Generalized Linear Models: A Unified Approach
Gill, Jeff, and Michelle Torres. Generalized Linear Models: A Unified Approach. Second. Thousand Oaks, CA: Sage, 2019. Publisher's Version Abstract

Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition provides examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models.


Software scripts, supporting documentation, data for the examples, and some extended mathematical derivations are available on the authors’ websites as well as through the \texttt{R} package \texttt{GLMpack}. All links are available at

Bayesian Methods: A Social and Behavioral Sciences Approach
SInce the Spring of 2016 all of the code and data for the book has been located in the R package BaM, including both R and JAGS. Answers to odd numbered exercises are here.  The Corrected index for the very first print run (few people will need this). The state.df dataset got left off the BaM package so it's here. The errata for the third edition are here.
Bayesian Methods: A Social and Behavioral Sciences Approach
Essential Mathematics for Political and Social Research
Manuscript Contents Listing and Preface: PDFErrataAnswers to odd-numbered exercises
Numerical Issues in Statistical Computing for the Social Scientist
Gill, Jeff, Micah Altman, and Michael McDonald. Numerical Issues in Statistical Computing for the Social Scientist. John Wiley & Sons, 2003. John Wiley & Sons
"A compact guide to the voluminous literature on optimisation,
 numerical analysis, and computational statistics. This is no small achievement." -Statistical Software Newsletter in Computational Statistics and Data Analysis.

See also the review in JASA, June 2005. Software ResourcesTable of Contents and Two Sample ChaptersTechnometrics ReviewACM Review,SciTech Book News ReviewChoice Review, Computational Statistics and Data Analysis Review, Journal of Statistical Software ReviewStatistical Methods in Medical Research ReviewJournal of Statistical Computation and Simulation Review, Corrigenda.
Bayesian Methods: A Social and Behavioral Sciences Approach
"...easily the most comprehensive, scholarly, and thoughtful book the subject... it will do much to promote the use of Bayesian methods." -David Blackwell, University of California, Berkeley.

"A surprisingly thorough review written by a user of Bayesian statistics, with applications drawn from the social sciences." -Persi Diaconis, Stanford University.

"This book is a brilliant and importantly very accessible introduction to the concept and application of Bayesian approaches to data analysis. The clear strength of the book is in making the concept practical and accessible, without necessarily dumbing it down. It therefore retains the richness and complexity of this approach. The coverage is also remarkable, especially discussing this approach in the context of hierarchical linear models." -S V Subramanian, Harvard University.
What Works, A New Approach to Program and Policy Analysis
Gill, Jeff, and Kenneth Meier. What Works, A New Approach to Program and Policy Analysis. Westview Press, 2000. Publisher's Version
Code and datasets from the book:  S-Plus/R code, Child SupportTexas Schools1854 "Lunatics"Murder Rates and Pooled Texas Data.
Generalized Linear Models: A Unified Approach
Datasets from the book: Capital PunishmentScottish Vote Educational Testing,   World Copper Prices Committee Bill Assignments
Incomplete Beta Function Table (Replication from Cox and Snell, 1968). 
S-Plus/R code for the examples
The Handout from my American Statistical Association (Boston chapter) Workshop. Contains lots of S-Plus/R code for GLMs. 
Data from the Workshop (useful for running the examples). 
Pat Altham's Homepage (several useful guides to GLMs in S-Plus and R). 
Page for the Venables and Ripley book (includes GLM functions/code). 
SAS whitepapers, including fitting GLMs in SAS
Stata Help for GLMs
GLMLAB: A Generalized Linear Model Package for MATLAB by Peter Dunn at the Department of Mathematics and Computing, University of Southern Queensland (Australia). 
Jim Lindsay's GLIM Code from the examples in his books. 
SAS Procs for running the book examples..