[Fix] llvm 19 compilation issue

.github/workflows/rhub.yaml

@@ -52,6 +52,8 @@ jobs:
 
     steps:
       - uses: r-hub/actions/checkout@v1
+        with:
+          submodules: true
       - uses: r-hub/actions/platform-info@v1
         with:
           token: ${{ secrets.RHUB_TOKEN }}

.gitmodules

@@ -1,3 +1,3 @@
 [submodule "src/libgeoda"]
 	path = src/libgeoda
-	url = https://github.com/geodacenter/libgeoda
+	url = https://github.com/geodacenter/libgeoda.git

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: rgeoda
 Type: Package
 Title: R Library for Spatial Data Analysis
-Version: 0.0.10-4
-Date: 2023-07-01
+Version: 0.0.10-7
+Date: 2024-10-21
 Authors@R: 
     c(person(given = "Xun", family = "Li", email="[email protected]", role=c("aut","cre")),
       person(given = "Luc", family = "Anselin", email="[email protected]", role="aut"))
@@ -32,9 +32,8 @@ Imports:
     sf,
     Rcpp (>= 1.0.1)
 LinkingTo:
-    Rcpp,
-    BH
-RoxygenNote: 7.1.1
+    Rcpp
+RoxygenNote: 7.3.2
 Encoding: UTF-8
 Suggests: 
     wkb,

NAMESPACE

@@ -59,6 +59,10 @@ export(min_distthreshold)
 export(min_neighbors)
 export(natural_breaks)
 export(neighbor_match_test)
+export(p_GeoDa)
+export(p_GeoDaTable)
+export(p_GeoDaWeight)
+export(p_LISA)
 export(percentile_breaks)
 export(quantile_breaks)
 export(queen_weights)

R/clustering.R

@@ -9,8 +9,8 @@
 #' @param bound_variable (optional) A data frame with selected bound variable
 #' @param min_bound (optional) A minimum bound value that applies to all
 #' clusters
-#' @param scale_method One of the scaling methods {'raw', 'standardize',
-#' 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data.
+#' @param scale_method One of the scaling methods \{'raw', 'standardize',
+#' 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data.
 #' Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the
 #' distance betwen observation i and j. Defaults to "euclidean". Options are
@@ -29,17 +29,17 @@
 #' guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
 #' guerry <- st_read(guerry_path)
 #' queen_w <- queen_weights(guerry)
 #' data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
 #' guerry_clusters <- skater(4, queen_w, data)
 #' guerry_clusters
 #' @export
 skater <- function(k, w, df, bound_variable=data.frame(), min_bound=0,
                    scale_method="standardize", distance_method="euclidean",
                    random_seed=123456789, cpu_threads=6, rdist=numeric()) {
   if (w$num_obs < 1) {
     stop("The weights is not valid.")
   }
   if (k <1 && k > w$num_obs) {
     stop("The number of clusters should be a positive integer number, which is
          less than the number of observations.")
   }
@@ -89,7 +89,7 @@
 #' @param method {"single", "complete", "average","ward"}
 #' @param bound_variable (optional) A data frame with selected bound variabl
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
-#' @param scale_method One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)
 #' @return A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
@@ -166,7 +166,7 @@
 #' @param method {"firstorder-singlelinkage", "fullorder-completelinkage", "fullorder-averagelinkage","fullorder-singlelinkage", "fullorder-wardlinkage"}
 #' @param bound_variable (optional) A data frame with selected bound variabl
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (int,optional) The seed for random number generator. Defaults to 123456789.
 #' @param cpu_threads (optional) The number of cpu threads used for parallel computation
@@ -238,7 +238,7 @@
 #' @param min_bound A minimum value that the sum value of bounding variable int each cluster should be greater than
 #' @param iterations (optional): The number of iterations of greedy algorithm. Defaults to 99.
 #' @param initial_regions (optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param cpu_threads (optional) The number of cpu threads used for parallel computation
@@ -313,7 +313,7 @@
 #' @param cooling_rate The cooling rate of a simulated annealing algorithm. Defaults to 0.85
 #' @param iterations (optional): The number of iterations of SA algorithm. Defaults to 99.
 #' @param sa_maxit (optional): The number of iterations of simulated annealing. Defaults to 1
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param initial_regions (optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
@@ -389,7 +389,7 @@
 #' @param tabu_length (optional): The length of a tabu search heuristic of tabu algorithm. Defaults to 10.
 #' @param conv_tabu (optional): The number of non-improving moves. Defaults to 10.
 #' @param iterations (optional): The number of iterations of Tabu algorithm. Defaults to 99.
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param initial_regions (optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
@@ -462,7 +462,7 @@
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
 #' @param inits (optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
 #' @param initial_regions (optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)
@@ -528,7 +528,7 @@
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
 #' @param inits (optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
 #' @param initial_regions (optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)
@@ -594,7 +594,7 @@
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
 #' @param inits (optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
 #' @param initial_regions (optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods \{'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'\} to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)