Computes the sBIC for various singular model collections including:
    binomial mixtures, factor analysis models, Gaussian mixtures,
    latent forests, latent class analyses, and reduced rank regressions.

This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review).
   In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. 
   Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.

Parameter inference methods for models defined implicitly using a random simulator. Inference is carried out using simulation-based estimates of the log-likelihood of the data. The inference methods implemented in this package are explained in Park, J. (2025) <doi:10.48550/arxiv.2311.09446>. These methods are built on a simulation metamodel which assumes that the estimates of the log-likelihood are approximately normally distributed with the mean function that is locally quadratic around its maximum. Parameter estimation and uncertainty quantification can be carried out using the ht() function (for hypothesis testing) and the ci() function (for constructing a confidence interval for one-dimensional parameters).

Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.

A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). 
  Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 'Bar-Hen et al.' (2020) <doi:10.48550/arXiv.1807.10138>.

Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.