Package: pencal 2.3.1

pencal: Penalized Regression Calibration 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. See Signorelli (2024) <doi:10.32614/RJ-2024-014> and Signorelli et al. (2021) <doi:10.1002/sim.9178> for details.

Authors:Mirko Signorelli [aut, cre, cph], Pietro Spitali [ctb], Roula Tsonaka [ctb], Barbara Vreede [ctb]

pencal_2.3.1.tar.gz
pencal_2.3.1.zip(r-4.7)pencal_2.3.1.zip(r-4.6)pencal_2.3.1.zip(r-4.5)
pencal_2.3.1.tgz(r-4.6-any)pencal_2.3.1.tgz(r-4.5-any)
pencal_2.3.1.tar.gz(r-4.7-any)pencal_2.3.1.tar.gz(r-4.6-any)
pencal_2.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
pencal/json (API)
NEWS

# Install 'pencal' in R:
install.packages('pencal', repos = c('https://m-signo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mirkosignorelli/r/issues

Datasets:

On CRAN:

Conda:

2.53 score 17 scripts 518 downloads 15 exports 118 dependencies

Last updated from:d87c5c6fcd. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK219
source / vignettesOK257
linux-release-x86_64OK209
macos-release-arm64OK138
macos-oldrel-arm64OK134
windows-develOK137
windows-releaseOK139
windows-oldrelOK155
wasm-releaseOK157

Exports:fit_lmmsfit_mlpmmsfit_prclmmfit_prcmlpmmpencoxperformance_pencoxperformance_prcsimulate_prclmm_datasimulate_prcmlpmm_datasimulate_t_weibullsummarize_lmmssummarize_mlpmmssurvplot_prcsurvpred_prclmmsurvpred_prcmlpmm

Dependencies:abindbackportsbase64encbootstrapbslibcachemcheckmateclasscliclustercmprskcodetoolscolorspacecpp11data.tablediagramdigestdoParalleldplyrevaluatefarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2glmnetglobalsgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalcmmlifecyclelistenvmagicmagrittrmarqLevAlgMASSMatrixMatrixModelsmemoisemetsmimemultcompmvtnormnlmennetnumDerivparallellypillarpkgconfigplotrixpolsplineprodlimprogressrPublishpurrrquantregR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenriskRegressionrlangrmarkdownrmetarmsrpartrstudioapiS7sandwichsassscalesshapespacefillrSparseMSQUAREMstringistringrSuppDistssurvcompsurvivalsurvivalROCTH.datatibbletidyselecttimeregtinytexutf8vctrsviridisLitewithrxfunyamlzoo

pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors

Rendered fromvignette.Rnwusingutils::Sweaveon Jun 02 2026.

Last update: 2026-04-09
Started: 2023-09-16

Readme and manuals

Help Manual

Help pageTopics
Step 1 of PRC LMM (estimation of the linear mixed models)fit_lmms
Step 1 of PRC MLPMM (estimation of the linear mixed models)fit_mlpmms
Step 3 of PRC LMM (estimation of the penalized Cox model(s))fit_prclmm
Step 3 of PRC MLPMM (estimation of the penalized Cox model(s))fit_prcmlpmm
A fitted PRC LMMfitted_prclmm
A fitted PRC MLPMMfitted_prcmlpmm
pbc2 datasetpbc2data
Estimation of a penalized Cox model with time-independent covariatespencox
Predictive performance of the penalized Cox model with time-independent covariatesperformance_pencox
Predictive performance of the PRC-LMM and PRC-MLPMM modelsperformance_prc
Print method for PRC LMM model fitsprint.prclmm
Print method for PRC MLPMM model fitsprint.prcmlpmm
Simulate data that can be used to fit the PRC LMM modelsimulate_prclmm_data
Simulate data that can be used to fit the PRC MLPMM modelsimulate_prcmlpmm_data
Generate survival data from a Weibull modelsimulate_t_weibull
Step 2 of PRC LMM (computation of the predicted random effects)summarize_lmms
Step 2 of PRC MLPMM (computation of the predicted random effects)summarize_mlpmms
Extract model fits from step 1 of PRC-LMMsummary.lmmfit
Extract model fits from step 1 of PRC-LMMsummary.mlpmmfit
Summary method for PRC LMM model fitssummary.prclmm
Summary method for PRC MLPMM model fitssummary.prcmlpmm
Summary for step 2 of PRCsummary.ranefs
Visualize survival predictions for a fitted PRC modelsurvplot_prc
Compute the predicted survival probabilities obtained from the PRC LMM modelsurvpred_prclmm
Compute the predicted survival probabilities obtained from the PRC MLPMM modelssurvpred_prcmlpmm