lsirm12pl: An R Package for the Latent Space Item Response Model

The item response model in latent space (LSIRM; Jeon et al. (2021)) uncovers unobserved interactions between respondents and items in the item response data by embedding both in a shared latent metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for various response types, base model specifications, and missing data handling. Furthermore, the lsirm12pl package provides methods to improve model utilization and interpretation, such as clustering item positions on an estimated interaction map. The package also offers convenient summary and plotting options to evaluate and process the estimated results. In this paper, we provide an overview of the LSIRM's methodological foundation and describe several extensions included in the package. We then demonstrate the use of the package with real data examples contained within it.

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Publication Details

Journal
The R Journal
Published
2025-10-21
DOI
https://doi.org/10.32614/rj-2025-021
Primary Topic
Psychometric Methodologies and Testing
Type
article
Field-Weighted Citation Impact
0.00

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article

lsirm12pl: An R Package for the Latent Space Item Response Model

Ick Hoon Jin, Minjeong Jeon, Dongyoung Go, Gwang-Hee Kim et al.
The R Journal
Psychometric Methodologies and Testing
article

lsirm12pl: An R Package for the Latent Space Item Response Model

Ick Hoon Jin, Minjeong Jeon, Dongyoung Go, Gwang-Hee Kim, Jun Yong Park, Jina Park
article en

Abstract

The item response model in latent space (LSIRM; Jeon et al. (2021)) uncovers unobserved interactions between respondents and items in the item response data by embedding both in a shared latent metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for various response types, base model specifications, and missing data handling. Furthermore, the lsirm12pl package provides methods to improve model utilization and interpretation, such as clustering item positions on an estimated interaction map. The package also offers convenient summary and plotting options to evaluate and process the estimated results. In this paper, we provide an overview of the LSIRM's methodological foundation and describe several extensions included in the package. We then demonstrate the use of the package with real data examples contained within it.

The R JournalVol. 17(3)
Yonsei University (KR), Samsung (South Korea) (KR), Defense Information School (US)
National Research Foundation, Yonsei University, National Research Foundation of Korea, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 56%
Psychometric Methodologies and Testing
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