Changes in version 0.1.0 (2026-05-27)                  

Initial release.

Distribution functions

  - dmhn(), pmhn(), qmhn(), and rmhn() provide density, distribution,
    quantile, and random generation for the Modified Half-Normal (MHN)
    distribution of Sun, Kong & Pal (2023).
  - All four functions are vectorised over both the evaluation argument
    and the parameters alpha, beta, gamma, following standard R
    recycling rules.
  - A ParamCache reuses the Fox--Wright Psi normalising constant across
    consecutive elements that share an (alpha, beta, gamma) triple, so
    grouped inputs are evaluated significantly faster than calling the
    functions inside an R loop.

Random generation

  - rmhn(..., method = "auto") (default) routes each parameter triple to
    the cheapest provably-correct sampler: closed-form shortcuts for the
    special cases, Sun et al. (2023, Algorithms 1 and 3) where they win,
    and the Gao & Wang (2025) Relaxed Transformed Density Rejection
    (RTDR) sampler elsewhere.
  - method = "rtdr" forces RTDR with its uniform 1/e acceptance bound.
  - method = "sun" forces Sun Algorithm 1 (gamma > 0, alpha > 1) or
    Algorithm 3 (gamma <= 0); Sun Algorithm 2 is intentionally not
    implemented and an unsupported combination triggers a clear error.

CDF and quantile

  - pmhn() uses the Sun et al. (2023, Lemma 1b) series in log space,
    truncated at the Sun et al. (2023, Supplementary Lemma 10(d))
    constructive bound K = max(K1, K2); the truncation residual is
    bounded by the user's tolerance divided by Psi.
  - For gamma < 0 the series uses sign-separated log-sum-exp + log-
    diff-exp accumulation and a runtime cancellation guard derived from
    the double-precision precision floor: when the relative cancellation
    loss would exceed the user's tolerance, pmhn() falls back to a
    peak-normalised Boost.Math quadrature (Gauss-Kronrod for alpha >= 1,
    tanh-sinh for alpha < 1) of the unnormalised density.
  - qmhn() inverts pmhn() via boost::math::tools::toms748_solve on the
    bracket [sqrt(eps), E(X) + 8 sqrt(Var(X))], doubling the upper end
    as needed.

Summary statistics

  - mhn_mean(), mhn_var(), mhn_skewness(), mhn_kurtosis(), and
    mhn_mode() evaluate the closed-form / recurrence-based expressions
    from Sun et al. (2023, Lemmas 2 and 3).

Tests and documentation

  - testthat suite with > 1,700 expectations covering goodness-of-fit
    (Kolmogorov-Smirnov), special-case identities, vectorised recycling,
    NA / NaN propagation, and the Sun / Gao & Wang acceptance bounds.
  - vignette("introduction", package = "mhn") walks through every
    exported function with runnable examples.
  - vignette("theory", package = "mhn") is the theoretical companion: it
    covers the MHN family and its special cases, the Fox--Wright Psi
    normalising constant, Algorithms 1 and 3 of Sun et al. (2023), the
    four-region Gao & Wang RTDR construction, and the rmhn(method =
    "auto") decision tree.
  - citation("mhn") returns three bibentry objects: the package, the Sun
    et al. (2023) paper, and the Gao & Wang (2025) paper.