Package: emIRT 0.0.14

Kosuke Imai

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 <imai@harvard.edu>, James Lo <jameslo989@gmail.com>, Jonathan Olmsted <jpolmsted@gmail.com>

emIRT_0.0.14.tar.gz
emIRT_0.0.14.tar.gz(r-4.5-noble)emIRT_0.0.14.tar.gz(r-4.4-noble)
emIRT_0.0.14.tgz(r-4.4-emscripten)emIRT_0.0.14.tgz(r-4.3-emscripten)
emIRT.pdf |emIRT.html✨
emIRT/json (API)

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

Conda-Forge:

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

openblascppopenmp

1.00 score 1 stars 324 downloads 10 exports 4 dependencies

Last updated 8 months agofrom:521131d4a2. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 04 2025
R-4.5-linux-x86_64OKMar 04 2025
R-4.4-linux-x86_64OKMar 04 2025

Exports:binIRTboot_emIRTconvertRCdynIRTgetStartshierIRTmakePriorsnetworkIRTordIRTpoisIRT

Dependencies:MASSpsclRcppRcppArmadillo