pscl1.5.9 package

Political Science Computational Laboratory

compute and optionally plot beta HDRs

add information about voting outcomes to a rollcall object

constrain item parameters in analysis of roll call data

constrain legislators' ideal points in analysis of roll call data

convert entries in a rollcall matrix to binary form

drop user-specified elements from a rollcall object

drop unanimous votes from rollcall objects and matrices

return the roll call object used in fitting an ideal model

Table of Actual Outcomes against Predicted Outcomes for discrete data ...

Control Parameters for Hurdle Count Data Regression

Hurdle Models for Count Data Regression

Testing for the Presence of a Zero Hurdle

analysis of educational testing data and roll call data with IRT model...

convert an object of class ideal to a coda MCMC object

inverse-Gamma distribution

nicely formatted tables

likelihood ratio test for over-dispersion in count data

plots an ideal object

plot methods for predictions from ideal objects

plot seats-votes curves

remap MCMC output via affine transformations

compute various pseudo-R2 measures

Methods for hurdle Objects

predicted probabilities from an ideal object

Methods for zeroinfl Objects

Predicted Probabilities for GLM Fits

predicted probabilities from fitting ideal to rollcall data

compute predicted probabilities from fitted models

read roll call data in Poole-Rosenthal KH format

create an object of class rollcall

A class for creating seats-votes curves

Monte Carlo estimate of pi (3.14159265...)

summary of an ideal object

summarize a rollcall object

trace plot of MCMC iterates, posterior density of legislators' ideal p...

convert roll call matrix to series of vectors

Vuong's non-nested hypothesis test

Control Parameters for Zero-inflated Count Data Regression

Zero-inflated Count Data Regression

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; seats-votes curves.

Maintainer: Simon Jackman License: GPL-2 Last published: 2024-01-31