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 <[email protected]>, James Lo <[email protected]>, Jonathan Olmsted <[email protected]>

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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

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

openblascppopenmp

1.40 score 1 stars 25 scripts 268 downloads 10 exports 4 dependencies

Last updated 5 months agofrom:521131d4a2. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linux-x86_64OKDec 04 2024

Exports:binIRTboot_emIRTconvertRCdynIRTgetStartshierIRTmakePriorsnetworkIRTordIRTpoisIRT

Dependencies:MASSpsclRcppRcppArmadillo