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>.
Package details |
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Author | Alexander Keil [aut, cre] |
Maintainer | Alexander Keil <akeil@unc.edu> |
License | GPL (>= 2) |
Version | 0.1.8 |
Package repository | View on CRAN |
Installation |
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