Correcting a minor bug in bandwidth selection

NEWS.md

@@ -7,9 +7,14 @@ This is an important release!
 * It is now possible to do bandwidth selection with adaptive bandwidths
 * One can also set local bandwidths manually for bandwidth selection
 
-## Changes in function
+## Changes in functions
+
 * for all the function where the parameters *bw_range* and *bw_step* were used, they were replaced by a single argument *bws*. It gives more flexibility to the user. 
 
+## corrected bugs
+
+When using ESC-NKDE with bandwidths selection, negative density values could be obtained (with a small parameter of depth). It led to NAN values in LOO score. They are now treated like zeros.
+
 # spNetwork 0.4.3.7
 
 This is only a maintenance release. A bug in CRAN caused by multiprocessing in example caused the rejection of hte package.

R/bandwidth_selection_cv_sf.R

@@ -435,10 +435,10 @@ bw_cv_likelihood_calc <- function(bws = NULL,
 
   # and we can calculate now the scores
   if(zero_strat == "min_double"){
-    all_loo_scores <- ifelse(all_loo_scores == 0, .Machine$double.xmin, all_loo_scores)
+    all_loo_scores <- ifelse(all_loo_scores <= 0, .Machine$double.xmin, all_loo_scores)
     cv_scores <- colSums(log(all_loo_scores)) / nrow(all_loo_scores)
   }else{
-    binary_mat <- all_loo_scores == 0
+    binary_mat <- all_loo_scores <= 0
     all_loo_scores <- ifelse(binary_mat, 1, all_loo_scores)
     cv_scores <- colSums(log(all_loo_scores)) / colSums(binary_mat == FALSE)
   }
@@ -692,10 +692,10 @@ bw_cv_likelihood_calc.mc <- function(bws, lines, events, w, kernel_name, method,
 
   # and we can calculate now the scores
   if(zero_strat == "min_double"){
-    all_loo_scores <- ifelse(all_loo_scores == 0, .Machine$double.xmin, all_loo_scores)
+    all_loo_scores <- ifelse(all_loo_scores <= 0, .Machine$double.xmin, all_loo_scores)
     cv_scores <- colSums(log(all_loo_scores)) / nrow(all_loo_scores)
   }else{
-    binary_mat <- all_loo_scores == 0
+    binary_mat <- all_loo_scores <= 0
     all_loo_scores <- ifelse(binary_mat, 1, all_loo_scores)
     cv_scores <- colSums(log(all_loo_scores)) / colSums(binary_mat == FALSE)
   }