Produces an economic evaluation of a sample of suitable variables of
             cost and effectiveness / utility for two or more interventions,
             e.g. from a Bayesian model in the form of MCMC simulations.
             This package computes the most cost-effective alternative and
             produces graphical summaries and probabilistic sensitivity analysis,
             see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.

A Bayesian model averaging approach to causal effect estimation
    based on the BCEE algorithm. Currently supports binary or continuous
    exposures and outcomes. For more details, see 
    Talbot et al. (2015) <doi:10.1515/jci-2014-0035>
    Talbot and Beaudoin (2022) <doi:10.1515/jci-2021-0023>.

Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.

Implements the Bayesian Clustering Factor Models (BCFM) for 
    simultaneous clustering and latent factor analysis of multivariate longitudinal data. 
    The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. 
    Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via 'Rcpp'. 
    Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) <doi:10.1002/sim.70350>.

Fit semiparametric bivariate correlated frailty models.

Fit and simulate bivariate correlated frailty models with
    proportional hazard structure. Frailty distributions, such as gamma and
    lognormal models are supported semiparametric procedures. Frailty 
    variances of the two subjects can be varied or equal. Details on the models are available in book of 
    Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given 
    in Kifle et al (2023) <DOI: 10.4310/22-SII738> with modifications. Lognormal fit is based on the approach
    by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, 
    such as gamma, inverse gaussian and power variance frailty models are supported 
    for parametric approach.