Assist in reproducible retrospective (ex-post) harmonization
    of data, particularly individual level survey data, by providing tools
    for organizing metadata, standardizing the coding of variables, and
    variable names and value labels, including missing values, and
    documenting the data transformations, with the help of comprehensive
    s3 classes.

A collection of tools to import and structure the (currently) single-season
    event, game-log, roster, and schedule data available from <https://www.retrosheet.org>.
    In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse.
    This package does the parsing on those files, returning the requested data in the most
    practical R structure to use for sabermetric or other analyses.

Provide simple mechanism to repeatedly evaluate an expression until either it succeeds or timeout exceeded. It is useful in situations that random failures could happen.

Estimates the p-probability return curve proposed by Murphy-Barltrop et al. (2023) <doi:10.1002/env.2797>. Implements pointwise and smooth estimation of the angular dependence function introduced by Wadsworth and Tawn (2013) <doi:10.3150/12-BEJ471>.

Simplified scenario testing and sensitivity analysis,
    redesigned to use packages 'future' and 'furrr'. Provides
    functions for generating function argument sets using
    one-factor-at-a-time (OFAT) and (sampled) permutations.

Provides functions for the Bayesian analysis of extreme value
    models.  The 'rust' package <https://cran.r-project.org/package=rust> is
    used to simulate a random sample from the required posterior distribution.
    The functionality of 'revdbayes' is similar to the 'evdbayes' package
    <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain
    Monte Carlo ('MCMC') methods for posterior simulation.  In addition, there
    are functions for making inferences about the extremal index, using 
    the models for threshold inter-exceedance times of Suveges and Davison 
    (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) 
    <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for 
    the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') 
    distributions that deal appropriately with cases where the shape parameter 
    is very close to zero.