value = .data$VarMean, sep = "mean") %>%
       dplyr::ungroup()
     
-    sd_summary <- cur_data %>% dplyr::group_by(sim, clust) %>%
-      dplyr::summarise(VarSD = sd(value)) %>%
-      tidyr::spread(clust, value = VarSD, sep = "sd") %>%
+    sd_summary <- cur_data %>% dplyr::group_by(.data$sim, .data$clust) %>%
+      dplyr::summarise(VarSD = sd(.data$value)) %>%
+      tidyr::spread(.data$clust, value = .data$VarSD, sep = "sd") %>%
       dplyr::ungroup()
     
     cur_data <- dplyr::inner_join(mean_summary, sd_summary, by = "sim") %>%
-      dplyr::select(- sim)
+      dplyr::select(- .data$sim)
     
     # remove column with NAs (for standard deviations)
     contains_na <- colSums(apply(cur_data, 2, is.na))
@@ -109,7 +112,7 @@ summary_cclustofvar <- function(dataset, groups) {
   
   summary_data <- lapply(groups, function(cur_clust) {
     cur_data <- dataset %>% dplyr::mutate("sim" = 1:nrow(dataset)) %>% 
-      tidyr::gather("Var", "value", -sim)
+      tidyr::gather("Var", "value", - .data$sim)
     var_number <- as.numeric(gsub("Var", "", cur_data$Var))
     cur_data$clust <- cur_clust[var_number]
     
@@ -123,8 +126,8 @@ summary_cclustofvar <- function(dataset, groups) {
 }
 
 compute_cov_summary <- function(aclust, dataset) {
-  tmp <- dataset %>% dplyr::filter(clust == aclust) %>% 
-    tidyr::spread(Var, value) %>% dplyr::arrange(sim) %>% 
+  tmp <- dataset %>% dplyr::filter(.data$clust == aclust) %>% 
+    tidyr::spread(.data$Var, .data$value) %>% dplyr::arrange(.data$sim) %>% 
     dplyr::select(starts_with("Var"))
   if (length(unique(dataset$Var[dataset$clust == aclust])) > 1) {
     out <- svd(tmp, 1, 1)