updated to CRAN version 1.3.2

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: ClusterR
 Type: Package
 Title: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering
-Version: 1.3.1
-Date: 2023-05-14
+Version: 1.3.2
+Date: 2023-12-04
 Authors@R: c( person(given = "Lampros", family = "Mouselimis", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person(given = "Conrad", family = "Sanderson", role = "cph", comment = "Author of the C++ Armadillo library"), person(given = "Ryan", family = "Curtin", role = "cph", comment = "Author of the C++ Armadillo library"), person(given = "Siddharth", family = "Agrawal", role = "cph", comment = "Author of the C code of the Mini-Batch-Kmeans algorithm (https://github.com/siddharth-agrawal/Mini-Batch-K-Means)"), person(given = "Brendan", family = "Frey", email = "[email protected]", role = "cph", comment = "Author of the matlab code of the Affinity propagation algorithm (for commercial use please contact the author of the matlab code)"), person(given = "Delbert", family = "Dueck", role = "cph", comment = "Author of the matlab code of the Affinity propagation algorithm"), person(given = "Vitalie", family = "Spinu", email = "[email protected]", role = "ctb", comment = c(Github = "Github Contributor")) )  
 Maintainer: Lampros Mouselimis <[email protected]>
 BugReports: https://github.com/mlampros/ClusterR/issues

NEWS.md

@@ -1,4 +1,11 @@
 
+## Cluster 1.3.2
+
+* I've fixed the CRAN *warning: format specifies type 'double' but the argument has type 'int''* in the following files & lines by replacing the `%g` expression with `%d`: 
+  * /inst/include/affinity_propagation.h:474:37 *and* 476:58
+* I removed the `-mthreads` compilation option from the "Makevars.win" file
+
+
 ## Cluster 1.3.1
 
 * I fixed a mistake related to a potential warning of the *'Optimal_Clusters_GMM()'* function (see issue: https://github.com/mlampros/ClusterR/issues/45)

