Package: pencal 2.2.4

Mirko Signorelli

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>.

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

pencal_2.2.4.tar.gz
pencal_2.2.4.tar.gz(r-4.5-noble)pencal_2.2.4.tar.gz(r-4.4-noble)
pencal_2.2.4.tgz(r-4.4-emscripten)
pencal.pdf |pencal.html✨
pencal/json (API)
NEWS

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

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

Datasets:

On CRAN:

Conda:

2.60 score 725 downloads 15 exports 120 dependencies

Last updated 15 hours agofrom:838e4c8971. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-linuxOKMar 10 2025
R-4.4-linuxOKMar 10 2025

Exports:fit_lmmsfit_mlpmmsfit_prclmmfit_prcmlpmmpencoxperformance_pencoxperformance_prcsimulate_prclmm_datasimulate_prcmlpmm_datasimulate_t_weibullsummarize_lmmssummarize_mlpmmssurvplot_prcsurvpred_prclmmsurvpred_prcmlpmm

Dependencies:abindbackportsbase64encbootstrapbslibcachemcheckmateclasscliclustercmprskcodetoolscolorspacedata.tablediagramdigestdoParalleldplyrevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2glmnetglobalsgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalcmmlifecyclelistenvmagicmagrittrmarqLevAlgMASSMatrixMatrixModelsmemoisemetsmgcvmimemultcompmunsellmvtnormnlmennetnumDerivparallellypillarpkgconfigplotrixpolsplineprodlimprogressrPublishpurrrquantregR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenriskRegressionrlangrmarkdownrmetarmsrpartrstudioapisandwichsassscalesshapespacefillrSparseMSQUAREMstringistringrSuppDistssurvcompsurvivalsurvivalROCTH.datatibbletidyselecttimeregtinytexutf8vctrsviridisviridisLitewithrxfunyamlzoo

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

Rendered fromvignette.Rnwusingutils::Sweaveon Mar 10 2025.

Last update: 2024-06-13
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-LMM 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 modelssurvpred_prclmm
Compute the predicted survival probabilities obtained from the PRC modelssurvpred_prcmlpmm