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.
|Author||Lulu Wang, Wen Zhou, Jennifer Hoeting|
|Date of publication||2016-06-11 09:42:05|
|Maintainer||Lulu Wang <email@example.com>|
apfp: Model-based Clustering with APFP
apL1: Model-based Clustering with APL1
heatmap_fit: summary plot of globally and pairwise informative variables
nopenalty: Classical Model-based Clustering
parse: Model-based Clustering with PARSE
response2drug: Gene-expression Data for Asthma Disease
summary: summary of the clustering results
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