Changes in version 0.1.0 (2026-04-24)                  

New features

  - fit_inad() gains a nb_inno_size_ub argument (default 50) that caps
    the upper bound of the negative-binomial innovation size parameter
    during optimization, improving numerical stability for near-Poisson
    data.
  - test_order_gau() accepts order_null and order_alt as convenience
    aliases for p and the absolute alternative order; both are also
    returned in the result object.

Bug fixes

  - ci_inad(): fixed a sign error in the observed Fisher information for
    the negative-binomial innovation size parameter; the Hessian term (r
    + u) / (r + λ)² was added instead of subtracted, producing
    confidence intervals that were too wide.
  - ci_inad(): the numerical second derivative for nb_inno_size CIs now
    retries with progressively smaller step sizes (×0.1, ×0.01) before
    falling back to NA, avoiding spurious failures when the default step
    lands in a non-finite region.
  - test_homogeneity_inad(): degrees of freedom for LRT tests involving
    innovation = "nbinom" are now computed from the actual number of NB
    size parameters in the fitted models rather than assuming a fixed
    count of 1. This corrects LRT statistics and p-values whenever
    nb_inno_size is fitted as a time-varying vector.
  - ci_inad() tau profile CI: nb_inno_size (negative-binomial innovation
    dispersion) is now held fixed at its full-model MLE during profile
    refits, consistent with the constrained-fit paradigm used throughout
    the package. Previously it was re-optimised as a nuisance parameter,
    which could widen the interval to the point of crossing zero even
    when the LRT clearly rejects the null (Variant 1 vs Variant 2 fix).
  - ci_inad() tau profile CI: the bracket search in .ci_tau_profile_inad
    no longer imposes an artificial upper cap (max(|tau_mle| + 1, 1)) on
    the search range. The maximum bracket iterations are increased
    from 20 to 50 and the initial step size is set to max(0.1, |tau_mle|
    * 0.2), preventing the search from stalling for large or near-zero
    MLEs.

Initial release

  - Initial CRAN submission candidate for Gaussian AD, categorical AD,
    and INAD workflows.
  - Added/expanded examples for key user-facing modeling functions
    (fit_*, em_*, simulate_*, logL_*).
  - Refreshed missing-data notes and harmonized documentation links and
    metadata.
  - logL_gau() default missing-data behavior is now na_action = "fail"
    (previously marginalization-first in earlier drafts). For missing
    inputs, pass na_action = "marginalize" or na_action = "complete"
    explicitly.
  - Added packaged datasets labor_force_cat (categorical labor-force
    sequences) and race_100km (continuous 100km race split times).