penAFT: Fit the Regularized Gehan Estimator with Elastic Net and Sparse Group Lasso Penalties

The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022+) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, to appear in Statistics in Medicine <doi:10.1002/sim.9264>.

Version: 0.2.0
Imports: Rcpp, Matrix, ggplot2, irlba
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-01-25
Author: Aaron J. Molstad ORCID iD [aut, cre], Piotr M. Suder [aut]
Maintainer: Aaron J. Molstad <amolstad at ufl.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: ajmolstad.github.io/research
NeedsCompilation: yes
Materials: README
CRAN checks: penAFT results

Documentation:

Reference manual: penAFT.pdf

Downloads:

Package source: penAFT_0.2.0.tar.gz
Windows binaries: r-devel: penAFT_0.2.0.zip, r-release: penAFT_0.2.0.zip, r-oldrel: penAFT_0.2.0.zip
macOS binaries: r-release (arm64): penAFT_0.2.0.tgz, r-release (x86_64): penAFT_0.2.0.tgz, r-oldrel: penAFT_0.2.0.tgz

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