NEWS

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pencal 2.2

pencal 2.2.2

• Released: June 2024
• Updated vignette with article version accepted by The R Journal
• Updated citations in documentation and CITATIOn file

pencal 2.2.1

• Released: March 2024
• getlmm and getmlpmm functions have been replaced by two S3 classes with summary methods
• S3 classes and summary methods added for the output of steps 2
• Tuning parameter added to the summary methods for step 3
• fitted_prclmm and fitted_prcmlpmm objects have been refitted
• Added survplot_prc function
• Updated documentation
• Updated DESCRIPTION and CITATION files

pencal 2.1

pencal 2.1.1

• Released: October 2023
• Vignette updated with arXiv preprint number
• Updated documentation
• Updated DESCRIPTION and CITATION files

pencal 2.1.0

• Released: September 2023
• Added NEW, more comprehensive vignette
• Added landmark argument to simulate_prclmm_data and simulate_prcmlpmm_data. Examples updated accordingly and refitted fit_prclmm and fit_prcmlpmm
• Updated examples in survpred_prclmm, survpred_prcmlpmm, performance_pencox and performance_pencox

pencal 2.0

pencal 2.0.1

• Released: August 2023
• Added computation of Brier score to performance_prc and performance_pencox_baseline
• Added metric argument to performance_prc and performance_pencox_baseline
• Refitted fit_prclmm and fit_prcmlpmm objects so they are up to date with classes and methods
• Renamed pencox_baseline to pencox and performance_pencox_baseline to performance_pencox
• Added pbc2data and corresponding documentation
• Updated documentation and vignettes
• Updated CITATION file using bibentry( ) to address CRAN note
• Updated DESCRIPTION file (added biocViews: to fix survcomp installation problems)
• Added LICENSE file

pencal 1.3

pencal 1.3.2

• Released: December 2022
• Updated vignettes, including mention of new functionalities introduced in version 1.3.1
• Parallelized an extra computation in summarize_lmms and summarize_mlpmms (this should yield computing time gains with thousands of longitudinal predictors)

pencal 1.3.1

• Released: December 2022
• Added classes (prclmm and prcmlpmm) and corresponding methods (print and summary) to the package
• Added the functions getlmm and getmlpmm
• Added control argument to fit_lmms. This argument is used to pass control parameters to nlme::lme (see ?nlme::lmeControl). See ?fit_lmms for the defaults
• simulate_prclmm_data now outputs an extra element (theta.true) containing the true parameters used to generate the data
• Added eval( ) when creating baseline.covs within survpred_prclmm and survpred_prcmlpmm

pencal 1.2

pencal 1.2.2

• Released: July 2022
• Added seed argument to fit_lmms and fit_mlpmms
• Added a fix within summarize_lmms in case estimation of a LMM fails for a bootstrap replicate
• Fixed condition that triggers error message associated with length of pfac.base.covs in fit_prclmm
• Minor updates in the vignette

pencal 1.2.1

• Released: June 2022
• Improved behaviour of survpred_prclmm when new.longdata is provided. From this version, when all observations of a longitudinal predictor for a given subject are missing, a warning is produced and the corresponding random effects are set equal to 0 (population average). Previously, the function returned an error due to the NAs
• Fixed description of standardize argument in documentation of pencox_baseline
• Parallelization within performance_prc and performance_pencox_baseline extended to computations of naive tdAUC performance
• Streamlined information messages and warnings about parallelization and number of cores used

pencal 1.2.0

• Released: May 2022
• Added max.ymissing argument to fit_lmms: with this change, it is now possible to estimate the LMMs within the PRC-LMM model even if there are subjects with missing measurements for some (but not all) of the longitudinal outcomes. Within summarize_lmms, the predicted random effects when a longitudinal outcome is missing for a given subject are set = 0 (marginal / population average). Setting max.ymissing = 0 disables such additional feature
• Added extra check to summarize_lmms on subjects without any longitudinal information available (i.e., 100% missing on all longitudinal variables used in step 1)
• Introduced dependency on purrr (now required by summarize_lmms)
• Fixed CRAN dependency issue with examples in simulate_prclmm_data and simulate_prcmlpmm_data

