TemporalHazard 1.0.3

Bug fixes / CRAN compliance

• hzr_bootstrap() no longer touches .GlobalEnv directly. The 1.0.2 oldseed/on.exit()/assign(".Random.seed", ...) save-restore wrapper added in 1.0.2 violated CRAN policy on writing to .GlobalEnv and has been removed. When seed is supplied the function simply calls set.seed(seed) (the documented R API for seeded reproducibility); the @param seed documentation now notes that the caller’s RNG state is not restored on exit. With seed = NULL (the default) the function does not call set.seed() at entry, so it starts from the caller’s current RNG state; the bootstrap still consumes random numbers and advances that state in the usual way.

TemporalHazard 1.0.2

Bug fixes / CRAN compliance

• The golden-fixture generators (.hzr_create_*_golden_fixture(), previously R/golden_fixtures.R) have been moved out of the package to data-raw/golden_fixtures.R. They are maintainer-only helpers for regenerating the bundled inst/fixtures/*.rds reference outputs and are not part of the installed package, so they are no longer shipped, checked, or user-reachable. This resolves the home-filespace concern at its root: the earlier fallback resolved to system.file("fixtures", ...) — i.e. the installed package directory — whenever the package was installed, so the 1.0.1 “falls back to tempdir()” fix did not actually prevent writing to the user library. The bundled .rds fixtures still ship and the parity tests still read them via system.file().
• .hzr_generate_golden_fixture() (the C-binary reference writer in R/parity-helpers.R, which shares a file with test-time helpers and so was kept in the package) now takes a required output_dir argument with no default path.
• Removed the remaining hardcoded seed = 42 literals from the relocated generators; recorded fixture metadata reflects the actual seed argument passed (NULL by default, so no seed is set inside the function).
• hzr_bootstrap() no longer leaves the caller’s random-number stream altered when seed is supplied: the global .Random.seed is saved before set.seed() and restored via on.exit(), matching the fixture generators. Bootstrap reproducibility under a given seed is unchanged.

TemporalHazard 1.0.1

Bug fixes / CRAN compliance

• Added \value documentation to all exported functions that were missing it: hazard(), coef.hazard(), vcov.hazard(), print.hzr_calibrate(), print.hzr_deciles(), print.hzr_gof(), and print.hzr_kaplan().
• Internal fixture generators (R/golden_fixtures.R) no longer set a specific seed unconditionally. Generators now accept an optional seed argument; when provided, the global RNG state is saved and restored via on.exit().
• Default output_dir for fixture generators falls back to tempdir() instead of the package source directory, keeping the home filespace unmodified.

TemporalHazard 0.9.8

New features

• Delta-method confidence limits on predict.hazard() — Phase 4g of the development plan lands. Two new arguments: se.fit = FALSE and level = 0.95. When se.fit = TRUE, the return value becomes a data frame with columns fit, se.fit, lower, upper.
  • Weibull and multiphase use closed-form Jacobians (dH/dtheta, dexp(eta)/dtheta, deta/dtheta); exponential / log-logistic / log-normal fall back to numDeriv::jacobian on a per-call cumhaz closure.
  • Transforms match SAS HAZARD (hzp_calc_haz_CL.c / hzp_calc_srv_CL.c): hazard and cumulative_hazard use log-scale CLs; survival uses log(-log S) CLs (equivalent to log-cumhaz) so 0 <= lower <= upper <= 1; linear_predictor is symmetric on the natural scale.
  • Fixed-shape / CoE multiphase fits produce meaningful CLs — the delta-method sandwich is restricted to the free-parameter submatrix of vcov, treating fixed parameters as known-with-zero-variance.
  • Backward compatible: se.fit = FALSE (default) preserves the pre-0.9.8 scalar-vector / decompose-data-frame return shape.

