<doi:10.1002/sim.1203>
link for the Royston and Parmar (2002) reference in the
DESCRIPTION Description field, and added
\value documentation for coxsnell_plot(),
km_compare_plot(), and plot.rpsurv().predict.rpsurv(type = "hr"): the model-implied
instantaneous hazard ratio between a contrast covariate profile
(newdata) and a reference profile (newdata0),
computed as the ratio of two type = "hazard" predictions.
This is the correct contrast under a time-varying effect
(tve), where exp(eta1 - eta0) is not the
instantaneous hazard ratio in general. Matches exp(coef)
exactly under proportional hazards and agrees with
rstpm2::predict(..., type = "hr") under tve to
within Monte Carlo/spline-basis tolerance. se.fit is not
yet supported for type = "hr" (nor for
"hazard").brcancer (baseline
fitting example, identical to rstpm2::brcancer),
veteran (a well-known non-proportional-hazards effect,
illustrating tve, identical to
survival::veteran), and heart (Stanford heart
transplant data, already in counting-process format with a time-varying
covariate, identical to survival::heart). Removes the need
for rstpm2 to be installed just to run the README/vignette
examples.README.md with real executed output
(fit/summary/ predict, time-varying effects, diagnostics, and the
rstpm2/flexsurv speed benchmark table).R-CMD-check.yaml workflow and
matching README badges..Rbuildignore: data-raw/ (containing
a cached benchmark .rds) was shipping in the source tarball
as a non-standard top-level directory; also ignored
cran-comments.md and *.Rcheck.cran-comments.md for the first CRAN
submission.Authors@R and citation-facing fields, matching the rest of
the package suite.rcs_basis()) to a C++ implementation
(rcs_basis_cpp()), rather than R’s vectorised-but-
interpreted arithmetic.optim(method = "BFGS") otherwise
evaluates fn and gr separately at every trial
point, doubling data passes for the same parameter vector.data-raw/benchmark.R) against rstpm2::stpm2
and flexsurv::flexsurvspline, plus a full vignette covering
the model, API, and benchmark.Surv(start, stop, status)), enabling genuine time-varying
covariates (as opposed to time-varying effects, see
tve): each interval contributes
log S(stop) - log S(start), correctly conditioning on
survival to start under the covariate values of the
previous interval. The same mechanism handles ordinary left
truncation (delayed entry) when covariate values don’t change.km_compare_plot() (fitted
vs. Kaplan-Meier calibration diagnostic).tvc to tve (time-varying effect)
throughout the fitting, predict, and summary API, to avoid confusion
with the new time-varying-covariate support above (a different,
data-representation- level concept).predict.rpsurv()
(type = "survival"/"hazard"/"cumhaz"/
"link", with optional delta-method confidence limits) and
plot.rpsurv().print/summary/coef/vcov/logLik/AIC/BIC/confint
methods, matching coxph()’s output style: interpretable
covariate effects (hazard/odds ratios, Wald tests) are reported
separately from the baseline spline’s nuisance coefficients.coxsnell_plot().rstpm2::stpm2 and
flexsurv::flexsurvspline on rstpm2::brcancer
across all three link scales (hazard, odds, normal); coefficients,
standard errors, and log-likelihoods agree to 4-5 decimal places.RcppParallel::parallelReduce), and rpsurv(),
the main model-fitting function, with support for a time-varying effect
(tve) via its own spline in log time multiplying the
covariate.rcs_basis()) and the Royston-Parmar design matrix builder,
following Royston and Parmar (2002).
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