HBglm: Hierarchical Bayesian Regression for GLMs

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Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.

Author
Asad Hasan, Alireza S. Mahani
Date of publication
2015-07-14 15:59:53
Maintainer
Asad Hasan <asad.hasan@sentrana.com>
License
GPL (>= 2)
Version
0.1

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Man pages

HBglm
Hierarchical Bayesian Regression for GLMs
linear_list
Simulated data for a Gaussian 2-level HB regression model.

Files in this package

HBglm
HBglm/NAMESPACE
HBglm/data
HBglm/data/linear_list.rda
HBglm/R
HBglm/R/slice_sampler.R
HBglm/R/model.R
HBglm/R/family.R
HBglm/R/formula.R
HBglm/R/predict.R
HBglm/R/methods.R
HBglm/R/hblinear.R
HBglm/R/modspecs.R
HBglm/R/hbglm.R
HBglm/R/misc.R
HBglm/R/mcmc.R
HBglm/R/invgamma.R
HBglm/MD5
HBglm/DESCRIPTION
HBglm/ChangeLog
HBglm/man
HBglm/man/linear_list.Rd
HBglm/man/HBglm.Rd