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We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in Tian et al. (2023) <doi:10.48550/arXiv.2302.02310>.
Version: | 0.1.0 |
Depends: | R (≥ 3.5.0) |
Imports: | foreach, doParallel, stats, Rcpp, nnet, magrittr, utils |
LinkingTo: | Rcpp |
Suggests: | knitr, rmarkdown |
Published: | 2023-02-16 |
DOI: | 10.32614/CRAN.package.pemultinom |
Author: | Ye Tian [aut, cre], Henry Rusinek [aut], Arjun V. Masurkar [aut], Yang Feng [aut] |
Maintainer: | Ye Tian <ye.t at columbia.edu> |
License: | GPL-2 |
NeedsCompilation: | yes |
CRAN checks: | pemultinom results |
Reference manual: | pemultinom.pdf |
Package source: | pemultinom_0.1.0.tar.gz |
Windows binaries: | r-devel: pemultinom_0.1.0.zip, r-release: pemultinom_0.1.0.zip, r-oldrel: pemultinom_0.1.0.zip |
macOS binaries: | r-release (arm64): pemultinom_0.1.0.tgz, r-oldrel (arm64): pemultinom_0.1.0.tgz, r-release (x86_64): pemultinom_0.1.0.tgz, r-oldrel (x86_64): pemultinom_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.