MTE: Maximum Tangent Likelihood Estimation for Linear Regression

Several robust estimators for linear regression and variable selection are provided. Included are Maximum tangent likelihood estimator (Qin, et al., 2017), 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: 1.0.1
Depends: R (≥ 3.1.0)
Imports: stats, quantreg, glmnet
Published: 2021-05-11
Author: Shaobo Li [aut, cre], Yichen Qin [aut]
Maintainer: Shaobo Li < at>
License: GPL-3
URL: GitHub:
NeedsCompilation: no
Materials: README
CRAN checks: MTE results


Reference manual: MTE.pdf


Package source: MTE_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MTE_1.0.1.tgz, r-release (x86_64): MTE_1.0.1.tgz, r-oldrel: MTE_1.0.1.tgz
Old sources: MTE archive


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