bayescomputer's Introduction

BayesComputeR

This R Markdown documents and associated datasets are used for a short course "Bayesian Thinking: Fundamentals, Computation, and Multilevel Modeling" by Jim Albert, Bowling Green State University

Packages -- the following packages available from CRAN are used in these examples and should be installed at first

arm - Data Analysis Using Multilevel/Hierarchical Models
BayesTestStreak (available from GitHub) - Testing Streakiness in Binary Sequences
betareg - Beta Regression
brms - Bayesian Regression Models using 'Stan'
coda - Output Analysis and Diagnostics for MCMC
lme4 - Linear Mixed-Effects Models using 'Eigen' and S4
LearnBayes - Functions for Learning Bayesian Inference
rethinking - Statistical Rethinking book package
rjags - Bayesian Graphical Models using MCMC (JAGS software also needs to be installed)
rstanarm - Bayesian Applied Regression Modeling via Stan
TeachBayes - Teaching Bayesian Inference
tidybayes - Tidy Data and 'Geoms' for Bayesian Models
tidyverse - suite of packages of the Tidyverse

Part I - Basic Bayes

Streakiness.Rmd -- learning about a geometric proportion
Moving_to_NC -- learning about a normal mean with known standard deviation
NormalSampling.Rmd - normal sampling model
SelectedData.Rmd -- learning about a normal mean observing selected order statistics
Intro_to_JAGS.Rmd -- illustration of JAGS for a Poisson log-linear model

Part II - Regression

Regression.Rmd -- Bayesian linear regression
Worship Data.Rmd - Modeling counts by a Poisson log linear model
Attendance.Rmd - Modeling fractions of capacity attendance using a beta regression model

Part III - Multilevel Modeling

HomeRun.Rmd -- Modeling home run rates using a varying intercept model
BBS_Survey.Rmd -- Multilevel estimate of some trend estimates
Coffee_Shop.Rmd - Modeling varying intercepts and varying slopes for waiting times

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