TemporalHazard 0.9.7

New features

• Counting-process / repeating-events likelihood wired up — Phase 4f of the development plan lands. Surv(start, stop, event) with any start > 0 is now accepted. The Weibull and multiphase log-likelihoods apply H(stop) - H(start) to event and right-censored terms; the trivial start = 0 case degenerates to H(stop) and recovers the plain-Surv fit exactly. Splitting each row into contiguous epochs preserves both the log-likelihood and the MLE to optimizer tolerance (split-invariance).
• Weibull + multiphase analytic gradients handle H(start). The closed-form Weibull score adds a -d H(start)/d theta term per row (guarded at start = 0). The multiphase analytic gradient computes per-phase Phi_j(start) and its shape derivatives, then adds +w_H_start * mu_j * dPhi_j(start) to each parameter’s score; G3 phase derivatives at start use the same finite-difference machinery as at stop.
• 0.9.5 narrowing removed. The hazard() guard that rejected counting-process Surv(start, stop, event) with any start > 0 is gone.

TemporalHazard 0.9.6

New features

• weights now supported for all distributions — Phase 4e of the development plan lands. The exponential, log-logistic, and log-normal likelihoods and their analytic gradients now apply row weights to every censoring term (event, right-censored, left-censored, interval-censored). The 0.9.5 guard in hazard() that rejected weights for dist %in% c("exponential", "loglogistic", "lognormal") has been removed. Fits with integer weights reproduce the row-duplicated fit to optimizer tolerance across all five distributions.
• Conservation of Events now honours weights. .hzr_conserve_events() and .hzr_select_fixmu_phase() take an optional weights argument; the multiphase optimizer threads it through so per-phase cumulative hazards are summed on the same scale as the (weighted) observed event count. CoE no longer auto-disables when weights are non-uniform — the dimension reduction stays on and the MLE matches the full-dim path.

Bug fixes

• Multiphase analytic gradient now applies weights. .hzr_gradient_multiphase() accepted neither weights nor its downstream equivalents: the per-row score weights w_H / inv_h were set to ±1 and the interval-censored finite-difference correction summed an unweighted LL. Weighted multiphase fits therefore optimised a weighted objective with an unweighted score; BFGS line search still converged near the correct MLE but the final gradient norm did not go to zero. All three paths now honour row weights, and the optimizer’s gradient_fn wrapper (including the all-zero numeric fallback and the CoE wrapper) forwards weights consistently. Regression test covers weighted analytic vs numerical gradient parity. Surfaced by Copilot review on PR #18.

TemporalHazard 0.9.5

New features

• Stepwise covariate selection — hzr_stepwise() runs forward, backward, or two-way stepwise selection on an existing hazard fit using Wald p-values or AIC deltas as the entry / retention criterion. Phase-specific entry is supported for multiphase models: a covariate can enter one phase and not another. Defaults match SAS PROC HAZARD (SLENTRY = 0.30, SLSTAY = 0.20); AIC mode uses ΔAIC < 0 uniformly. SAS-style MOVE oscillation guard freezes variables that enter + exit more than max_move times. Returns an object of class c("hzr_stepwise", "hazard") with a $steps selection trace, scope record, and elapsed timer. Implements the core algorithm from C HAZARD stepw.c / backw.c.

Bug fixes

• Multiphase convergence after weights/repeating-events merge — restored multiphase optimization that regressed in 0.9.4: three interacting defects in the new weights threading (dup-arg collision in the multiphase / Weibull closures, positional-arg corruption in every distribution’s gradient call) made every optimizer iteration error silently inside tryCatch. Diagnosed and fixed via commit 73b4657.
• Weibull analytic gradient now applies weights — both .hzr_gradient_weibull() and the grad_internal closure inside .hzr_optim_weibull() accepted weights as a formal but did not apply it to the score vector. The optimizer still converged via line search on the (weighted) log-likelihood, but the gradient direction was wrong and the final gradient norm did not go to zero. Both gradient paths now weight the event indicator and cumulative hazard building blocks. Fits with integer weights reproduce the equivalent row-duplicated fit to optimizer tolerance.

