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metropolis (version 0.1.8)

The Metropolis Algorithm

Description

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); .

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Version

Install

install.packages('metropolis')

Monthly Downloads

153

Version

0.1.8

License

GPL (>= 2)

Maintainer

Alexander Keil

Last Published

September 21st, 2020

Functions in metropolis (0.1.8)

metropolis.control

metropolis.control
expit

Inverse logit transform
as.mcmc.metropolis.samples

Convert glm_metropolis output to mcmc object from package coda
metropolis_glm

Use the Metropolis Hastings algorithm to estimate Bayesian glm parameters
logistic_ll

logistic log likelihood
summary.metropolis.samples

Summarize a probability distribution from a Markov Chain
print.metropolis.samples

Print a metropolis.samples object
magfields

A case control study of childhood leukemia and magnetic fields from Savitz, Wachtel, Barnes, et al (1998) doi:10.1093/oxfordjournals.aje.a114943.
plot.metropolis.samples

Plot the output from the metropolis function
normal_ll

Gaussian log likelihood