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sim_gs_n()
is available (#268,
thanks to @LittleBeannie).sim_gs_n()
(#273, thanks to @LittleBeannie).sim_pw_surv()
and passed into the wlr()
test
(#281, #285 thanks to @LittleBeannie and @jdblischak).summary()
is rewritten from
tibble
or data.frame
to
data.table
, which is optimized to use as little memory as
possible by avoiding making unnecessary temporary copies of data frames.
This results in code that is more efficient in both time and memory use.
(#289, thanks to @jdblischak).sim_fixed_n()
function has been updated to allow
parallel simulations (#249, #252, #253, #262, thanks to @cmansch and @jdblischak).wlr()
function is enhanced to S3 generic to accept
both counting process and time-to-event data as its input (#276, #277,
thanks to @jdblischak).summary()
is added (#282, thanks to
@jdblischak).noSuggests
checks by running code examples, tests,
and vignettes conditionally if the Suggests
dependencies
are not installed (#243).rmst()
introduces the RMST test (#188, thanks, @LittleBeannie).milestone()
introduces the milestone test (#199, #204,
#211, #237, thanks, @LittleBeannie).sim_gs_n()
provides an experimental implementation for
fixed sample size group sequential design simulation, with unit tests
upcoming (#195, #201, #208, #212, thanks, @LittleBeannie and @jdblischak).create_cut()
allows users to create custom interim and
final analyses cuttings based on their specific requirements (#201,
#221, thanks, @jdblischak).create_test()
enables users to create various testing
approaches for interim and final analyses (#215, #221, thanks, @jdblischak).multitest()
gives users the option to perform multiple
tests on a simulated dataset (#215, thanks, @jdblischak). Note: This function is
still experimental and may be improved in future releases, as it was
created prior to the standardization of test functions in #227.early_zero()
under
stratified designs (#233, thanks, @LittleBeannie).get_analysis_date()
(#186,
thanks, @LittleBeannie).This release makes minor improvements on auxiliary code with side-effects.
options()
within
vignette("modest-wlrt")
.tempdir()
instead of the package directory.This release introduces significant changes to the API, improves simulation performance substantially, and adds new features and documentation.
sim_fixed_n()
now utilizes the %dofuture%
operator for parallelization, enhancing flexibility and reproducibility
(thanks, @cmansch,
#110).rpwexp()
adopts the inverse CDF method for random
number generation, with the naive methods now as internal functions
(thanks, @jianxiaoyang, #15 and #174).sim_fixed_n()
is optimized to skip Breslow’s method in
the absence of ties (thanks, @jdblischak, #130).wlr_z_stat()
(thanks, @elong0527, #105).early_zero_weight()
is added as a weighting function
for early data removal (thanks, @LittleBeannie, #123).get_analysis_date()
is added to calculate interim/final
analysis dates under various conditions (thanks, @LittleBeannie, #122).vignette("workflow")
providing an overview of data
manipulations involved in TTE simulations (thanks, @keaven, #99).vignette("parallel")
demonstrating the
parallelization workflow and coding best practices (thanks, @cmansch, #113 and
#134).GitHub release in February 2023.
This is the version that enables parallel computation in
simfix()
.
GitHub release in May 2022.
This version supports the Biometrical Journal paper “A unified framework for weighted parametric group sequential design (WPGSD)” by Keaven M. Anderson, Zifang Guo, Jing Zhao, and Linda Z. Sun.
Internal development release in August 2020.
Internal development release in February 2020.
wMB()
to compute Magirr-Burman weights.Depends
with Imports
in
DESCRIPTION
.Internal development release in November 2019.
simfix()
,
simfix2simPWSurv()
, pMaxCombo()
).hgraph()
with intent to put it into a release
of gsDesign.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.