Multidimensional item response theory in R.
Analysis of dichotomous and polytomous response data using latent trait models under the Item Response Theory paradigm. Exploratory models can be estimated via quadrature or stochastic methods, a generalized confirmatory bi-factor analysis is included, and confirmatory models can be fit with a Metropolis-Hastings Robbins-Monro algorithm which may include polynomial or product constructed latent traits. Multiple group analysis and mixed effects designs may be performed for unidimensional or multidimensional item response models for detecting differential item functioning and modelling item and person covariates.
It's recommended to use the development version of this package since it is more likely to be up to date than the version on CRAN. To install this package from source:
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Obtain recent gcc and g++ compilers. Windows users can install the Rtools suite while Mac users will have to download the necessary tools from the Xcode suite and its related command line tools (found within Xcode's Preference Pane under Downloads/Components); most Linux distributions should already have up to date compilers (or if not they can be updated easily).
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Install the
devtoolspackage (if necessary). In R, paste the following into the console:
install.packages('devtools')- Load the
devtoolspackage and install from the github source code.
library('devtools')
install_github('mirt', 'philchalmers', quick = TRUE)For those having difficulty installing the package on Windows, binary installation (.zip) files for 32- or 64-bit Windows may be installed with:
download.file('http://dl.dropbox.com/u/10780530/mirt/mirt.zip', 'mirt.zip')
install.packages('mirt.zip', repos=NULL)Note that this binary file is updated periodically and is not guarenteed to be in sync with the source code.
Bug reports are always welcome and the preferred way to address these bugs is through the github 'issues'. Feel free to submit issues or feature requests on the site, and I'll address them ASAP. Also, if you have any questions about the package, or IRT in general, then feel free to create a 'New Topic' in the mirt-package Google group. Cheers!