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

lsirm12pl: An R package for latent space item response modeling