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functions have been replaced by two S3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_prcmlpmm
survpred_prclmm
, survpred_prcmlpmm
, performance_pencox
and performance_pencox
performance_prc
and performance_pencox_baseline
metric
argument to performance_prc
and performance_pencox_baseline
fit_prclmm
and fit_prcmlpmm
objects so they are up to date with classes and methodspencox_baseline
to pencox
and performance_pencox_baseline
to performance_pencox
pbc2data
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 getmlpmm
control
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_prcmlpmm
seed
argument to fit_lmms
and fit_mlpmms
summarize_lmms
in case estimation of a LMM fails for a bootstrap replicatepfac.base.covs
in fit_prclmm
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 NA
sstandardize
argument in documentation of pencox_baseline
performance_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_data
tau.age
argument to simulate_prclmm_data
and simulate_prclmm_data
fit_lmms
(row 181)survpred_prclmm
survpred_prclmm
fail when new data for just 1 subject were supplied (added missing drop = FALSE
)survpred_prcmlpmm
survpred_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_baseline
T
with TRUE
)simulate_prcmlpmm_data
, fit_mlpmms
, summarize_mlpmms
and fit_prcmlpmm
performance_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!