Changes in version 2.0.0 (2026-09-12)                  

Major rewrite of the package. The statistical model and the user
interface are unchanged, so existing analysis scripts keep working, but
the numerical core, the graphics and the documentation have been
rebuilt, and several tools for interpreting a fit have been added.

Numerical core

  - The Fortran 77 core has been rewritten in C++ using Rcpp and
    RcppArmadillo. Results are identical to version 1.0-5 (verified by a
    regression test suite against the original implementation), and
    fitting is roughly 6 times faster for the plain model, 7 times for
    the frailty model and up to 45 times for the jackknife.
  - The limit of 200 recurrences per subject, imposed by the static
    arrays of the Fortran code, is gone.
  - Jackknife standard errors are computed in parallel with OpenMP where
    available. The number of threads honours OMP_NUM_THREADS and can be
    capped with the new gcmrecThreads().
  - Third-party numerical routines with licences incompatible with the
    GPL have been removed. The one-dimensional maximiser used by maxXi =
    "Brent" is now an implementation of Brent's algorithm working on the
    log scale, which is more stable than the previous version.

New features

  - mcf() estimates the mean cumulative function of recurrent events,
    with pointwise confidence bands and optional grouping, plus plot()
    and print() methods.
  - predict() returns the linear predictor, the relative risk or the
    survivor and cumulative hazard curves of given covariate profiles;
    plotPredict() draws them.
  - anova() performs likelihood ratio tests between nested models, and
    in particular tests whether alpha = 1 (no effect of accumulating
    occurrences). It refuses comparisons that are not valid likelihood
    ratio tests; see ?anova.gcmrec.
  - plotForest() draws the hazard ratios with their confidence
    intervals.
  - plotBaseline() compares the baseline functions of several fits on
    the same axes.
  - coef(), vcov() and logLik() methods, so AIC() and similar functions
    now work on a fit.
  - as_gcmrec_data() is a new S3 generic that normalises the input data.
    As a result, gcmrec() and graph.caltimes() accept the legacy list
    format directly, without calling List.to.Dataframe() first, and
    support for further input formats only requires a new method.
  - theme_gcmrec() gives the package graphics a consistent look and can
    be applied to a user's own plots.
  - gcmrec() gains keep.data, which stores the prepared data in the
    fitted object so that anova() can refit the model.

Graphics

  - All plotting functions return ggplot objects, which can be restyled
    with the usual ggplot2 syntax.
  - plot() on a fit now draws a pointwise confidence band around the
    baseline function.
  - graph.caltimes() has been redrawn as an event chart, with a
    follow-up segment per subject and a marker for the end of follow-up.
    Its sortevents argument, which previous versions accepted but
    ignored, now orders subjects by follow-up (default) or by number of
    events.
  - lines() on a fit is deprecated, since plot() no longer draws on a
    base graphics device; use plotBaseline() with a list of models.

Bug fixes

  - The baseline functions estimated with rhoFunc = "Identity" were
    computed from an incomplete coefficient vector, which read past the
    end of the vector in the Fortran code. They are now correct.
  - The jackknife of the frailty model passed the full offset and
    frailty vectors to each leave-one-out refit, so the values were
    misaligned with the reduced data.
  - summary() dropped the first covariate coefficient when rhoFunc =
    "Identity", mistaking it for alpha.
  - plot() and lines() relied on partial matching of $Surv and $Lam to
    reach the Survival and Lambda components of a fit.
  - The log-likelihood of a frailty fit was labelled as marginal; it is
    the partial log-likelihood conditional on the estimated frailties,
    and is labelled as such.
  - gcmrec() no longer emits a spurious warning from model.matrix()
    about the contrasts argument.

Documentation and infrastructure

  - All exported functions are documented with roxygen2, with runnable
    examples, and the R sources are split one file per function.
  - New vignette walking through a complete analysis:
    vignette("gcmrec").
  - Test suite based on testthat, including regression tests that check
    the C++ core against results produced by the original Fortran code.
  - Data sets are shipped in .rda format with lazy loading.
  - Native routines are registered, and the package declares its imports
    explicitly.

                        Changes in version 1.0-5                        

  - Last version released on CRAN, in 2012, with the numerical core
    written in Fortran 77.