Learn R Programming

⚠️There's a newer version (3.17) of this package.Take me there.

catR (version 3.1)

Procedures to generate IRT adaptive tests (CAT)

Description

The catR package allows the generation of response patterns under computerized adaptive testing (CAT) framework, with the choice of several starting rules, next item selection routines, stopping rules and ability estimators. Control methods for item exposure and content balancing are also included.

Copy Link

Version

Install

install.packages('catR')

Monthly Downloads

1,370

Version

3.1

License

GPL (>= 2)

Maintainer

David Magis

Last Published

June 4th, 2014

Functions in catR (3.1)

Item information functions, first and second derivatives (dichotomous and polytomous models)

Function $J(\theta)$ for weighted likelihood estimation (dichotomous and polytomous IRT models)

Standard error of ability estimation (dichotomous and polytomous models)

Item response probabilities, first, second and third derivatives (dichotomous and polytomous models)

Kullback-Leibler (KL) and posterior Kullback-Leibler (KLP) values for item selection

Numerical integration by linear interpolation (for catR internal use)

Maximum likelihood weighted information (MLWI) and maximum posterior weighted information (MPWI)

Items parameters of the TCALS 1998 data set and subgroups of items

Observed information function (dichotomous and polytomous models)

Standard error of EAP ability estimation (dichotomous and polytomous IRT models)

simulateRespondents

Simulation of multiple examinees of adaptive tests

Item bank generation (dichotomous models)

Random generation of polytomous IRT matrices of item parameters

Breaking the item bank in item parameters and group membership (for content balancing)

Ability estimation (dichotomous and polytomous models)

Testing the format of the input lists

Random generation of item response patterns under dichotomous and polytomous IRT models

Selection of the first items

Selection of the next item

Random generation of adaptive tests (dichotomous and polytomous models)

Expected Posterior Variance (EPV)

(Maximum) Expected Information (MEI)

EAP ability estimation (dichotomous and polytomous IRT models)

Testing the format of the list for content balancing under dichotomous or polytomous IRT models