An MCMC algorithm for simultaneous feature selection and classification, 
    and visualization of the selected features and feature interactions. 
    An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.

Estimation and inference for parameters 
        in a Gaussian copula model,
        treating the univariate marginal distributions as nuisance
        parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. 
        This package also provides a
        semiparametric imputation procedure for missing multivariate
        data.

Computes a simple blinding index for randomized controlled trials introduced in Petroff, Bacak, Dagres, Dilk, Wachter: A simple blinding index for randomized controlled trials. Contemp Clin Trials Commun. 2024 Nov 26;42:101393. <doi:10.1016/j.conctc.2024.101393>. PMID: 39686958.

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).