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The aim of this document is to keep track of the changes made to the different versions of the R package pencal.
The numbering of package versions follows the convention a.b.c, where a and b are non-negative integers, and c is a positive integer. When minor changes are made to the package, a and b are kept fixed and only c is increased. Major changes to the package, instead, are made apparent by changing a or b.
Each section of this document corresponds to a major change in the package - in other words, within a section you will find all those package versions a.b.x where a and b are fixed whereas x = 1, 2, 3, … Each subsection corresponds to a specific package version.
getlmm and getmlpmm functions have been replaced by two S3 classes with summary methodssummary methods added for the output of steps 2summary methods for step 3fitted_prclmm and fitted_prcmlpmm objects have been refittedsurvplot_prc functionlandmark argument to simulate_prclmm_data and simulate_prcmlpmm_data. Examples updated accordingly and refitted fit_prclmm and fit_prcmlpmmsurvpred_prclmm, survpred_prcmlpmm, performance_pencox and performance_pencoxperformance_prc and performance_pencox_baselinemetric argument to performance_prc and performance_pencox_baselinefit_prclmm and fit_prcmlpmm objects so they are up to date with classes and methodspencox_baseline to pencox and performance_pencox_baseline to performance_pencoxpbc2data and corresponding documentationCITATION file using bibentry( ) to address CRAN noteDESCRIPTION file (added biocViews: to fix survcomp installation problems)LICENSE filesummarize_lmms and summarize_mlpmms (this should yield computing time gains with thousands of longitudinal predictors)prclmm and prcmlpmm) and corresponding methods (print and summary) to the packagegetlmm and getmlpmmcontrol argument to fit_lmms. This argument is used to pass control parameters to nlme::lme (see ?nlme::lmeControl). See ?fit_lmms for the defaultssimulate_prclmm_data now outputs an extra element (theta.true) containing the true parameters used to generate the dataeval( ) when creating baseline.covs within survpred_prclmm and survpred_prcmlpmmseed argument to fit_lmms and fit_mlpmmssummarize_lmms in case estimation of a LMM fails for a bootstrap replicatepfac.base.covs in fit_prclmmsurvpred_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 NAsstandardize argument in documentation of pencox_baselineperformance_prc and performance_pencox_baseline extended to computations of naive tdAUC performancemax.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 featuresummarize_lmms on subjects without any longitudinal information available (i.e., 100% missing on all longitudinal variables used in step 1)purrr (now required by summarize_lmms)CRAN dependency issue with examples in simulate_prclmm_data and simulate_prcmlpmm_datatau.age argument to simulate_prclmm_data and simulate_prclmm_datafit_lmms (row 181)survpred_prclmmsurvpred_prclmm fail when new data for just 1 subject were supplied (added missing drop = FALSE)survpred_prcmlpmmsurvpred_prc replaced by two distinct functions: survpred_prclmm for the PRC-LMM model, and survpred_prcmlpmm for the PRC-MLPMM modelfit_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)pencox_baseline and performance_pencox_baselineT with TRUE)simulate_prcmlpmm_data, fit_mlpmms, summarize_mlpmms and fit_prcmlpmmperformance_prclmm to performance_prc, and survpred_prclmm to survpred_prc (the functions work both for the PRC-LMM, and the PRC-MLPMM)survpred_prclmm, which computes predicted survival probabilities from the fitted PRC-LMM modelfitted_prclmm data object and related documentation (it is used in the examples of performance_prclmm)pencal package. It comprises the skeleton around which the rest of the R package will be builtsimulate_t_weibull and simulate_prclmm_data);fit_lmms, summarize_lmms and fit_prclmm);performance_prclmm)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!These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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