boral: Bayesian Ordination and Regression AnaLysis

Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via JAGS. Three types of models may be fitted: 1) With explanatory variables only, boral fits independent column 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.

AuthorFrancis K.C. Hui
Date of publication2017-01-02 22:22:18
MaintainerFrancis Hui <>

View on CRAN


about.distributions Man page
about.ssvs Man page
about.traits Man page
boral Man page
boral.default Man page
boral-package Man page
calc.condlogLik Man page
calc.logLik.lv0 Man page
calc.marglogLik Man page
coefsplot Man page Man page
ds.residuals Man page
fitted.boral Man page
get.dic Man page
get.enviro.cor Man page
get.hpdintervals Man page
get.measures Man page
get.more.measures Man page
get.residual.cor Man page
lvsplot Man page
make.jagsboralmodel Man page
make.jagsboralnullmodel Man page
plot.boral Man page
print.boral Man page
print.summary.boral Man page
simulate.boral Man page
summary.boral Man page

Questions? Problems? Suggestions? or email at

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.