cIRT

Choice Item Response Theory


Keywords
armadillo, bayesian, choice, cognitive-diagnostic-models, gibbs-sampling, item-response-theory, r, rcpparmadillo, rstats
Licenses
CNRI-Python-GPL-Compatible/CNRI-Python-GPL-Compatible

Documentation

cIRT: Choice Item Response Theory

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Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015).

Installation

You can install cIRT from CRAN using:

install.packages("cIRT")

Or, you can be on the cutting-edge development version on GitHub using:

if(!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("tmsalab/cIRT")

Usage

To use the cIRT package, load it into R using:

library("cIRT")

Authors

Steven Andrew Culpepper and James Joseph Balamuta

Citing the cIRT package

To ensure future development of the package, please cite cIRT package if used during an analysis or simulation studies. Citation information for the package may be acquired by using in R:

citation("cIRT")

License

GPL (>= 2)