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Several robust estimators for linear regression and variable selection are provided. Included are Maximum tangent likelihood estimator by Qin, et al., (2017) <doi:10.48550/arXiv.1708.05439>, least absolute deviance estimator and Huber regression. The penalized version of each of these estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to produce consistent estimates for both fixed and high-dimensional settings.
| Version: | 1.2 |
| Depends: | R (≥ 3.1.0) |
| Imports: | stats, quantreg, glmnet, rqPen |
| Published: | 2023-04-11 |
| DOI: | 10.32614/CRAN.package.MTE |
| Author: | Shaobo Li [aut, cre], Yichen Qin [aut] |
| Maintainer: | Shaobo Li <shaobo.li at ku.edu> |
| License: | GPL-3 |
| URL: | GitHub: https://github.com/shaobo-li/MTE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | MTE results |
| Reference manual: | MTE.pdf |
| Package source: | MTE_1.2.tar.gz |
| Windows binaries: | r-devel: MTE_1.2.zip, r-release: MTE_1.2.zip, r-oldrel: MTE_1.2.zip |
| macOS binaries: | r-release (arm64): MTE_1.2.tgz, r-oldrel (arm64): MTE_1.2.tgz, r-release (x86_64): MTE_1.2.tgz, r-oldrel (x86_64): MTE_1.2.tgz |
| Old sources: | MTE archive |
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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.