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Performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," accepted for publication in The Journal of the American Statistical Association, DOI: 10.1198/jasa.2011.tm10183.


This is a two-in-one package which provides interfaces to both R and Python. It implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines:


The package performs cluster analysis via non-parametric density estimation. Operationally, the kernel method is used througout to estimate the density. Diagnostics methods for evaluating the quality of clustering are available. The package includes also a routine to estimate the probability density function by the kernel method, given a set of data with arbitrary dimensions.


Fit and simulate mixtures of von Mises-Fisher distributions.


Various methods for MRI tissue classification.


Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.


SigClust is a statistical method for testing the significance of clustering results. SigClust can be applied to assess the statistical significance of splitting a data set into two clusters. For more than two clusters, SigClust can be used iteratively.


Robust Trimmed Clustering


Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions.


This package provides functions for the estimation of various latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear outcomes using a maximum likelihood method