The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
<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).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.