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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
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Package details |
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| Author | Lulu Wang, Wen Zhou, Jennifer Hoeting |
| Maintainer | Lulu Wang <wanglulu@stat.colostate.edu> |
| License | CC0 |
| Version | 0.1.0 |
| Package repository | View on CRAN |
| Installation |
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