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