California Congressional Districts in 2006
Methods for zeroinfl Objects
Control Parameters for Hurdle Count Data Regression
remap MCMC output via affine transformations
compute and optionally plot beta HDRs
article production by graduate students in biochemistry Ph.D. programs
convert an object of class ideal to a coda MCMC object
add information about voting outcomes to a rollcall
object
convert entries in a rollcall matrix to binary form
nicely formatted tables
constrain item parameters in analysis of roll call data
Testing for the Presence of a Zero Hurdle
drop user-specified elements from a rollcall object
Table of Actual Outcomes against Predicted Outcomes for discrete
data models
constrain legislators' ideal points in analysis of roll call data
Methods for hurdle Objects
elections to Australian House of
Representatives, 1949-2004
return the roll call object used in fitting an ideal model
drop unanimous votes from rollcall objects and matrices
Hurdle Models for Count Data Regression
compute various pseduo-R2 measures
summary of an ideal object
plot methods for predictions from ideal objects
trace plot of MCMC iterates, posterior density of legislators'
ideal points
read roll call data in Poole-Rosenthal KH format
plot seats-votes curves
compute predicted probabilities from fitted models
create an object of class rollcall
political parties appearing in the U.S. Congress
cross national rates of trade union density
predicted probabilities from fitting ideal to rollcall data
rollcall object, 109th U.S. Senate
A class for creating seats-votes curves
analysis of educational testing data and roll call data with IRT models, via Markov
chain Monte Carlo methods
summarize a rollcall object
information about the American states needed for U.S. Congress
Vuong's non-nested hypothesis test
convert roll call matrix to series of vectors
Applications to a Political Science PhD Program
Absentee and Machine Ballots in Pennsylvania State Senate Races
inverse-Gamma distribution
Prussian army horse kick data
Control Parameters for Zero-inflated Count Data Regression
votes from the United States Supreme Court, from 1994-1997
plots an ideal object
predicted probabilities from an ideal object
likelihood ratio test for over-dispersion in count data
Predicted Probabilties for GLM Fits
Zero-inflated Count Data Regression
Monte Carlo estimate of pi (3.14159265...)