Description: Provides efficient Bayesian nonparametric models for network feature selection
Imports: DPpackage, igraph, mclust, pscl, tmvtnorm, network
Author: Zhou Lan, Yize Zhao, Jian Kang, Tianwei Yu

Description: Provides a full package of posterior inference, model comparison, and graphical illustration of model fitting. A parallel computing algorithm for the Markov chain Monte Carlo (MCMC) based posterior inference and an Expectation-Maximization (EM) based algorithm for posterior approximation are are developed, both of which greatly reduce the computational time for model inference.

Depends: R (>= 3.0.3), foreach, doParallel
Imports: DPpackage, igraph, mclust, pscl, tmvtnorm, network, coda

Imports: DPpackage, igraph, mclust, pscl, tmvtnorm, network, coda,

Imports: DPpackage, igraph, mclust, network, GGally, truncnorm,
graphics, stats, flexmix, grDevices,foreach, doParallel, coda,
Author: Zhuxuan Jin, Zhou Lan, Yize Zhao, Jian Kang, Tianwei Yu

Title: BANOVA: Hierarchical Bayesian ANOVA Models
Description: BANOVA covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral sciences and related fields. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial(Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of means, which are easily interpretable for behavioral researchers. BANOVA uses JAGS as the computational platform.

Title: Hierarchical Bayesian ANOVA Models

Description: It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral sciences and related fields. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial(Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of means, which are easily interpretable for behavioral researchers. BANOVA uses JAGS as the computational platform.