Description: Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.

Author: Francis K.C. Hui <fhui28@gmail.com>, with contributions from Wade Blanchard <wade.blanchard@anu.edu.au>

Author: Francis K.C. Hui <fhui28@gmail.com>, with contributions from Wafe Blanchard <wade.blanchard@anu.edu.au>

Imports: abind, corpcor, fishMod, graphics, grDevices, MASS, mvtnorm,

Maintainer: Francis K.C. Hui <fhui28@gmail.com>
Imports: abind, corpcor, fishMod, graphics, grDevices, lifecycle, MASS,

Title: Estimate Causal Effects with Borrowing Between Data Sources
Maintainer: Jeffrey A. Boatman <boat0036@umn.edu>
Description: Estimate population average treatment effects from a primary data source
with borrowing from supplemental sources. Causal estimation is done with either a
Bayesian linear model or with Bayesian additive regression trees (BART) to adjust
for confounding. Borrowing is done with multisource exchangeability models (MEMs). For
information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>.
For information on MEMs, see Kaizer, Koopmeiners, &

Maintainer: Jeffrey A. Boatman <jeffrey.boatman@gmail.com>

Title: Boosted One-Step Statistics: Fast and accurate approximations
for GLM, GEE and Mixed models for use in GWAS
Description: BOSS uses parameter estimates obtained without genotype to
boost standard one-step approximations, and precomputes as much
as possible without genotype (using boss.set) to minimize

Description: BOSS uses parameter estimates obtained without genotype to boost standard one-step approximations, and precomputes as much as possible without genotype (using boss.set) to minimize effort required.