Package: MTE 1.2.1

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 preprint <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.

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

MTE_1.2.1.tar.gz
MTE_1.2.1.zip(r-4.7)MTE_1.2.1.zip(r-4.6)MTE_1.2.1.zip(r-4.5)
MTE_1.2.1.tgz(r-4.6-any)MTE_1.2.1.tgz(r-4.5-any)
MTE_1.2.1.tar.gz(r-4.7-any)MTE_1.2.1.tar.gz(r-4.6-any)
MTE_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
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 9 scripts 344 downloads 24 mentions 7 exports 25 dependencies

Last updated from:30935d111a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK132
source / vignettesOK154
linux-release-x86_64OK126
macos-release-arm64OK174
macos-oldrel-arm64OK144
windows-develOK102
windows-releaseOK98
windows-oldrelOK109
wasm-releaseOK108

Exports:huber.lassohuber.reghuberlossLADLADlassoMTEMTElasso

Dependencies:clicodetoolsdata.tableforeachglmnethqreghrqglasiteratorslatticelifecycleMASSMatrixMatrixModelsplyrquantregrbibutilsRcppRcppArmadilloRcppEigenRdpackrlangrqPenshapeSparseMsurvival