Date Subject Code
1-10 Introduction [lead]
1-15 Binomial data [R]
1-17 BUGS: Language, engines, and interfaces [premature] [mixture]
1-22 One-parameter models [R]
1-24 The normal distribution [R] [bad]
1-31 A Case Study: Two-sample categorical data [GREAT] [OR vs. RR]
2-7 Linear regression [R] [alcohol]
2-12 Regression analysis: Extensions [hills] [puromycin] [beetles] [roots]
2-14 Frequentist properties of Bayesian methods [simulation]
2-19 Model comparison: Deviance-based approaches [alcohol]
2-21 Bayes factors and multi-model inference [brunner.txt] [brunner.R]
2-28 MCMC Methods: Gibbs and Metropolis [R] [US arrests]
3-5 MCMC Diagnostics [swiss]
3-19 Introduction to hierarchical models: Varying intercepts [radon]
3-21 Group-level predictors [radon]
3-26 Varying intercepts and slopes [Varying slopes] [With group-level predictor] [Correlated parameters]
3-28 Wishart Priors [R] [Wishart]
[Scaled Wishart]
4-2 Non-nested models and generalized linear models [Flight-ANOVA]
[Flight-logistic]
[Earnings]
4-4 Uncertainty vs. variability, finite- vs. super-populations [Radon][Flight]
[Earnings]
4-11 Enoxaparin case study (available via e-mail to enrolled students)
4-18 Summarizing explained variance and partial pooling [radon]
4-23 Missing data [CD4, ignorable]
[CD4, informative]
[THM, ignorable]
[THM, probit]



Some miscellaneous R functions: fun.R