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 (2024) <doi:10.48550/arXiv.2309.15600> and in Signorelli et al. (2021) <doi:10.1002/sim.9178>.

Version: 2.2.2
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: 2024-06-12
DOI: 10.32614/CRAN.package.pencal
Author: Mirko Signorelli ORCID iD [aut, cre, cph], Pietro Spitali [ctb], Roula Tsonaka [ctb], Barbara Vreede [ctb]
Maintainer: Mirko Signorelli <msignorelli.rpackages at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: pencal citation info
Materials: NEWS
CRAN checks: pencal results


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


Package source: pencal_2.2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): pencal_2.2.2.tgz, r-oldrel (arm64): pencal_2.2.2.tgz, r-release (x86_64): pencal_2.2.2.tgz, r-oldrel (x86_64): pencal_2.2.2.tgz
Old sources: pencal archive


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