Changes in version 0.1.0 (2026-09-15)                  

  - Initial CRAN submission.
  - Implemented the autorelevated family of distributions (PDF, CDF,
    survival, hazard, exact quantile function via Lambert W_-1, and
    random generation) for ten two-parameter baseline lifetime
    distributions: Weibull, Lomax, Burr XII, Gompertz, Log-Logistic,
    Chen, Exponentiated Exponential, Power Lindley, Log-normal, and
    Gamma (dautorelevate(), pautorelevate(), sautorelevate(),
    haautorelevate(), qautorelevate(), rautorelevate()).
  - Added Maximum Likelihood, Maximum Product of Spacings, Least
    Squares, Weighted Least Squares, and Cramer-von Mises estimation
    methods via fit_autorelevate(), reporting AIC, BIC, CAIC, and HQIC
    for MLE method.
  - Added fit_all_methods() and compare_families() for comparing
    estimation methods and all ten members of the autorelevated family.
  - Added ttt_plot() for Total Time on Test plots (Aarset, 1987), a
    standard tool to determine the hazard function shape.
  - Added print(), summary(), and plot() methods for autorelevate_fit
    objects, and a print() method for autorelevate_compare objects.
  - Added a bundled real dataset, bladder_cancer (Lee & Wang, 2003; n
    = 128 bladder cancer remission times), with no dependency on
    external data packages. The MLE fit of the Autorelevated Weibull
    family to this dataset reproduces the published results of Dileep
    Kumar, Shabeer, and Sankaran (2025) to within optimizer tolerance.
  - Added an internal input-validation layer used by every exported
    function, giving clear error messages for invalid dist, p1, p2, or
    data instead of silent NaN/Inf propagation, and a warning (rather
    than silent truncation) when non-finite or non-positive observations
    are dropped from data.
  - Made optimization in fit_autorelevate() robust via a data-adaptive
    starting-value grid search and an automatic Nelder-Mead/restart
    fallback if the primary optimizer fails to converge.
  - Fully vectorized the internal Lambert W_-1 solver
    (.lambert_w_minus1()) for performance at scale, with a convergence
    check and warning.
  - Fixed a rare edge case in the internal Lambert W_-1 solver where an
    extreme quantile request (p within 1e-10 of 1) could silently return
    a slightly inaccurate result; verified against independent
    high-precision root-finding to now be accurate to machine precision
    across the full domain.