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General Resources

  • CRAN. The R mirror site at WashU.
  • "Thank R It's Friday" Tutorial Basics.
  • "Thank R It's Friday" Tutorial Graphing.
  • "Thank R It's Friday" Tutorial Regression.

Authored Software

  • GLMpack Contains all the data and functions used in Generalized Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create all models, tables, and plots are included for each data set (Jeff Gill, Michelle Torres, Simon Heuberger).
  • krige Estimates kriging models for geographical point-referenced data (Jason S. Byers, Le Bao, Jamie Carson, Jeff Gill).
  • glmdm Performs generalized linear mixed Dirichlet models using posterior simulation (Jeff Gill, George Casella, Minjung Kyung, and Jonathan Rapkin).
  • BaM Functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach" First, Second, and Third Editions (Jeff Gill).
  • hot.deck Performs multiple hot-deck imputation of categorical and continuous variables in a data frame (Skyler Cranmer, Jeff Gill, Natalie Jackson, Andreas Murr, Dave Armstrong).
  • superdiag! for producing four comprehensive MCMC convergence diagnostics with one short R command (Tsung-han Tsai, Jeff Gill, and Jonathan Rapkin).
  • A function for modern regression Summaries.
  • Elicit: a Web-based tool for eliciting beta-distributed priors from subject-matter experts about nodes and edges of a network. Developed by John Freeman, Jeff Gill, Stephen R. Haptonstahl, Aaron Rapport.
  • R Code for Simulated Temporing.
  • R Code for Importance Sampling.
  • C Language Source Code for Building Survey Subsets.
  • R Code for GSRLS and SWLS Procedures.
  • R Code for log-like functions (for simulations).
  • R Code generating multivariate normals.
  • Gauss Code for the Gill-Murray generalized Cholesky Decomposition.
  • Gauss Code for the Schnabel-Eskow generalized Cholesky Decomposition, R version, and Some R routines for checking/running.
  • R Code for colon cancer data analysis.
  • Turning Your Mac Into a Scientific Workstation. Some links and steps for making your OS X machine a serious workstation for research. Jake Bowers and I have collected these resources with additions from Chris Zorn, Michael Herron, and Andrew Martin. Additional suggestions welcome.
TeX and LaTeX
I get a reasonable number of queries about using the TeX typesetting program. Basically MS-Word is lame, and the idea of using WYSIWYG is incompatible with scientific communication. Conversely, the LaTeX program is ideally suited to the way we work. Actually I haven't seriously used a wordprocessor since about 1991. Some grad students are put off by the TeX world, which initially seems to be dominated by European academics with too much time on their hands and a weird fixation for animal drawings. The truth is that these are incredibly powerful tools for assembling perfectly typeset documents. The important thing is to get over the initial difference in using a typesetting program instead of a wordprocessor. Here are some links to get one started.