The Particle Gibbs sampler and Gibbs/Metropolis-Hastings sampler were implemented
to fit Bayesian additive regression tree model. Construction of the model (training) and prediction
for a new data set (testing) can be separated. Our reference papers are:
Lakshminarayanan B, Roy D, Teh Y W. Particle Gibbs for Bayesian additive regression trees[C],
Artificial Intelligence and Statistics. 2015: 553-561,
<http://proceedings.mlr.press/v38/lakshminarayanan15.pdf>
and Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298, <https://doi.org/10.1214/09-aoas285>.

The Particle Gibbs sampler and Gibbs/Metropolis-Hastings sampler were implemented
to fit Bayesian additive regression tree model. Construction of the model (training) and prediction
for a new data set (testing) can be separated. Our reference papers are:
Lakshminarayanan B, Roy D, Teh Y W. Particle Gibbs for Bayesian additive regression trees[C],
Artificial Intelligence and Statistics. 2015: 553-561,
<http://proceedings.mlr.press/v38/lakshminarayanan15.pdf>
and Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298, <doi:10.1214/09-aoas285>.

Generate Random Samples from the Polya-Gamma Distribution
Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.

Penalized Generalized Estimating Equations for Bivariate Mixed
Outcomes
person("Ved", "Deshpande", email = "veddeshpande@gmail.com",
role = c("aut", "cre"))
Perform simultaneous estimation and variable selection for correlated bivariate
mixed outcomes (one continuous outcome and one binary outcome per cluster) using
penalized generalized estimating equations. In addition, clustered Gaussian and binary
outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported.
Cross-validation can be performed to find the optimal regularization parameter(s).