Learning and using the Metropolis algorithm for Bayesian fitting of a generalized linear model. The package vignette includes examples of hand-coding a logistic model using several variants of the Metropolis algorithm. The package also contains R functions for simulating posterior distributions of Bayesian generalized linear model parameters using guided, adaptive, guided-adaptive and random walk Metropolis algorithms. The random walk Metropolis algorithm was originally described in Metropolis et al (1953) <doi:10.1063/1.1699114>.
| Version: | 0.1.5 |
| Depends: | coda, R (≥ 3.5.0) |
| Imports: | stats |
| Suggests: | knitr |
| Published: | 2020-01-23 |
| Author: | Alexander Keil [aut, cre] |
| Maintainer: | Alexander Keil <akeil at unc.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README NEWS |
| CRAN checks: | metropolis results |
| Reference manual: | metropolis.pdf |
| Vignettes: |
The metropolis algorithm for fitting Bayesian GLMs |
| Package source: | metropolis_0.1.5.tar.gz |
| Windows binaries: | r-devel: metropolis_0.1.5.zip, r-release: metropolis_0.1.5.zip, r-oldrel: metropolis_0.1.5.zip |
| macOS binaries: | r-release: metropolis_0.1.5.tgz, r-oldrel: metropolis_0.1.5.tgz |
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