HBglm: Hierarchical Bayesian Regression for GLMs
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.
| Version: |
0.1 |
| Depends: |
R (≥ 2.10) |
| Imports: |
Formula, bayesm, sns, MfUSampler, stats |
| Published: |
2015-07-14 |
| Author: |
Asad Hasan, Alireza S. Mahani |
| Maintainer: |
Asad Hasan <asad.hasan at sentrana.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
ChangeLog |
| CRAN checks: |
HBglm results |
Documentation:
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