pencal 1.1

pencal 1.1.1

• Released: May 2022
• Added tau.age argument to simulate_prclmm_data and simulate_prclmm_data
• Minor fix of an error message inside fit_lmms (row 181)

pencal 1.1.0

• Released: March 2022
• Fixed subject ids displayed in the output of survpred_prclmm
• Fixed bug that made survpred_prclmm fail when new data for just 1 subject were supplied (added missing drop = FALSE)
• Added function call to the output of survpred_prcmlpmm

pencal 1.0

pencal 1.0.2

• Released: February 2022
• Added volume, issue and page number to CITATION file, vignette and help pages
• Updated vignette with more detailed installation instructions

pencal 1.0.1

• Released: December 2021
• Minor correction to package description (it was still mentioning arXiv instead of the Statistics in Medicine publication)

pencal 1.0.0

• Released: September 2021
• The article describing Penalized Regression Calibration is now published in Statistics in Medicine! The article is published with Open Access, so anybody can freely download it from the website of Statistics in Medicine
• Updated package description and citation info
• Updated vignette and help pages

pencal 0.4

pencal 0.4.2

• Released: May 2021
• Function survpred_prc replaced by two distinct functions: survpred_prclmm for the PRC-LMM model, and survpred_prcmlpmm for the PRC-MLPMM model
• Documentation and vignette updated accordingly

pencal 0.4.1

• Released: April 2021
• fit_lmms is now more memory efficient (keep.data = F when calling lme)
• fit_mlpmms is now faster (parallelization implemented also before the CBOCP is started)
• Added functions pencox_baseline and performance_pencox_baseline
• Minor updates to the vignette

pencal 0.3

pencal 0.3.2

• Released: March 2021
• Fixed CRAN error in PRC MLPMM examples (replaced T with TRUE)
• Corrected typos in vignette

pencal 0.3.1

• Released: March 2021
• Added a set of functions that can be used to fit the PRC-MLPMM model(s): simulate_prcmlpmm_data, fit_mlpmms, summarize_mlpmms and fit_prcmlpmm
• Renamed performance_prclmm to performance_prc, and survpred_prclmm to survpred_prc (the functions work both for the PRC-LMM, and the PRC-MLPMM)
• Vignettes and documentation updated to reflect the changes

pencal 0.2

pencal 0.2.2

• Released: January 2021
• Fixed CRAN error caused by parallel::detectCores()
• Added link to arXiv preprint in package description and vignette
• Added CITATION file
• Vignettes updated and revised
• Updated references in help pages

pencal 0.2.1

• Released: January 2021
• Added vignette: “An introduction to the R package pencal”

pencal 0.1

pencal 0.1.2

• Released: December 2020
• Added the function survpred_prclmm, which computes predicted survival probabilities from the fitted PRC-LMM model
• Added fitted_prclmm data object and related documentation (it is used in the examples of performance_prclmm)
• Several corrections and clarifications added to the documentation
• Changed displaying style for function arguments in the documentation

pencal 0.1.1

• Released: November 2020
• This is the first public release of the pencal package. It comprises the skeleton around which the rest of the R package will be built
• This version comprises functions to perform the following tasks:
  1. simulate data corresponding to the PRC-LMM model (functions simulate_t_weibull and simulate_prclmm_data);
  2. estimate the PRC-LMM model and its associated cluster bootstrap optimism correction procedure (functions fit_lmms, summarize_lmms and fit_prclmm);
  3. compute the optimism-corrected estimates of the C index and time-dependent AUC (function performance_prclmm)
• Note: developing an R package that is user-friendly, comprehensive and well-documented is an effort that takes months, sometimes even years. This package is currently under active development, and many additional features and functionalities (including vignettes!) will be added incrementally with the next releases. If you notice a bug or something unclear in the documentation, feel free to get in touch with the maintainer of the package!