1. 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.
4. 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.
5. ziLRhClust: hclust for zeroinflated data- Algorithm to process hierarchical clustering, based on log-likelihood ratio, considering standard Gaussian or zero-Inflated Gaussian data. The agglomeration method is also the log-likelihood-based dissimilarity between clusters.
6. clamix - Clustering Symbolic Objects- The package implements clustering procedures of symbolic objects that are described by multi valued modal data. Additional: classes symObject and symData for saving symbolic objects and their methods are implemented.
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. 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.
9. 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.