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rmst_fast(): restricted mean survival time for a single
group or a two-group comparison (difference and ratio contrasts),
integrating the Kaplan-Meier survival step function in a single C++
scan.wmst_fast(): window mean survival time over an
interval, generalizing rmst_fast() (which is the special
case with a lower window limit of zero), for a single group or a
two-group difference, computed in the same single C++ scan with a
Greenwood-type variance in which each event time contributes its squared
remaining window area.milestone_fast(): two-group comparison of Kaplan-Meier
survival at a milestone timepoint, with Wald, log-log, and MOVER
inference methods.medsurv_fast(): median survival time for a single group
or a two-group difference, with a native kernel-hazard variance method
and an nph-compatible local-constant-hazard method that
reproduces the median comparison of the nph package to
numerical precision; the point estimate is the same under both
methods.maxcombo_fast(): max-combo test over a set of
Fleming-Harrington weighted log-rank statistics, with the joint p-value
obtained from the implied multivariate normal distribution.rmw_fast(): robust modestly-weighted log-rank test of
Magirr and Öhrn, the maximum of the standard log-rank and a
modestly-weighted log-rank statistic, with the joint p-value obtained
from the implied bivariate normal distribution.wkm_fast(): weighted Kaplan-Meier (Pepe-Fleming) test,
the weighted integrated difference between two Kaplan-Meier curves, with
Pepe-Fleming, square-root, and constant weights, reproducing the
weighted Kaplan-Meier statistic of the nphsim package.ahsw_fast(): average hazard with survival weight of Uno
and Horiguchi, reporting the ratio (RAH) and difference (DAH)
contrasts.ahr_fast(): Kalbfleisch-Prentice average hazard ratio
between two groups over a restricted interval, the estimator used by
Dormuth et al. (2024) for sample-size calculation under non-proportional
hazards, with a test on the group-share scale and an equivalent test and
confidence interval on the log scale.survdiff_fast() gains weighted log-rank tests
(Fleming-Harrington, modestly-weighted, Gehan-Breslow, Tarone-Ware) and
stratified and stratified-weighted variants, all sharing the single-scan
C++ backend.simdata_fast() extended with optional subgroups defined
by a prevalence specification and a flexible accrual specification:
a.rate gives absolute accrual rates (with the end of an
open final interval solved from the total when a trailing rate is
supplied) and a.prop gives accrual proportions, with
deterministic per-interval accrual counts. The entire generation
pipeline runs in a single C++ kernel that materializes the output data
frame once. It can also generate two correlated time-to-event endpoints
(for example progression-free and overall survival) from an
illness-death model with three transition hazards and optional treatment
switching, reducing to the Fleischer maximal-independence model when the
post-event hazard equals the direct terminal hazard. A vector
n of length greater than two together with a per-arm
survival list generates a multi-arm trial, each arm produced with the
single-group kernel over a common accrual window and labeled 1 to
length(n), for analysis as pairwise contrasts against a
shared control.analysis_fast(): interim or sequential analysis of
simulated data at one or more looks, defined by target event counts or
calendar times, computed by a fused C++ kernel that reuses the analysis
cores of the standalone functions. Supports subgroup analyses.pairwise_fast(): runs analysis_fast() for
each experimental arm against a shared control on multi-arm data, at
either fixed calendar looks or the per-simulation cutoffs of a
designated primary contrast, and stacks the results with an optional
Bonferroni adjustment across contrasts.simsummary_fast(): operating-characteristic summary
(rejection and futility rates, stopping-look distribution, expected
timing) from analysis_fast() output and supplied
group-sequential boundaries, with a print() method that
lays the results out as a group-sequential design report.gen_scenario_fast(): assembles one or more two-group
scenarios into a scenario_fast object for design-stage
exploration, with a plot() method that draws the analytic
survival curves and the piecewise hazard ratio of each scenario and a
print() method that summarizes the medians, the start and
end hazard ratios, and whether the curves cross.kmcurve_fast(): builds the Kaplan-Meier curves of a
single trial realization (for example one replicate of
simdata_fast()) into a kmcurve_fast object,
with a plot() method that draws the curves with optional
restricted-mean shading and a smoothed time-varying hazard-ratio panel,
and a print() method that summarizes the events and
medians.print() method, and the print methods share a unified
display format.C++ via
Rcpp for use inside large simulation loops.survfit_fast(): single-time-point Kaplan-Meier
estimator with Greenwood standard error and plain / log / log-log
confidence intervals. The C++ backend locates the evaluation cutoff via
binary search and accumulates the Kaplan-Meier product and Greenwood
variance sum in a single scan over event positions. Returns an object of
class "survfit_fast" with a print()
method.survdiff_fast(): log-rank test returning a one-sided
Z-score or a two-sided chi-square statistic. The C++ backend uses a
two-pointer merge scan over pooled sorted vectors, eliminating the rank
construction, tabulate(), and reverse cumulative sum
operations of the standard implementation. Returns an object of class
"survdiff_fast" with a print() method.coxph_fast(): closed-form hazard ratio estimator via
the Pike-Halley Estimator method with Wald confidence interval. The C++
backend performs group splitting, at-risk counting, and
per-distinct-event-time accumulation in a single pass. Returns an object
of class "coxph_fast" with a print()
method.simdata_fast(): clinical trial data simulator
supporting one- and two-group designs, piecewise uniform accrual, and
simple and piecewise exponential survival and dropout times. C++
backends handle piecewise sampling and two-group interleaving, and
random number generation uses dqrng.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.