Generates predictive distributions based on calibrating priors
    for various commonly used statistical models, including models with 
    predictors. Routines for densities, probabilities, quantiles, random
    deviates and the parameter posterior are provided. The predictions are
    generated from the Bayesian prediction integral, with priors chosen to
    give good reliability (also known as calibration). For homogeneous models,
    the prior is set to the right Haar prior, giving predictions which are
    exactly reliable. As a result, in repeated testing, the frequencies of
    out-of-sample outcomes and the probabilities from the predictions agree.
    For other models, the prior is chosen to give good reliability. Where
    possible, the Bayesian prediction integral is solved exactly. Where exact
    solutions are not possible, the Bayesian prediction integral is solved
    using the Datta-Mukerjee-Ghosh-Sweeting (DMGS) asymptotic expansion.
    Optionally, the prediction integral can also be solved using posterior
    samples generated using Paul Northrop's ratio of uniforms sampling package
    ('rust'). Results are also generated based on maximum likelihood, for
    comparison purposes. Various model selection diagnostics and testing
    routines are included. Based on "Reducing reliability bias in assessments
    of extreme weather risk using calibrating priors", Jewson, S., Sweeting, T.
    and Jewson, L. (2024); <doi:10.5194/ascmo-11-1-2025>.

Extends the fitdistr() function (of the MASS package) with several functions 
  to help the fit of a parametric distribution to non-censored or censored data. 
  Censored data may contain left censored, right censored and interval censored values, 
  with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), 
  the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit 
  estimation (MGE) and maximum spacing estimation (MSE) methods (available only for 
  non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. 
  Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction 
  to parametric estimation.