R in the Political Science Computational Laboratory, Stanford University
new classes and/or methods for discrete data models; e.g., models for counts, ordinal data, item-response theory (IRT) models for binary data

new classes and/or methods for discrete data models; e.g., models for counts, ordinal data, item-response theory (IRT) models for binary data, computing highest density regions

Political Science Computational Laboratory, Stanford University
models for counts, ordinal data, item-response theory (IRT) models, computing highest density regions

Simon Jackman <jackman@stanford.edu>, with contributions from Alex Tahk, Christina Maimone and Jim Fearon
Bayesian analysis of item-response theory (IRT) models, roll call analysis; computing highest density regions; maximum likelihood estimation of zero-inflated and hurdle models for count data; data sets used in writing and teaching at PSCL

R (>= 2.3.0), MASS, stats, mvtnorm, coda

Simon Jackman <jackman@stanford.edu>, with contributions from Alex Tahk, Achim Zeileis, Christina Maimone and Jim Fearon
Bayesian analysis of item-response theory (IRT) models, roll call analysis; computing highest density regions; maximum likelihood estimation of zero-inflated and hurdle models for count data; data sets used in writing and teaching at the Political Science Computational Laboratory; seats-votes curves

Simon Jackman, with contributions from
Alex Tahk, Achim Zeileis, Christina Maimone and Jim Fearon
Bayesian analysis of item-response theory (IRT) models, roll call analysis;
computing highest density regions; maximum likelihood estimation of
zero-inflated and hurdle models for count data; goodness-of-fit measures for
GLMs; data sets used in writing
and teaching at the Political Science Computational Laboratory; seats-votes curves.

Bayesian analysis of item-response theory (IRT) models, roll call analysis;
computing highest density regions; maximum likelihood estimation of
zero-inflated and hurdle models for count data; goodness-of-fit measures for
GLMs; data sets used in writing and teaching at the Political Science
Computational Laboratory; seats-votes curves.