Package: catR 3.17

catR: Generation of IRT Response Patterns under Computerized Adaptive Testing

Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).

Authors:David Magis, Gilles Raiche, Juan Ramon Barrada

catR_3.17.tar.gz
catR_3.17.zip(r-4.5)catR_3.17.zip(r-4.4)catR_3.17.zip(r-4.3)
catR_3.17.tgz(r-4.4-any)catR_3.17.tgz(r-4.3-any)
catR_3.17.tar.gz(r-4.5-noble)catR_3.17.tar.gz(r-4.4-noble)
catR_3.17.tgz(r-4.4-emscripten)catR_3.17.tgz(r-4.3-emscripten)
catR.pdf |catR.html✨
catR/json (API)
NEWS

# Install 'catR' in R:
install.packages('catR', repos = c('https://huacheng1985.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • cat_pav - Items parameters of the CAT_PAV data set
  • tcals - Items parameters of the TCALS 1998 data set and subgroups of items

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.98 score 3 stars 1 packages 105 scripts 861 downloads 10 mentions 31 exports 0 dependencies

Last updated 2 years agofrom:112a2efb66. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 17 2024
R-4.5-winOKNov 17 2024
R-4.5-linuxOKNov 17 2024
R-4.4-winOKNov 17 2024
R-4.4-macOKNov 17 2024
R-4.3-winOKNov 17 2024
R-4.3-macOKNov 17 2024

Exports:aStratifiedbreakBankcheckStopRuleeapEsteapSemEPVfullDistGDIgenDichoMatrixgenPatterngenPolyMatrixIiintegrate.catRJiKLMEIMWInextItemOIiPiplot.catplot.catResultprint.catprint.catResultrandomCATsemThetasimulateRespondentsstartItemstest.cbListtestListthetaEst

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Item membership assessment for a-stratified samplingaStratified
Breaking the item bank in item parameters and group membership (for content balancing)breakBank
Items parameters of the CAT_PAV data set (with item names)cat_pav
Checking whether the stopping rule is satisfiedcheckStopRule
EAP ability estimation (dichotomous and polytomous IRT models)eapEst
Standard error of EAP ability estimation (dichotomous and polytomous IRT models)eapSem
Expected Posterior Variance (EPV)EPV
Full distribution of ability estimator (dichotomous models only)fullDist
Global-discrimination index (GDI) and posterior global-discrimination index (GDIP) for item selectionGDI
Item bank generation (dichotomous models)genDichoMatrix
Random generation of item response patterns under dichotomous and polytomous IRT modelsgenPattern
Item bank generation (polytomous models)genPolyMatrix
Item information functions, first and second derivatives (dichotomous and polytomous models)Ii
Numerical integration by linear interpolation (for catR internal use)integrate.catR
Function J(theta) for weighted likelihood estimation (dichotomous and polytomous IRT models)Ji
Kullback-Leibler (KL) and posterior Kullback-Leibler (KLP) values for item selectionKL
(Maximum) Expected Information (MEI)MEI
Maximum likelihood weighted information (MLWI) and maximum posterior weighted information (MPWI)MWI
Selection of the next itemnextItem
Observed information function (dichotomous and polytomous models)OIi
Item response probabilities, first, second and third derivatives (dichotomous and polytomous models)Pi
Random generation of adaptive tests (dichotomous and polytomous models)plot.cat print.cat randomCAT
Standard error of ability estimation (dichotomous and polytomous models)semTheta
Simulation of multiple examinees of adaptive testsplot.catResult print.catResult simulateRespondents
Selection of the first itemsstartItems
Items parameters of the TCALS 1998 data set and subgroups of itemstcals
Testing the format of the list for content balancing under dichotomous or polytomous IRT modelstest.cbList
Testing the format of the input liststestList
Ability estimation (dichotomous and polytomous models)thetaEst