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PARSE (version 0.1.0)

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

Description

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.

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Install

install.packages('PARSE')

Monthly Downloads

17

Version

0.1.0

License

CC0

Maintainer

Last Published

June 11th, 2016

Functions in PARSE (0.1.0)

heatmap_fit

summary plot of globally and pairwise informative variables
response2drug

Gene-expression Data for Asthma Disease
summary

summary of the clustering results