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penAFT (version 0.3.2)

Fit the Semiparametric Accelerated Failure Time Model with Elastic Net and Sparse Group Lasso Penalties

Description

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 rank-based 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, Statistics in Medicine .

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Version

Install

install.packages('penAFT')

Monthly Downloads

301

Version

0.3.2

License

GPL (>= 2)

Maintainer

Aaron J. Molstad

Last Published

June 16th, 2025

Functions in penAFT (0.3.2)

penAFT.predict

Obtain linear predictor for new subjects using fitted model from penAFT or penAFT.cv
genSurvData

Generate a survival dataset from the log-logistic accelerated failure time model.
penAFT.plot

Plot cross-validation curves
penAFT-package

Fit the semiparametric accelerated failure time model in high dimensions by minimizing a rank-based estimation criterion plus weighted elastic net or weighted sparse group-lasso penalty.
penAFT.trace

Print trace plot for the semiparametric AFT fit using penAFT or penAFT.cv
penAFT.cv

Cross-validation function for fitting a regularized semiparametric accelerated failure time model
penAFT.coef

Extract regression coefficients from fitted model object
penAFT

Fit the solution path for the penalized semiparametric accelerated failure time model with weighted elastic net or weighted sparse group lasso penalties.