Vignettes and other documentation

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Vignettes from package 'glmbayes'

glmbayes::Chapter-00 Chapter 00: Introduction HTML source R code
glmbayes::Chapter-01 Chapter 01: Getting started with glmbayes HTML source R code
glmbayes::Chapter-02-S01 Chapter 02-S01: Conjugate Models — Introduction and Overview HTML source R code
glmbayes::Chapter-02-S02 Chapter 02-S02: Normal–Normal Conjugacy for One Mean HTML source R code
glmbayes::Chapter-02-S03 Chapter 02-S03: Beta–Binomial Conjugacy for One Proportion HTML source R code
glmbayes::Chapter-02-S04 Chapter 02-S04: Gamma–Poisson Conjugacy for One Count Rate HTML source R code
glmbayes::Chapter-02-S05 Chapter 02-S05: Gamma–Gamma Conjugacy for One Response Rate HTML source R code
glmbayes::Chapter-03 Chapter 03: Estimating Bayesian linear models HTML source R code
glmbayes::Chapter-04 Chapter 04: Tailoring priors — leveraging the Prior_Setup function HTML source R code
glmbayes::Chapter-05 Chapter 05: Model predictions and posterior predictive checks (+ bayesplot ppc_*) HTML source R code
glmbayes::Chapter-06 Chapter 06: Deviance residuals, model statistics and posterior inference (+ bayestestR) HTML source R code
glmbayes::Chapter-07 Chapter 07: Foundations of GLMs — families, links, and log-concave likelihoods HTML source
glmbayes::Chapter-08 Chapter 08: Estimating Bayesian generalized linear models HTML source R code
glmbayes::Chapter-09 Chapter 09: Models for the Binomial family HTML source R code
glmbayes::Chapter-10 Chapter 10: Models for the Poisson family HTML source R code
glmbayes::Chapter-11 Chapter 11: Models for the Gamma family HTML source R code
glmbayes::Chapter-12 Chapter 12: Visualizing posteriors with bayesplot HTML source R code
glmbayes::Chapter-13 Chapter 13: Bayesian inference and decision making with bayestestR HTML source R code
glmbayes::Chapter-14 Chapter 14: Informative priors — centering and differential prior weights HTML source R code
glmbayes::Chapter-15 Chapter 15: Estimating models with unknown dispersion parameters HTML source R code
glmbayes::Chapter-16 Chapter 16: Large models — GPU acceleration using OpenCL HTML source R code
glmbayes::Chapter-17 Chapter 17: Linear mixed-effects models HTML source R code
glmbayes::Chapter-18 Chapter 18: Generalized linear mixed-effects models HTML source R code
glmbayes::Chapter-A01 Chapter A01: A detailed overview of the glmbayes package HTML source R code
glmbayes::Chapter-A02 Chapter A02: Overview of Estimation Procedures HTML source R code
glmbayes::Chapter-A03 Chapter A03: Methods available in glmbayes HTML source R code
glmbayes::Chapter-A04 Chapter A04: Directional Tail Diagnostics for Prior-Posterior Disagreement HTML source R code
glmbayes::Chapter-A05 Chapter A05: Simulation Methods - Likelihood Subgradient Densities HTML source R code
glmbayes::Chapter-A06 Chapter A06: Accept–Reject Sampling for Dispersion in Gamma Regression HTML source R code
glmbayes::Chapter-A07 Chapter A07: Accept–Reject Sampling for gaussian Regression models with independent normal-gamma priors HTML source R code
glmbayes::Chapter-A08 Chapter A08: Overview of Envelope Related Functions HTML source R code
glmbayes::Chapter-A09 Chapter A09: Parallel Sampling Implementation using RcppParallel HTML source R code
glmbayes::Chapter-A10 Chapter A10: Accelerated EnvelopeBuild Implementation using OpenCL HTML source R code
glmbayes::Chapter-A11 Chapter A11: Implementation Companion for Independent Normal-Gamma HTML source R code
glmbayes::Chapter-A12 Chapter A12: Technical Derivations for Priors Returned by `Prior_Setup() HTML source R code