1. Bayesian Biclustering- This package uses a Bayesian spike-and-slab model to construct bidendrograms using log posterior as the natural distance defined by the model and calculates importance using log Bayes factor.
2. bclust- Bayesian clustering with variable selection is a fully automatic clustering method, designed for low-sample-size-high-dimensional situations. This might be regarded as a high-dimensional variant of the mclust package.
Development Status : 5 - Production/Stable [Filter]
4. Local Depth- The package "localdepth" contains functions for the evaluation of some Depth functions and their corresponding local versions, namely Local Depths. Simplicial, Ellipsoid and Mahalanobis Depths are implemented for general dimension.
6. Clustering using convex fusion penalties- An R/C++ implementation of the clusterpath algorithm described in Hocking et al. 2011, for robust convex clustering using sparsity-inducing fusion penalties.
7. Mixed-data clustering and visualization- The aim of the project is the development of utilities for clustering subjects and variables of mixed data types. The main purpose is the generation of a mixed-data heatmap.