NEWS

rpsurv 0.8.1

tvecurve() and nlecurve() now return data frames of class "tvecurve" and "nlecurve", which keep the covariate, reference value and scale as attributes.
New plot() methods for both: the hazard ratio (or log effect) against time or against the covariate, with its 95% confidence band, a reference line at no effect and a log y axis by default. add = TRUE overlays curves.

New nle and nle.df arguments to rpsurv(): a smooth non-linear effect g(x) for numeric covariates, modelled as a restricted cubic spline in the covariate (knots at its range and equally spaced centiles, basis rescaled to [0, 1] for conditioning). It is specified separately from the time-varying effect (tve, tve.df), and a covariate may have both, giving g(x) + beta(t) x. nle.df = 1 reproduces the linear fit exactly.
tve.df and nle.df accept a single value or a vector named by covariate.
New nlecurve(): g(x) - g(ref) with a delta-method 95% confidence interval (the log hazard ratio versus ref on the hazard scale when the covariate is not also time-varying).
New tvecurve(): the time-varying effect, either as the log instantaneous hazard ratio per unit increase (type = "hr", matching predict(type = "hr")) or as the coefficient function beta(t) on the linear-predictor scale (type = "link"). For a covariate in both nle and tve, tvecurve() and nlecurve() return the two components separately, and their sum equals the total log hazard ratio.

CRAN resubmission: added a <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().

Added 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").

Bundled three datasets, each chosen for a specific package feature rather than as a generic example: 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.
Added a detailed README.md with real executed output (fit/summary/ predict, time-varying effects, diagnostics, and the rstpm2/flexsurv speed benchmark table).
Added a GitHub Actions R-CMD-check.yaml workflow and matching README badges.
Fixed .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.
Added cran-comments.md for the first CRAN submission.

Standardized author name casing to "El Badisy" (title case) in Authors@R and citation-facing fields, matching the rest of the package suite.

Delegated the restricted cubic spline basis (rcs_basis()) to a C++ implementation (rcs_basis_cpp()), rather than R's vectorised-but- interpreted arithmetic.
Cached the fused log-likelihood/gradient C++ call keyed on the parameter vector, since optim(method = "BFGS") otherwise evaluates fn and gr separately at every trial point, doubling data passes for the same parameter vector.
Closed-form starting values are now computed from a bounded (default 20,000-row) subsample of the Kaplan-Meier curve, avoiding an O(n log n) sort of the full data purely for initialization.
Added a reproducible speed benchmark (data-raw/benchmark.R) against rstpm2::stpm2 and flexsurv::flexsurvspline, plus a full vignette covering the model, API, and benchmark.

Added support for left-truncated / counting-process data (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.
Added deviance residuals and km_compare_plot() (fitted vs. Kaplan-Meier calibration diagnostic).
Renamed 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).

Added predict.rpsurv() (type = "survival"/"hazard"/"cumhaz"/ "link", with optional delta-method confidence limits) and plot.rpsurv().
Added 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.
Added Cox-Snell and martingale residuals plus coxsnell_plot().
Validated against 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.

Added the parallel C++ log-likelihood and its analytic gradient (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.

Initial implementation: restricted cubic spline basis in log time (rcs_basis()) and the Royston-Parmar design matrix builder, following Royston and Parmar (2002).