A guided walk through the Metropolis algorithmm | The data | Helper functions | Maximum likelihood estimates | Random walk metropolis | Inspecting output | Guided metropolis | Contrasting output with random walk | Guided, adaptive metropolis algorithm | Contrasting output | Guided, adaptive metropolis algorithm using normal priors | Using the R package | Using the R package, smart initial values | Extending the logistic model results after samples are generated
Package: metropolis 0.1.8
metropolis: The Metropolis Algorithm
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>.
Authors:
metropolis_0.1.8.tar.gz
metropolis_0.1.8.zip(r-4.7)metropolis_0.1.8.zip(r-4.6)metropolis_0.1.8.zip(r-4.5)
metropolis_0.1.8.tgz(r-4.6-any)metropolis_0.1.8.tgz(r-4.5-any)
metropolis_0.1.8.tar.gz(r-4.7-any)metropolis_0.1.8.tar.gz(r-4.6-any)
metropolis_0.1.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
metropolis/json (API)
| # Install 'metropolis' in R: |
| install.packages('metropolis', repos = c('https://alexpkeil1.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- magfields - A case control study of childhood leukemia and magnetic fields from Savitz, Wachtel, Barnes, et al (1998) doi:10.1093/oxfordjournals.aje.a114943 <https://doi.org/10.1093/oxfordjournals.aje.a114943>.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:dfbd96a2d1. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 131 | ||
| source / vignettes | OK | 155 | ||
| linux-release-x86_64 | OK | 132 | ||
| macos-release-arm64 | OK | 122 | ||
| macos-oldrel-arm64 | OK | 102 | ||
| windows-devel | OK | 108 | ||
| windows-release | OK | 112 | ||
| windows-oldrel | OK | 107 | ||
| wasm-release | OK | 94 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Convert glm_metropolis output to 'mcmc' object from package coda | as.mcmc.metropolis.samples |
| Inverse logit transform | expit |
| logistic log likelihood | logistic_ll |
| A case control study of childhood leukemia and magnetic fields from Savitz, Wachtel, Barnes, et al (1998) doi:10.1093/oxfordjournals.aje.a114943 <https://doi.org/10.1093/oxfordjournals.aje.a114943>. | magfields |
| Use the Metropolis Hastings algorithm to estimate Bayesian glm parameters | metropolis_glm |
| metropolis.control | metropolis.control |
| Gaussian log likelihood | normal_ll |
| Plot the output from the metropolis function | plot.metropolis.samples |
| Print a metropolis.samples object | print.metropolis.samples |
| Summarize a probability distribution from a Markov Chain | summary.metropolis.samples |
