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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 |
| 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 |
| Reference manual: | ALassoSurvIC.pdf |
| 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 |
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