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>.

Fit and simulate a semiparametric bivariate correlated frailty models with proportional 
    hazard structure. Frailty distributions, such as gamma and lognormal models are 
    supported. Bivariate gamma fit is obtained using the approach given in Iachine (1995) and 
    lognormal fit is based on the approach by 
    Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>.

Fit and simulate bivariate correlated frailty models with
    proportional hazard structure. Frailty distributions, such as gamma and
    lognormal models are supported for 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 Iachine (1995) 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.

Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.

Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them.