An Empirical Bayes Method for Chi-Squared Data
c(person(given = "Lilun",
family = "Du",
role = c("aut", "cre"),
email = "dulilun@ust.hk"),
person(given = "Inchi",
family = "Hu",
role = c("aut"),
email = "imichu@ust.hk"))
We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2020) <doi:10.1080/01621459.2020.1777137>.

Empirical Bayesian Elastic Net algorithms.

Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.

separationplot, ensembleBMA, plyr, methods, Hmisc
'forecastData.R' 'fitEnsemble.R' 'calibrateEnsemble.R'
'fDatFitLogit.R' 'compareModels.R' 'document-data.R'
'summary.R'

Jacob M. Montgomery, Florian Hollenbach, and Michael D. Ward
separationplot, ensembleBMA, plyr, methods, Hmisc, abind
'forecastData.R' 'calibrateEnsemble.R' 'fDatFitLogit.R'
'compareModels.R' 'document-data.R' 'summary.R'
'FDatFitNormal.R' 'fitEnsembleLogit.R' 'fitEnsembleNormal.R'
'makeForecastData.R' 'plot.R'

'forecastData.R' 'calibrateEnsemble.R' 'fDatFitLogit.R'
'fDatFitNormal.R' 'compareModels.R' 'document-data.R'
'summary.R' 'fitEnsembleLogit.R' 'fitEnsembleNormal.R'
'makeForecastData.R' 'plot.R'

Jacob M. Montgomery, Florian M. Hollenbach, and Michael D. Ward
stats, graphics, separationplot, plyr, methods, Hmisc, abind

Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs
Sampling or EM-Algorithms
c(person("Florian M.", "Hollenbach", email = "fhollenbach@tamu.edu",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9599-556X")),
person("Jacob M.", "Montgomery", role = c("aut")),
person("Michael D.", "Ward", role = c("aut")))
Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling.
Rcpp (>= 1.0.2), plyr, graphics, separationplot, Hmisc, abind,
gtools, methods
'EBMAforecast.R' 'forecastData.R' 'EBMApredict.R'
'RcppExports.R' 'calibrateEnsemble.R' 'compareModels.R'
'document-data.R' 'fitEnsembleLogit.R' 'fitEnsembleNormal.R'
'global.R' 'makeForecastData.R' 'predictLogit.R'
'predictNormal.R' 'summary_plot.R' 'print_show.R'
'utility-functions.R'
Florian M. Hollenbach [aut, cre]
(<https://orcid.org/0000-0002-9599-556X>),
Jacob M. Montgomery [aut],
Michael D. Ward [aut]