Regularization for Variable Selection in Model-Based Clustering and Discriminant Analysis
Overview
Performs a regularization approach to variable selection in the model-based clustering and classification frameworks. First, the variables are arranged in order with a lasso-like procedure. Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011) doi:10.1016/j.csda.2009.04.013, doi:10.1016/j.jmva.2011.05.004 is adapted to define the role of variables in the two frameworks.
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Maintainer since 1.0 (2014)
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- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-12-05issues were not corrected despite reminders
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 1.2.12017-10-16 · diff ↗
- RR 3.4.0 released · 2017-04-21
- 1.22016-11-07 · diff ↗
- RR 3.3.0 released · 2016-05-03
- 1.12015-09-20 · diff ↗
- RR 3.2.0 released · 2015-04-16
- RR 3.1.0 released · 2014-04-10
- 1.02014-04-03
- RR 3.0.0 released · 2013-04-03
Package metadata
- First published
- 2014-04-03
- Total releases
- 4 / 12 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.1.0
- Bundled data
- 11 KB / 1 file
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