lsirm12pl: An R package for latent space item response modeling

Item response theory (IRT) is a widely used approach to measure test takers' latent traits from assessment data. IRT models typically require conditional independence and homogeneity assumptions that may be violated in practice when unobserved interactions between respondents and items exist. To alleviate these assumptions, Jeon et al. (2021) introduced a latent space item response model (LSIRM) for binary item response data by assuming that items and respondents are embedded in an unobserved metric space, called an interaction map, where the probability of a correct response decreasing as a function of the distance between the respondent's and the item's position in the latent space. The R package lsirm12pl implements the original and extended versions of LSIRM, while supplying several flexible modeling options: (1) an extension to the two-parameter model; (2) an extension to continuous item responses; and (3) handling missing responses. The R package lsirm12pl also offers convenient functions for visualizing estimated results, including the interaction map that represents item and person positions and their interactions. In this paper, we provide a brief overview of the methodological basis of LSIRM and describe the extensions that are considered in the package. We then showcase the use of the package lsirm12pl with real data examples that are contained in the package.