The latent space item response model (LSIRM; Jeon et al., 2021) allows us to show interactions between respondents and items in item response data by embedding both items and respondents in a shared and unobserved metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for different response types, base model specifications, and missing data. Further, the lsirm12pl offers methods to improve model utilization and interpretation, such as clustering of item positions in an estimated interaction map. lsirm12pl also provides convenient summary and plotting options to assess and process estimated results. In this paper, we give an overview of the methodological basis of LSIRM and describe the LSIRM extensions considered in the package. We then present the utilization of the package lsirm12pl with real data examples that are contained in the package.

翻译：潜空间项目反应模型（LSIRM；Jeon等，2021）通过将项目与受访者嵌入共享的未观测度量空间，揭示了项目反应数据中受访者与项目之间的交互关系。R包lsirm12pl实现了LSIRM及其扩展模型的贝叶斯估计，涵盖不同响应类型、基础模型规范及缺失数据处理。此外，lsirm12pl提供了改进模型应用与解释的方法，例如对估计交互地图中项目位置进行聚类。该包还包含便捷的汇总与绘图功能，用于评估和处理估计结果。本文概述了LSIRM的方法论基础，描述了包中考虑的LSIRM扩展模型，并通过包内附的真实数据示例展示了lsirm12pl的使用方法。