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-any)MTE_1.2.1.zip(r-4.6-any)MTE_1.2.1.zip(r-4.5-any)
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✨
DESCRIPTION
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 297 downloads 24 mentions 7 exports 25 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK146
source / vignettesOK165
linux-releaseOK123
macos-releaseOK181
macos-oldrelOK206
windows-develOK93
windows-releaseOK80
windows-oldrelOK109
wasm-releaseOK80

Exports:huber.lassohuber.reghuberlossLADLADlassoMTEMTElasso

Dependencies:clicodetoolsdata.tableforeachglmnethqreghrqglasiteratorslatticelifecycleMASSMatrixMatrixModelsplyrquantregrbibutilsRcppRcppArmadilloRcppEigenRdpackrlangrqPenshapeSparseMsurvival