PD-Clustering and Related Methods R package FPDclustering version 2.3.5 CRAN Task View: Cluster (https://CRAN.R-project.org/view=Cluster) Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets. Software Depends: ThreeWay,mvtnorm,R (>= 4.1.0) Imports: ExPosition, cluster,rootSolve, MASS, klaR, GGally, ggplot2, ggeasy Cristina Tortora <grikris1@gmail.com> Comprehensive R Archive Network (CRAN) Cristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], Tina Kalra [aut], Paul D. McNicholas [fnd] GPL (>= 2) 2025-03-06 application/tgz https://CRAN.R-project.org/package=FPDclustering doi:10.32614/CRAN.package.FPDclustering