Changes in version 0.5.2 (2026-05-23)                  

  - Prototype constructors (dfr_exponential(), dfr_weibull(),
    dfr_gompertz(), dfr_loglogistic()) now prepend a family-specific S3
    subclass (e.g. "dfr_exponential") to the class chain. This enables
    downstream packages to dispatch on component type via inherits()
    rather than maintaining parallel lookup tables. Existing inherits(x,
    "dfr_dist") checks are unaffected (additive change).

                 Changes in version 0.5.1 (2026-03-19)                  

  - Initial CRAN release.
  - Added JOSS paper (paper.md, paper.bib).
  - Added community guidelines (CONTRIBUTING.md, CODE_OF_CONDUCT.md).
  - Added Zenodo archive (DOI: 10.5281/zenodo.18757479).

                 Changes in version 0.5.0 (2026-03-02)                  

Changes

  - Added helper distribution constructors: dfr_exponential(),
    dfr_weibull(), dfr_gompertz(), dfr_loglogistic() with analytical
    hazard, cumulative hazard, score, and Hessian functions where
    available.
  - Added diagnostic methods: residuals() (Cox-Snell and Martingale),
    plot() (survival, hazard, cumulative hazard), qqplot_residuals().
  - Added density() method (alias for pdf).
  - Added assumptions() method for listing model assumptions.
  - Added kaplan_meier() internal utility for empirical survival
    estimation.
  - Added support for left-censored observations (delta = -1).
  - Improved numerical stability in log-likelihood computation.
  - Removed femtograd dependency — users supply their own derivative
    functions via score_fn and hess_fn, or the package falls back to
    numDeriv.

                        Changes in version 0.1.0                        

Initial release.

Features

  - dfr_dist() constructor for creating distributions from hazard
    functions
  - Complete distribution interface: hazard(), cum_haz(), surv(), cdf(),
    pdf(), inv_cdf(), sampler()
  - Likelihood model interface: loglik(), score(), hess_loglik(), fit()
  - Support for right-censored survival data
  - Numerical integration for cumulative hazard
  - Inverse CDF sampling

Examples

Supports modeling complex hazard patterns:

  - Exponential (constant hazard)
  - Weibull (monotonic hazard)
  - Bathtub curves (infant mortality + useful life + wear-out)
  - Proportional hazards with covariates