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glmbayes: Bayesian Generalized Linear Models (IID Samples)
Provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>. The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for larger models.
Getting started
Package overview
README.md
Chapter 00: Introduction
Chapter 01: Getting started with glmbayes
Chapter 02-S01: Conjugate Models — Introduction and Overview
Chapter 02-S02: Normal–Normal Conjugacy for One Mean
Chapter 02-S03: Beta–Binomial Conjugacy for One Proportion
Chapter 02-S04: Gamma–Poisson Conjugacy for One Count Rate"
Chapter 02-S05: Gamma–Gamma Conjugacy for One Response Rate
Chapter 03: Estimating Bayesian linear models
Chapter 04: Tailoring priors — leveraging the Prior_Setup function
Chapter 05: Model predictions and posterior predictive checks (+ bayesplot ppc_*)
Chapter 06: Deviance residuals, model statistics and posterior inference (+ bayestestR)"
Chapter 07: Foundations of GLMs — families, links, and log-concave likelihoods
Chapter 08: Estimating Bayesian generalized linear models
Chapter 09: Models for the Binomial family
Chapter 10: Models for the Poisson family
Chapter 11: Models for the Gamma family
Chapter 12: Visualizing posteriors with bayesplot
Chapter 13: Bayesian inference and decision making with bayestestR
Chapter 14: Informative priors — centering and differential prior weights"
Chapter 15: Estimating models with unknown dispersion parameters
Chapter 16: Large models — GPU acceleration using OpenCL"
Chapter 17: Linear mixed-effects models"
Chapter 18: Generalized linear mixed-effects models
Chapter A01: A detailed overview of the glmbayes package
Chapter A02: Overview of Estimation Procedures"
Chapter A03: Methods available in glmbayes"
Chapter A04: Directional Tail Diagnostics for Prior-Posterior Disagreement"
Chapter A05: Simulation Methods - Likelihood Subgradient Densities"
Chapter A06: Accept–Reject Sampling for Dispersion in Gamma Regression"
Chapter A07: Accept–Reject Sampling for gaussian Regression models with independent normal-gamma priors
Chapter A08: Overview of Envelope Related Functions"
Chapter A09: Parallel Sampling Implementation using RcppParallel"
Chapter A10: Accelerated EnvelopeBuild Implementation using OpenCL"
Chapter A11: Implementation Companion for Independent Normal-Gamma
Chapter A12: Technical Derivations for Priors Returned by `Prior_Setup()
Browse package contents
Package details |
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| Author | Kjell Nygren [aut, cre], The R Core Team [ctb, cph] (R Mathlib sources, R stats modeling code, and derived/adapted routines), The R Foundation [cph] (Portions of R Mathlib and R source code), Ross Ihaka [ctb, cph] (R Mathlib and original R modeling infrastructure), Robert Gentleman [ctb, cph] (Portions of R Mathlib), Simon Davies [ctb] (Original R glm implementation), Morten Welinder [ctb, cph] (Portions of R Mathlib), Martin Maechler [ctb] (Portions of R Mathlib) |
| Maintainer | Kjell Nygren <kjell.a.nygren@gmail.com> |
| License | GPL-2 |
| Version | 0.9.75 |
| URL | https://CRAN.R-project.org/package=glmbayes https://github.com/knygren/glmbayes https://knygren.r-universe.dev/glmbayes |
| Package repository | View on CRAN |
| Installation |
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