Title: Bayesian Inference for the Multivariate Skew-t Model
Authors@R: c(person("Antonio", "Parisi", role = c("aut", "cre"),
person("Brunero", "Liseo", role = "aut"),
person("Romain", "Francois", role = "ctb"))
Description: Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. Objective Bayesian analysis for the multivariate skew-t model (to appear).

Description: Estimates the multivariate skew-t and nested models, as described in the
articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal
model: a population Monte Carlo approach. Comput. Statist. Data Anal.
<doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017). Objective Bayesian
analysis for the multivariate skew-t model. Statistical Methods & Applications

Description: Generates correlated binary data based on the method of

Description: Generates correlated binary data based on the method of Emrich and Piedmonte (1991)
Packaged: Wed Feb 18 13:38:10 2009; euclid

Title: Tree Boosting for Multivariate Outcomes
Description: Fits a multivariate model of decision trees for multiple,
continuous outcome variables. A model for each outcome variable is fit separately, selecting
predictors that explain that explain covariance in multiple outcomes.
Suggests: testthat, plyr, MASS, parallel, lars, ggplot2, knitr,
Imports: gbm, RColorBrewer, stats, graphics, grDevices, utils,

continuous outcome variables. A model for each outcome variable is fit
separately, selecting predictors that explain that explain covariance in
multiple outcomes. Package is built on top of 'gbm', which fits an ensemble
of decision trees to univariate outcomes.

Description: Fits a multivariate model of decision trees for multiple, continuous outcome variables. A model for each outcome variable is fit separately, selecting predictors that explain covariance in the outcomes. Built on top of 'gbm', which fits an ensemble of decision trees to univariate outcomes.