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>.
|Author||Alexander Keil [aut, cre]|
|Maintainer||Alexander Keil <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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