Functions and classes to store, manipulate and summarise
    Monte Carlo Markov Chain (MCMC) samples. For more information see
    Brooks et al. (2011) <isbn:978-1-4200-7941-8>.

Fit multi-level models with possibly correlated
    random effects using Markov Chain Monte Carlo simulation.
    Such models allow smoothing over space and time and are useful in,
    for example, small area estimation.

Provides tools for computing Monte Carlo standard errors (MCSE) in
    Markov chain Monte Carlo (MCMC) settings (survey in <doi:10.1201/b10905>,
    Chapter 7). MCSE computation for expectation and quantile estimators is
    supported as well as multivariate estimations. The package also provides
    functions for computing effective sample size and for plotting Monte Carlo
    estimates versus sample size.

Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with Stan, NIMBLE, JAGS, and other software.

Provides functions for isotonic regression and classification
    when there are multiple independent variables. The functions solve the
    optimization problem using a projective Bayes approach with recursive
    sequential update algorithms, and are useful for situations with a
    relatively large number of covariates. Supports binary outcomes via a
    Beta-Binomial conjugate model ('miso', 'PBclassifier') and continuous
    outcomes via a Normal-Inverse-Chi-Squared conjugate model ('misoN').
    Parallel computing wrappers ('mcmiso', 'mcPBclassifier', 'mcmisoN') are
    provided that run the down-up and up-down algorithms simultaneously and
    return whichever finishes first. The estimation method follows the
    projective Bayes solution described in Cheung and Diaz (2023)
    <doi:10.1093/jrsssb/qkad014>.

Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of 'mc2d' to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, align multivariate mcnodes, automate the creation of mcnodes, visualise model structure, assess convergence, and perform sensitivity analysis. For more details see Ciria (2026) <https://nataliaciria.com/mcmodule/>.