Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

A system for writing hierarchical statistical models largely
    compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models
    and do basic R-style math, and compiling both models and nimbleFunctions via
    custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace
    Approximation, deterministic nested approximations, Monte Carlo Expectation
    Maximization, and some other tools.
    The nimbleFunction system makes it easy to do things like implement new MCMC
    samplers from R, customize the assignment of samplers to different parts of
    a model from R, and compile the new samplers automatically via C++ alongside
    the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language
    by making it extensible: New distributions and functions can be added,
    including as calls to external compiled code. Although most people think
    of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models,
    one can use 'NIMBLE' for writing arbitrary other kinds of model-generic
    algorithms as well. A full User Manual is available at <https://r-nimble.org>.

Functions for adaptive parallel tempering (APT) with NIMBLE models. Adapted from 'Lacki' & 'Miasojedow' (2016) <DOI:10.1007/s11222-015-9579-0> and 'Miasojedow, Moulines and Vihola' (2013) <DOI:10.1080/10618600.2013.778779>.

Provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the 'nimble' modelling framework.  It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.