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Package: emIRT | ||
Type: Package | ||
Title: EM Algorithms for Estimating Item Response Theory Models | ||
Version: 0.0.12 | ||
Date: 2021-05-12 | ||
Version: 0.0.13 | ||
Date: 2022-02-14 | ||
Author: Kosuke Imai <[email protected]>, James Lo <[email protected]>, Jonathan Olmsted <[email protected]> | ||
Maintainer: James Lo <[email protected]> | ||
Description: Various Expectation-Maximization (EM) algorithms are implemented for item response theory | ||
(IRT) models. The current implementation includes IRT models for binary and ordinal | ||
responses, along with dynamic and hierarchical IRT models with binary responses. The | ||
latter two models are derived and implemented using variational EM. Subsequent edits | ||
also include variational network and text scaling models. | ||
Description: Various Expectation-Maximization (EM) algorithms are implemented for item | ||
response theory (IRT) models. The package includes IRT models for binary and ordinal | ||
responses, along with dynamic and hierarchical IRT models with binary responses. The | ||
latter two models are fitted using variational EM. The package also includes | ||
variational network and text scaling models. The algorithms are described in Imai, Lo, | ||
and Olmsted (2016) <DOI:10.1017/S000305541600037X>. | ||
License: GPL (>= 3) | ||
Depends: R (>= 2.10), pscl (>= 1.0.0), Rcpp (>= 0.10.6) | ||
Suggests: MCMCpack | ||
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