ggplot2, methods, car, crayon, DiagrammeR, bridgesampling

ggplot2, methods, car, crayon, DiagrammeR, bridgesampling,
Suggests: rmarkdown, rstantools, openxlsx, hexbin, MASS, ggmcmc,

rhandsontable, shiny, pracma, shinydashboard, shinythemes

Suggests: openxlsx, hexbin, MASS, ggmcmc, magrittr

Suggests: openxlsx, hexbin, MASS, ggmcmc, magrittr, markdown

Suggests: openxlsx, hexbin, MASS, magrittr, markdown, rmarkdown

ggplot2, methods, car, crayon, bridgesampling, rhandsontable,

Imports: knitr, stats, graphics, tcltk, grDevices, ggplot2, methods,
car, crayon, bridgesampling, rhandsontable, shiny, pracma,
Suggests: hexbin, MASS, magrittr, markdown, rmarkdown

Authors@R: c(person("Patrick","Trainor",email="patrick.trainor@louisville.edu", role = c("aut", "cre")),person("Hao","Wang",email="haowang@msu.edu",role="aut"))
Description: Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) <doi:10.1214/12-BA729>.

Title: Predict Brain Asymmetry Based on Handedness and Dichotic
Description: Functional differences between the cerebral hemispheres
are a fundamental characteristic of the human brain. Researchers
interested in studying these differences often infer underlying
hemispheric dominance for a certain function (e.g., language) from
laterality indices calculated from observed performance or brain
activation measures . However, any inference from observed measures
to latent (unobserved) classes has to consider the prior probability
of class membership in the population. The provided functions
implement a Bayesian model for predicting hemispheric dominance from
observed laterality indices (Sorensen and Westerhausen, Laterality:

Author: Oystein Sorensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>)

Authors@R: c(person("Qingzhao", "Yu", role = c("aut", "cre", "cph"),
Depends: R (>= 2.14.1), survival, R2jags, car, gplots,lattice
Description: We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.