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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functions have been
replaced by two S3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_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!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.