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  "Title": "Latent Space Item Response Model",
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  "Date": "2026-05-28",
  "Authors@R": "c(person(\"Dongyoung\", \"Go\", role = c(\"aut\"),\nemail = \"dongyoung.gr@yonsei.ac.kr\"),\nperson(\"Gwanghee\", \"Kim\", role = c(\"aut\", \"cre\"),\nemail = \"musagh08@yonsei.ac.kr\"),\nperson(\"Jina\", \"Park\", role = c(\"aut\"),\nemail = \"pja070707@gmail.com\"),\nperson(\"Ickhoon\", \"Jin\", role = c(\"ctb\"),\nemail = \"ijin@yonsei.ac.kr\"),\nperson(\"Minjeong\", \"Jeon\", role = \"ctb\",\nemail = \"mjjeon@ucla.edu\"))",
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  "Description": "Analysis of dichotomous, ordinal, and continuous response\ndata using latent space item response model ('LSIRM'). Provides\n1PL and 2PL 'LSIRM' for binary response data as described in\nJeon et al. (2021) <doi:10.1007/s11336-021-09762-5>, graded\nresponse models ('GRM') for ordinal data (De Carolis et al.,\n2025, <doi:10.1080/00273171.2025.2605678>), and extensions for\ncontinuous response data. Supports Bayesian model selection\nwith spike-and-slab priors, adaptive MCMC algorithms, and\nmethods for handling missing data under missing at random\n('MAR') and missing completely at random ('MCAR') assumptions.\nProvides various diagnostic plots to inspect the latent space\nand summaries of estimated parameters.",
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      "topics": [
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      "title": "1PL LSIRM with normal likelihood and model selection approach.",
      "topics": [
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      "title": "1PL LSIRM.",
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