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.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.0.0) |
| Imports: | stats, mvtnorm, gplots, foreach, doParallel, grDevices, utils |
| Published: | 2016-06-11 |
| Author: | Lulu Wang, Wen Zhou, Jennifer Hoeting |
| Maintainer: | Lulu Wang <wanglulu at stat.colostate.edu> |
| License: | CC0 |
| NeedsCompilation: | no |
| CRAN checks: | PARSE results |
| Reference manual: | PARSE.pdf |
| Package source: | PARSE_0.1.0.tar.gz |
| Windows binaries: | r-devel: PARSE_0.1.0.zip, r-devel-gcc8: PARSE_0.1.0.zip, r-release: PARSE_0.1.0.zip, r-oldrel: PARSE_0.1.0.zip |
| OS X binaries: | r-release: PARSE_0.1.0.tgz, r-oldrel: PARSE_0.1.0.tgz |
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