README.md

@@ -248,7 +248,7 @@ If you use the code of this repository in your paper or research please cite bot
   title = {{ClusterR}: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering},
   author = {Lampros Mouselimis},
   year = {2023},
-  note = {R package version 1.3.1},
+  note = {R package version 1.3.2},
   url = {https://CRAN.R-project.org/package=ClusterR},
 }
 ```

inst/CITATION

@@ -57,7 +57,7 @@ bibentry(
   author   = person("Delbert", "Dueck"),
   year     = "2009",
   school   = "University of Toronto",
-  url      = "http://old.psi.toronto.edu/~psi/pubs2/2009/DDueck-thesis_printable.pdf"
+  url      = "https://hdl.handle.net/1807/17755"
 )
 
 bibentry(

inst/include/affinity_propagation.h

@@ -471,9 +471,9 @@ Rcpp::List Affinity_Propagation::affinity_propagation(arma::mat &s, std::vector<
 
   if (details) {
     Rprintf("\nNumber of exemplars identified: %d  (for %d data points)\n", K, N);
-    Rprintf("Net similarity: %g\n", tmpnetsim);
+    Rprintf("Net similarity: %d\n", tmpnetsim);
     Rprintf("  Similarities of data points to exemplars: %g\n", dpsim(0,i));
-    Rprintf("  Preferences of selected exemplars: %g\n", tmpexpref);
+    Rprintf("  Preferences of selected exemplars: %d\n", tmpexpref);
     Rprintf("Number of iterations: %d\n\n", i);
   }
 

src/Makevars.win

@@ -1,3 +1,3 @@
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) -DARMA_64BIT_WORD
-PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB_OPENMP_CXXFLAGS) -mthreads
+PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB_OPENMP_CXXFLAGS)
 PKG_CPPFLAGS =	-I../inst/include/

src/init.c

@@ -8,29 +8,29 @@
 */
 
 /* .Call calls */
-extern SEXP _ClusterR_affinity_propagation(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_check_NaN_Inf(SEXP);
-extern SEXP _ClusterR_ClaraMedoids(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_ClusterMedoids(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_cost_clusters_from_dis_meds(SEXP, SEXP);
-extern SEXP _ClusterR_dissim_mat(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_dissim_MEDOIDS(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_evaluation_rcpp(SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_GMM_arma(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_GMM_arma_AIC_BIC(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_KMEANS_arma(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_KMEANS_rcpp(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_mini_batch_kmeans(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_OptClust(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_opt_clust_fK(SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_predict_medoids(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_predict_MGausDPDF(SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_Predict_mini_batch_kmeans(SEXP, SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_preferenceRange(SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_SCALE(SEXP, SEXP, SEXP);
-extern SEXP _ClusterR_silhouette_clusters(SEXP, SEXP);
-extern SEXP _ClusterR_split_rcpp_lst(SEXP);
-extern SEXP _ClusterR_validate_centroids(SEXP, SEXP, SEXP, SEXP, SEXP);
+extern SEXP _ClusterR_affinity_propagation(void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_check_NaN_Inf(void *);
+extern SEXP _ClusterR_ClaraMedoids(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_ClusterMedoids(void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_cost_clusters_from_dis_meds(void *, void *);
+extern SEXP _ClusterR_dissim_mat(void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_dissim_MEDOIDS(void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_evaluation_rcpp(void *, void *, void *);
+extern SEXP _ClusterR_GMM_arma(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_GMM_arma_AIC_BIC(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_KMEANS_arma(void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_KMEANS_rcpp(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_mini_batch_kmeans(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_opt_clust_fK(void *, void *, void *);
+extern SEXP _ClusterR_OptClust(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_predict_medoids(void *, void *, void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_predict_MGausDPDF(void *, void *, void *, void *, void *);
+extern SEXP _ClusterR_Predict_mini_batch_kmeans(void *, void *, void *, void *);
+extern SEXP _ClusterR_preferenceRange(void *, void *, void *);
+extern SEXP _ClusterR_SCALE(void *, void *, void *);
+extern SEXP _ClusterR_silhouette_clusters(void *, void *);
+extern SEXP _ClusterR_split_rcpp_lst(void *);
+extern SEXP _ClusterR_validate_centroids(void *, void *, void *, void *, void *);
 
 static const R_CallMethodDef CallEntries[] = {
     {"_ClusterR_affinity_propagation",        (DL_FUNC) &_ClusterR_affinity_propagation,         9},
@@ -46,8 +46,8 @@ static const R_CallMethodDef CallEntries[] = {
     {"_ClusterR_KMEANS_arma",                 (DL_FUNC) &_ClusterR_KMEANS_arma,                  7},
     {"_ClusterR_KMEANS_rcpp",                 (DL_FUNC) &_ClusterR_KMEANS_rcpp,                 12},
     {"_ClusterR_mini_batch_kmeans",           (DL_FUNC) &_ClusterR_mini_batch_kmeans,           13},
-    {"_ClusterR_OptClust",                    (DL_FUNC) &_ClusterR_OptClust,                    12},
     {"_ClusterR_opt_clust_fK",                (DL_FUNC) &_ClusterR_opt_clust_fK,                 3},
+    {"_ClusterR_OptClust",                    (DL_FUNC) &_ClusterR_OptClust,                    12},
     {"_ClusterR_predict_medoids",             (DL_FUNC) &_ClusterR_predict_medoids,              7},
     {"_ClusterR_predict_MGausDPDF",           (DL_FUNC) &_ClusterR_predict_MGausDPDF,            5},
     {"_ClusterR_Predict_mini_batch_kmeans",   (DL_FUNC) &_ClusterR_Predict_mini_batch_kmeans,    4},

vignettes/the_clusterR_package.Rmd

@@ -243,7 +243,7 @@ Values below the fixed threshold (here fK_threshold = 0.85) could be recommended
 <br>
 
 
-[Mini-batch-kmeans](http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf) is a variation of the classical k-means algorithm. It is particularly useful for big data sets because rather than using the whole data (as k-means does) it uses mini-batches from random data samples to optimize the objective function.
+Mini-batch-kmeans (`http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf`) is a variation of the classical k-means algorithm. It is particularly useful for big data sets because rather than using the whole data (as k-means does) it uses mini-batches from random data samples to optimize the objective function.
 <br>
 
 The parameters of the **MiniBatchKmeans** algorithm are almost the same as for the KMeans_rcpp function in the ClusterR package. The most important differences are the *batch_size* (the size of the mini batches) and the *init_fraction* (the percentage of data to use for the initialized centroids, which applies if the initializer equals to 'kmeans++' or 'quantile_init').