Computes the probability density and cumulative distribution functions of fourteen distributions used for the probabilistic hazard assessment. Estimates the model parameters of the distributions using the maximum likelihood and reports the goodness-of-fit statistics. The recurrence interval estimations of earthquakes are computed for each distribution.

Simulates and estimates the Exponential Random Partition Model presented 
    in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. 
    It can also be used to estimate longitudinal partitions, following the model 
    proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. 
    The model is an exponential family distribution on the space of partitions 
    (sets of non-overlapping groups) and is called in reference to the Exponential 
    Random Graph Models (ERGM) for networks.

Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.

Errors in data can be located and removed using validation rules from package 
   'validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>,
   chapter 7.

Support for measurement errors in R vectors, matrices and arrays:
    automatic uncertainty propagation and reporting.
    Documentation about 'errors' is provided in the paper by Ucar, Pebesma &
    Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a
    vignette; see 'citation("errors")' for details.

Provides a full pipeline from regularized or standard regression
    models (elastic net, linear models, generalized linear models, random
    forests) to informed Bayesian priors, structured forecast uncertainty
    decomposition (parameter / environmental / residual, plus a temporal
    component when the model carries an autocorrelation term), and forecast
    shelf life analysis (the quantification of when a forecast becomes
    uninformative). Designed for ecological and genomic forecasting with
    climate or environmental covariates. Methods build on Bürkner (2017)
    <doi:10.18637/jss.v080.i01> for Bayesian regression via 'Stan',
    Friedman, Hastie, and Tibshirani (2010) <doi:10.18637/jss.v033.i01>
    for elastic net regularization, Wright and Ziegler (2017)
    <doi:10.18637/jss.v077.i01> for random forests, and Vehtari, Gelman,
    and Gabry (2017) <doi:10.1007/s11222-016-9696-4> for leave-one-out
    cross-validation.