km_curve()local() wrappers and made several code
optimizations.internal.weights().SEQopts().SEQuential() time.col validation
detecting and repairing non-zero-indexed time.eligible.col valuestime_varying.cols and
fixed.colsSEQopts()treat.level values exist in the
treatment columnexcused.cols flagsfollowup.min/max
orderingtreat.level length validation for multinomial and
non-multinomial analysescense.eligible and
weight.eligible_colsfollowup.min and
weight.lower from -Inf to 0internal.plot()km_curve() returning list instead of ggplot for
non-subgroup casekm_curve() subtitle conditionrisk.comparison() CIs being NA with
competing eventscbind() with := in expansion chain
to avoid intermediate copymerge() with data.table native join in
expansion data_list combine stepFALSEmatch(TRUE, ...) instead of
which(...)[1] to find first switch/event per groupsapply loop with single matrix multiply in
multinomial predictioncopy() in data_all construction and free data
list in internal_survival.R to reduce peak memory during bootstrapfollowup==0 before adding trialID in
internal.survival to avoid copying entire expanded datasetset.seed() call in
internal.hazard() to make main estimate reproducible. And
also implement fix to ensure the bootstrapping, including both standard
error and percentiles, is deterministic given the seed.hazard_ratio() function now correctly desscribes
the estimate as “Hazard ratio”covariates() function now returns more nicely
formatted output (with spaces around ~ and +
symbols in the model formulae)table() call with data.table’s
.Ngc() callscopy()
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