PARSE: Model-Based Clustering with Regularization Methods for High-Dimensional Data

Model-based clustering and identifying informative features based on regularization methods. The package includes three regularization methods - PAirwise Reciprocal fuSE (PARSE) penalty proposed by Wang, Zhou and Hoeting (2016), the adaptive L1 penalty (APL1) and the adaptive pairwise fusion penalty (APFP). Heatmaps are included to shown the identification of informative features.

Getting started

Package details

AuthorLulu Wang, Wen Zhou, Jennifer Hoeting
MaintainerLulu Wang <[email protected]>
Package repositoryView on CRAN
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PARSE documentation built on May 30, 2017, 1:16 a.m.