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pscl (version 1.5.5)

Political Science Computational Laboratory

Description

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.

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install.packages('pscl')

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43,915

Version

1.5.5

License

GPL-2

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Repository

http://github.com/atahk/pscl

Maintainer

Last Published

March 7th, 2020

Functions in pscl (1.5.5)

compute and optionally plot beta HDRs

AustralianElectionPolling

Political opinion polls in Australia, 2004-07

convert entries in a rollcall matrix to binary form

UKHouseOfCommons

1992 United Kingdom electoral returns

constrain.legis

constrain legislators' ideal points in analysis of roll call data

constrain.items

constrain item parameters in analysis of roll call data

AustralianElections

elections to Australian House of Representatives, 1949-2016

add information about voting outcomes to a rollcall object

drop user-specified elements from a rollcall object

U.S. Senate vote on the use of force against Iraq, 2002.

convert an object of class ideal to a coda MCMC object

rollcall object, National Journal key votes of 2007

Absentee and Machine Ballots in Pennsylvania State Senate Races

drop unanimous votes from rollcall objects and matrices

article production by graduate students in biochemistry Ph.D. programs

Methods for hurdle Objects

California Congressional Districts in 2006

Batting Averages for 18 major league baseball players, 1970

information about the American states needed for U.S. Congress

Hurdle Models for Count Data Regression

remap MCMC output via affine transformations

Monte Carlo estimate of pi (3.14159265...)

extractRollCallObject

return the roll call object used in fitting an ideal model

Testing for the Presence of a Zero Hurdle

Voter turnout experiment, using Rock The Vote ads

inverse-Gamma distribution

analysis of educational testing data and roll call data with IRT models, via Markov chain Monte Carlo methods

compute predicted probabilities from fitted models

Control Parameters for Hurdle Count Data Regression

nicely formatted tables

vectorRepresentation

convert roll call matrix to series of vectors

Reports of voting in the 1992 U.S. Presidential election.

compute various pseudo-R2 measures

plots an ideal object

plot.predict.ideal

plot methods for predictions from ideal objects

summary of an ideal object

predicted probabilities from an ideal object

politicalInformation

Interviewer ratings of respondent levels of political information

likelihood ratio test for over-dispersion in count data

Table of Actual Outcomes against Predicted Outcomes for discrete data models

predict.zeroinfl

Methods for zeroinfl Objects

predicted probabilities from fitting ideal to rollcall data

political parties appearing in the U.S. Congress

Predicted Probabilities for GLM Fits

rollcall object, 109th U.S. Senate (2005-06).

plot.seatsVotes

plot seats-votes curves

presidentialElections

elections for U.S. President, 1932-2016, by state

Prussian army horse kick data

summary.rollcall

summarize a rollcall object

Zero-inflated Count Data Regression

create an object of class rollcall

A class for creating seats-votes curves

Vuong's non-nested hypothesis test

trace plot of MCMC iterates, posterior density of legislators' ideal points

zeroinfl.control

Control Parameters for Zero-inflated Count Data Regression

cross national rates of trade union density

votes from the United States Supreme Court, from 1994-1997

read roll call data in Poole-Rosenthal KH format

Applications to a Political Science PhD Program