pencal: Penalized Regression Calibration (PRC) for the Dynamic Prediction of Survival

Computes penalized regression calibration (PRC), a statistical method for the dynamic prediction of survival when many longitudinal predictors are available. PRC is described in Signorelli et al. (2021) <doi:10.1002/sim.9178> and Signorelli (2023) <doi:10.48550/arXiv.2309.15600>.

Version: 2.2.1
Depends: R (≥ 4.1.0)
Imports: doParallel, dplyr, foreach, glmnet, lcmm, magic, MASS, Matrix, methods, nlme, purrr, riskRegression, stats, survcomp, survival, survivalROC
Suggests: knitr, ptmixed, rmarkdown, survminer
Published: 2024-03-31
Author: Mirko Signorelli ORCID iD [aut, cre, cph], Pietro Spitali [ctb], Roula Tsonaka [ctb], Barbara Vreede [ctb]
Maintainer: Mirko Signorelli <msignorelli.rpackages at gmail.com>
License: GPL (≥ 3)
URL: https://mirkosignorelli.github.io/r
NeedsCompilation: no
Citation: pencal citation info
Materials: NEWS
CRAN checks: pencal results

Documentation:

Reference manual: pencal.pdf
Vignettes: pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors

Downloads:

Package source: pencal_2.2.1.tar.gz
Windows binaries: r-devel: pencal_2.2.1.zip, r-release: pencal_2.2.1.zip, r-oldrel: pencal_2.2.1.zip
macOS binaries: r-release (arm64): pencal_2.2.1.tgz, r-oldrel (arm64): pencal_2.2.1.tgz, r-release (x86_64): pencal_2.2.1.tgz
Old sources: pencal archive

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