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-4.4-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'))

Peer review:

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

On CRAN:

3.70 score 8 scripts 656 downloads 24 mentions 7 exports 26 dependencies

Last updated 2 years agofrom:9065f91847. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 22 2024
R-4.5-winOKOct 22 2024
R-4.5-linuxOKOct 22 2024
R-4.4-winOKOct 22 2024
R-4.4-macOKOct 22 2024
R-4.3-winOKOct 22 2024
R-4.3-macOKOct 22 2024

Exports:huber.lassohuber.reghuberlossLADLADlassoMTEMTElasso

Dependencies:clicodetoolsdata.tableforeachglmnetgluehqreghrqglasiteratorslatticelifecycleMASSMatrixMatrixModelsplyrquantregrbibutilsRcppRcppArmadilloRcppEigenRdpackrlangrqPenshapeSparseMsurvival