Scope change

• weights is now only accepted for dist = "weibull" and dist = "multiphase". The 0.9.4 NEWS claimed weights were threaded through all distribution-specific likelihoods; in fact the exponential, log-logistic, and log-normal single-distribution paths accepted the formal but never applied it, so the fit was silently unweighted. hazard() now raises an explicit error when weights is supplied with one of those distributions rather than returning an unweighted fit. Full support for the remaining single-dist paths is tracked in inst/dev/DEVELOPMENT-PLAN.md Phase 4e.
• Conservation of Events is auto-disabled when weights are not all 1. .hzr_conserve_events() receives the weighted event count as its target but sums per-phase cumulative hazards across rows without applying weights, so Turner’s adjustment comes out on a mismatched scale. The multiphase optimizer now detects non-unit weights and skips the CoE dimension reduction, falling through to the (correctly weighted) full-dimensional path. Fits are still correct; they just don’t benefit from the one-parameter analytical closed-form solve. Weighted CoE wire-up is tracked alongside the other weights completion work in inst/dev/DEVELOPMENT-PLAN.md Phase 4e.
• Repeating-events / counting-process notation narrowed. Surv(start, stop, event) with start > 0 is no longer accepted by hazard(). The 0.9.4 NEWS claimed each epoch contributed H(stop) - H(start) to the likelihood, but downstream likelihoods only read time_lower for interval-censored rows (status == 2); counting-process rows (status in {0, 1}) were silently scored with H(stop) alone, so any fit with nonzero entry times was silently wrong. hazard() now raises an explicit error. The trivial case Surv(0, t, d) – equivalent to Surv(t, d) – continues to work. Full wire-up of H(stop) - H(start) for all distribution paths is tracked in inst/dev/DEVELOPMENT-PLAN.md Phase 4f.

TemporalHazard 0.9.4

New features

• Observation weights — weights argument in hazard() applies Fisher weighting to the log-likelihood for dist = "weibull" and dist = "multiphase". Each observation’s contribution is multiplied by its weight, enabling severity-weighted event analyses. Implements the SAS WEIGHT statement. The original 0.9.4 entry claimed coverage of all distribution paths; the 0.9.5 patch corrected the claim and fixed a gradient wire-up bug in the Weibull path.
• Repeating events — Surv(start, stop, event) start-stop notation is parsed. The original 0.9.4 entry claimed each epoch contributed H(stop) - H(start) to the likelihood, but the downstream likelihoods never applied the lower bound for counting-process rows; the 0.9.5 patch narrowed the feature to the trivial start = 0 case and added an explicit error for nonzero starts.

TemporalHazard 0.9.3

New features

• hzr_deciles() — Decile-of-risk calibration function comparing observed vs. expected event counts across risk groups with chi-square GOF testing. Implements the SAS deciles.hazard.sas macro workflow.
• hzr_gof() — Goodness-of-fit function comparing parametric predictions against nonparametric (Kaplan-Meier) estimates with observed vs. expected event counting. Implements the SAS hazplot.sas macro workflow.
• hzr_kaplan() — Kaplan-Meier survival estimator with logit-transformed confidence limits that respect the [0, 1] boundary, interval hazard rate, density, and restricted mean survival time (life integral). Implements the SAS kaplan.sas macro output structure.
• hzr_calibrate() — Variable calibration function for assessing functional form before model entry. Groups a continuous covariate into quantile bins and applies logit, Gompertz, or Cox link transforms. Supports stratification via the by parameter. Implements the SAS logit.sas and logitgr.sas macros.
• hzr_nelson() — Wayne Nelson cumulative hazard estimator with lognormal confidence limits. Supports weighted events for severity-adjusted repeated event analyses. Implements the SAS nelsonl.sas macro.
• hzr_bootstrap() — Bootstrap resampling for hazard model coefficients with bagging support (fractional sampling). Returns per-replicate estimates and summary statistics (mean, SD, percentile CI). Implements the SAS bootstrap.hazard.sas macro workflow.
• hzr_competing_risks() — Competing risks cumulative incidence using the Aalen-Johansen estimator with Greenwood variance. Handles any number of competing event types. Implements the SAS markov.sas macro.
• Conservation of Events (CoE) — Turner’s theorem is now integrated into the multiphase optimizer. One phase’s log_mu scaling parameter is solved analytically at each iteration, reducing the optimization dimension by 1 and improving numerical stability and convergence. Enabled by default; disable with control = list(conserve = FALSE). Implements the core algorithm from C HAZARD setcoe.c / consrv.c.
• New vignette: “Complete Clinical Analysis Walkthrough” — end-to-end workflow from Kaplan-Meier baseline through validated multivariable model, mirroring the SAS HAZARD analytical sequence.

