Algorithms optimize penalized models. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), and each possibly combining with L_2 penalty. See Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2019) <arXiv:1912.11119>.
| Version: | 0.3-26 |
| Depends: | methods |
| Imports: | MASS, pscl, numDeriv, foreach, doParallel, bst |
| Suggests: | zic, R.rsp, knitr, gdata |
| Published: | 2020-06-01 |
| Author: | Zhu Wang, with contributions from Achim Zeileis, Simon Jackman, Brian Ripley, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Gil Chu and Patrick Breheny |
| Maintainer: | Zhu Wang <wangz1 at uthscsa.edu> |
| BugReports: | https://github.com/zhuwang46/mpath |
| License: | GPL-2 |
| Copyright: | see file COPYRIGHTS |
| URL: | https://github.com/zhuwang46/mpath |
| NeedsCompilation: | yes |
| Citation: | mpath citation info |
| Materials: | NEWS |
| CRAN checks: | mpath results |
| Reference manual: | mpath.pdf |
| Vignettes: |
Classification of Cancer Patients with Penalized Robust Nonconvex Loss Functions (with Results) Variable Selection for Health Care Demand in Germany (with Results) Classification of Cancer Patients with Penalized Robust Nonconvex Loss Functions (without Results) Variable Selection for Health Care Demand in Germany (without Results) KKT Conditions for Zero-Inflated Regression |
| Package source: | mpath_0.3-26.tar.gz |
| Windows binaries: | r-devel: mpath_0.3-26.zip, r-release: mpath_0.3-26.zip, r-oldrel: mpath_0.3-26.zip |
| macOS binaries: | r-release: mpath_0.3-26.tgz, r-oldrel: mpath_0.3-26.tgz |
| Old sources: | mpath archive |
| Reverse imports: | bujar |
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