geocmeans 0.2.0

New Features

Added support to use raster data for clustering (see vignette rasters)
Added a S3 method to predict the membership matrix of a new set of observations (predict.FCMres)
Added a shiny app (function: sp_clust_explorer) for result exploration
The results of the functions CMeans, GCMeans, SFCMeans, SGFCMeans are now objects of class FCMres and the generic methods predict, summary, plot, is and print can be used on them. FMCres object can easily be created by hand with results from other classifier if needed, see the new vignette FMCres.
Added some clustering quality indices : Negentropy Increment index, Generalized Dunn’s index (43 and 53), David-Bouldin index, Calinski-Harabasz index
Added a function to perform clustering validation by bootstrap (see function bstp_group_validation)
Added a function to reorder the results of a classification to match the most similar groups in a second classification (groups_matching)
Added functions to evaluate spatial autocorrelation of a classification results: ELSA and FuzzyELSA (see functions calcELSA and calcFuzzyELSA and the end of the vignette rasters)

issue 1 fixed by editing the mapping functions. A bug occurred when the fid of a SpatialDataFrame read from a shapefile was different from 1:nrow(df)

an important performance gain can be observed for large dataset, the function to compare matrices between two iterations is now significantly faster.
core functions rewritten with Rcpp for massive time gain