Improvements

• Multi-start optimizer now respects user-set RNG seeds for reproducibility (removed set.seed(NULL) that was actively breaking determinism).
• Vignette metadata normalized to YAML vignette: key across all 8 files.
• fit parameter documentation corrected to state default is FALSE.
• README now includes key capabilities table and development plan link.

TemporalHazard 0.9.1

New features

• G3 late-phase decomposition (hzr_phase("g3", ...)) now fully integrated into the multiphase optimizer, Hessian, and prediction pipeline.
• fixed = "shapes" parameter in hzr_phase() allows fixing shape parameters during estimation (matching C/SAS HAZARD workflow of estimating only log-mu scale parameters).

Bug fixes

• summary.hazard() now correctly reports standard errors when some parameters are fixed. Previously, anyNA(vcov) rejected the entire variance-covariance matrix when fixed parameters had NA entries.
• print.summary.hazard() coefficient table now shows the correct label for G3 phases (was printing empty parentheses).
• print.summary.hazard() phase listing now uses the phase name in CDF labels (e.g., “cdf (late risk)”) instead of hardcoded “early risk”.
• SAS missing value markers (.) in CSV datasets are now handled via na.strings = c("NA", ".") in data-raw/make_data.R, preventing numeric columns from being read as character.

Documentation

• Seven Quarto vignettes: getting-started, fitting-hazard-models, prediction-visualization, inference-diagnostics, mathematical-foundations, package-architecture, and sas-to-r-migration.
• Roxygen examples now include both single-phase and multiphase models.
• README switched to self-contained CABGKUL examples with G3 late phase.
• Dataset axis labels corrected to “Months” (not “Years”).

Infrastructure

• CI workflows updated to use roxygen2::load_pkgload for lazy data compatibility.
• Added lintr CI workflow with .lintr configuration.
• pkgdown action bumped to peaceiris/actions-gh-pages@v4.
• Added use-public-rspm: true to all CI workflows.
• Added lintr to Suggests.

TemporalHazard 0.9.0

New features

• Multiphase engine: N-phase additive cumulative hazard models via dist = "multiphase" with hzr_phase() specification.
• hzr_decompos() parametric family implementing the three-parameter temporal decomposition of Blackstone, Naftel, and Turner (1986).
• Multi-start optimizer with Hessian-based variance-covariance estimation.
• C binary parity tests against the KUL CABG reference dataset.
• Five clinical reference datasets: avc, cabgkul, omc, tga, valves.

TemporalHazard 0.1.0

New features

• Single-phase engine: Weibull, exponential, log-logistic, and log-normal distributions with formula interface.
• hazard() API with predict(), summary(), coef(), vcov() S3 methods.
• Golden fixture regression testing system.
• Numerically stable helper primitives (hzr_log1pexp, hzr_log1mexp, hzr_clamp_prob).

TemporalHazard 0.0.0.9000

• Initial package scaffold.
• Added numerically stable helper primitives.
• Added baseline unit tests and CI workflow.