Package: emIRT 0.0.14

emIRT: EM Algorithms for Estimating Item Response Theory Models

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>.

Authors:Kosuke Imai <[email protected]>, James Lo <[email protected]>, Jonathan Olmsted <[email protected]>

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emIRT.pdf |emIRT.html
emIRT/json (API)

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

Peer review:

Bug tracker:https://github.com/kosukeimai/emirt/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • AsahiTodai - Asahi-Todai Elite Survey
  • dwnom - Poole-Rosenthal DW-NOMINATE data and scores, 80-110 U.S. Senate
  • manifesto - German Manifesto Data
  • mq_data - Martin-Quinn Judicial Ideology Scores
  • ustweet - U.S. Twitter Following Data

On CRAN:

openblascppopenmp

5.43 score 26 stars 26 scripts 238 downloads 10 exports 4 dependencies

Last updated 7 months agofrom:0c44dedb20. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 04 2025
R-4.5-win-x86_64OKJan 04 2025
R-4.5-linux-x86_64OKJan 04 2025
R-4.4-win-x86_64OKJan 04 2025
R-4.4-mac-x86_64OKJan 04 2025
R-4.4-mac-aarch64OKJan 04 2025
R-4.3-win-x86_64OKJan 04 2025
R-4.3-mac-x86_64OKJan 04 2025
R-4.3-mac-aarch64OKJan 04 2025

Exports:binIRTboot_emIRTconvertRCdynIRTgetStartshierIRTmakePriorsnetworkIRTordIRTpoisIRT

Dependencies:MASSpsclRcppRcppArmadillo