c(person("Oystein", "Sorensen",
email = "oystein.sorensen.1985@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-0724-3542")))
Oystein Sorensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>)

c(person("Qingzhao", "Yu", role = c("aut", "cre", "cph"),
email = "qyu@lsuhsc.edu"),
person("Bin", "Li", role = "aut"))
Qingzhao Yu [aut, cre, cph],
Bin Li [aut]
R (>= 2.14.1), survival, R2jags, car, gplots,lattice
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.

A Shiny web application for creating interactive Bayesian Network models,
learning the structure and parameters of Bayesian networks, and utilities for classical
network analysis.
person("Paul", "Govan", email = "pgovan1@aggienetwork.com", role = c("aut", "cre"))
bnlearn, d3heatmap, lattice, networkD3, rhandsontable, shiny,
shinydashboard

A 'Shiny' web application for creating interactive Bayesian Network models,
learning the structure and parameters of Bayesian networks, and utilities for classical
network analysis.

A 'Shiny' web application for creating interactive Bayesian Network models,
learning the structure and parameters of Bayesian networks, and utilities for classic
network analysis.
bnlearn, heatmaply, lattice, networkD3, plotly, rintrojs,
shiny, shinyAce, shinydashboard

person("Paul", "Govan", email = "pgovan1@aggienetwork.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1821-8492"))
bnlearn, heatmaply, lattice, networkD3, plotly, rintrojs,
shiny, shinyAce, shinydashboard, shinytest, shinyWidgets,
testthat

Sample Size and Power for Comparing Inequality Constrained
Hypotheses
A collection of methods to determine the required sample size for
the evaluation of inequality constrained hypotheses by means of a Bayes
factor. Alternatively, for a given sample size, the unconditional error
probabilities or the expected conditional error probabilities can be
determined. Additional material on the methods in this package is
available in Klaassen, F., Hoijtink, H. & Gu, X. (2019)
<doi:10.31219/osf.io/d5kf3>.