2. 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.
3. 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.
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. 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]
8. 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.