Package: MTE 1.2

MTE: Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection

Several robust estimators for linear regression and variable selection are provided. Included are Maximum tangent likelihood estimator by Qin, et al., (2017) <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.

Authors:Shaobo Li [aut, cre], Yichen Qin [aut]

MTE_1.2.tar.gz
MTE_1.2.zip(r-4.5)MTE_1.2.zip(r-4.4)MTE_1.2.zip(r-4.3)
MTE_1.2.tgz(r-4.5-any)MTE_1.2.tgz(r-4.4-any)MTE_1.2.tgz(r-4.3-any)
MTE_1.2.tar.gz(r-4.5-noble)MTE_1.2.tar.gz(r-4.4-noble)
MTE_1.2.tgz(r-4.4-emscripten)MTE_1.2.tgz(r-4.3-emscripten)
MTE.pdf |MTE.html✨
MTE/json (API)

# Install 'MTE' in R:
install.packages('MTE', repos = c('https://shaobo-li.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/shaobo-li/mte/issues

On CRAN:

Conda:

3.70 score 1 stars 8 scripts 596 downloads 24 mentions 7 exports 26 dependencies

Last updated 2 years agofrom:9065f91847. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 21 2025
R-4.5-winOKMar 21 2025
R-4.5-macOKMar 21 2025
R-4.5-linuxOKMar 21 2025
R-4.4-winOKMar 21 2025
R-4.4-macOKMar 21 2025
R-4.4-linuxOKMar 21 2025
R-4.3-winOKMar 21 2025
R-4.3-macOKMar 21 2025

Exports:huber.lassohuber.reghuberlossLADLADlassoMTEMTElasso

Dependencies:clicodetoolsdata.tableforeachglmnetgluehqreghrqglasiteratorslatticelifecycleMASSMatrixMatrixModelsplyrquantregrbibutilsRcppRcppArmadilloRcppEigenRdpackrlangrqPenshapeSparseMsurvival