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Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. 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 (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2024) <doi:10.1111/anzs.12409>.
Version: | 0.4-2.26 |
Depends: | R (≥ 3.5.0), methods, glmnet |
Imports: | MASS, pscl, numDeriv, foreach, doParallel, bst, WeightSVM |
Suggests: | zic, R.rsp, knitr, rmarkdown, openxlsx, e1071, SparseM, slam |
Published: | 2024-06-27 |
DOI: | 10.32614/CRAN.package.mpath |
Author: | Zhu Wang, with contributions from Achim Zeileis, Simon Jackman, Brian Ripley, and Patrick Breheny |
Maintainer: | Zhu Wang <zwang145 at uthsc.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 |
In views: | MachineLearning |
CRAN checks: | mpath results |
Package source: | mpath_0.4-2.26.tar.gz |
Windows binaries: | r-devel: mpath_0.4-2.26.zip, r-release: mpath_0.4-2.26.zip, r-oldrel: mpath_0.4-2.26.zip |
macOS binaries: | r-release (arm64): mpath_0.4-2.26.tgz, r-oldrel (arm64): mpath_0.4-2.26.tgz, r-release (x86_64): mpath_0.4-2.26.tgz, r-oldrel (x86_64): mpath_0.4-2.26.tgz |
Old sources: | mpath archive |
Reverse depends: | NBtsVarSel |
Reverse imports: | bujar, irboost, nbfar |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.