ALassoSurvIC: Adaptive Lasso for the Cox Regression with Interval Censored and Possibly Left Truncated Data

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 <doi:10.1177/0962280219856238>). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.

Version:	0.1.1
Depends:	R (≥ 3.5.0)
Imports:	Rcpp, parallel
LinkingTo:	Rcpp
Published:	2022-12-01
DOI:	10.32614/CRAN.package.ALassoSurvIC
Author:	Chenxi Li, Daewoo Pak and David Todem
Maintainer:	Daewoo Pak <heavyrain.pak at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	ALassoSurvIC results

Documentation:

Reference manual:

ALassoSurvIC.pdf

Downloads:

Package source:	ALassoSurvIC_0.1.1.tar.gz
Windows binaries:	r-devel: ALassoSurvIC_0.1.1.zip, r-release: ALassoSurvIC_0.1.1.zip, r-oldrel: ALassoSurvIC_0.1.1.zip
macOS binaries:	r-release (arm64): ALassoSurvIC_0.1.1.tgz, r-oldrel (arm64): ALassoSurvIC_0.1.1.tgz, r-release (x86_64): ALassoSurvIC_0.1.1.tgz, r-oldrel (x86_64): ALassoSurvIC_0.1.1.tgz
Old sources:	ALassoSurvIC archive

Linking:

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