Doc fixes

NAMESPACE

@@ -88,13 +88,8 @@ export(extract_spectrum)
 export(extract_variogram)
 export(flowlength_sews)
 export(generic_sews)
-export(generic_spews)
-export(indicator_moran)
 export(indicator_plrange)
 export(indicator_psdtype)
-export(indicator_sdr)
-export(indicator_skewness)
-export(indicator_variance)
 export(indictest)
 export(kbdm_sews)
 export(label)
@@ -103,7 +98,6 @@ export(lsw_sews)
 export(pLSW)
 export(pair_counts)
 export(patchdistr_sews)
-export(patchdistr_spews)
 export(patchsizes)
 export(percolation)
 export(pl_fit)
@@ -125,7 +119,6 @@ export(raw_structvar)
 export(raw_variogram_metrics)
 export(rspectrum)
 export(spectral_sews)
-export(spectral_spews)
 export(tpl_fit)
 export(variogram_sews)
 export(xmin_estim)

NEWS

@@ -7,18 +7,20 @@ This release has received external contributions from K. Siteur
 Improvements: 
 
   * The type of neighborhood can now be specified in all patch-related functions, 
-      including high-level functions (e.g. patchdistr_sews). Neighborhood can also be 
-      specified using "von_neumann" or "moore", on top of a 3x3 matrix
+      including high-level functions (e.g. `patchdistr_sews`). Neighborhood can also be 
+      specified using the strings "von_neumann" or "moore", on top of a 3x3 matrix
 
 New indicators: 
 
   * Clustering of pairs (e.g. Schneider et al. 2016)
 
   * Indicators related to the LSW distribution (Siteur et al. 2023)
 
-Bug fixes: 
+Bug fixes and misc changes: 
 
   * Documentation improvements and minor fixes 
+
+  * Old defunct functions have been removed
 
 spatialwarnings v3.0.3 (Release date: 2022-03-21)
 =================================================

R/RcppExports.R

@@ -36,7 +36,7 @@ label_cpp <- function(mat, nbmask, wrap) {
 #'   \code{NaN} is returned. This function assumes a 4-way neighborhood, and does 
 #'   not wrap around at the sides of the matrix.
 #' 
-#' @seealso \code{\link{indicator_moran}}, \code{\link{generic_sews}} 
+#' @seealso \code{\link{generic_sews}} 
 #' 
 #' @examples
 #' 

R/clustering.R

@@ -61,6 +61,8 @@
 #' print(clust)
 #' 
 #' # Compute clustering along the gradient for the serengeti dataset
+#' \donttest{
+#' 
 #' data(forestgap)
 #' clust_indic <- compute_indicator(serengeti, raw_clustering,
 #'                                  wrap = TRUE, use_8_nb = FALSE)
@@ -80,6 +82,8 @@
 #' # ... or the total count
 #' pair_counts(serengeti[[5]], prop = FALSE)
 #' 
+#' }
+#' 
 #'@export
 raw_clustering <- function(mat,
                            wrap = TRUE,