{"id":"https://openalex.org/W4416977341","doi":"https://doi.org/10.32614/rj-2025-021","title":"lsirm12pl: An R Package for the Latent Space Item Response Model","display_name":"lsirm12pl: An R Package for the Latent Space Item Response Model","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4416977341","doi":"https://doi.org/10.32614/rj-2025-021"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2025-021","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-021","pdf_url":"https://journal.r-project.org/articles/RJ-2025-021/RJ-2025-021.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The R Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journal.r-project.org/articles/RJ-2025-021/RJ-2025-021.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070630588","display_name":"Dongyoung Go","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongyoung Go","raw_affiliation_strings":["Department of Statistics and Data Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074889924","display_name":"Gwang-Hee Kim","orcid":"https://orcid.org/0000-0002-7715-9496"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gwanghee Kim","raw_affiliation_strings":["Department of Statistics and Data Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100752307","display_name":"Jina Park","orcid":"https://orcid.org/0009-0000-8703-5094"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]},{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jina Park","raw_affiliation_strings":["Department of Statistics and Data Science","Samsung Electronics, Formerly at Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science","institution_ids":[]},{"raw_affiliation_string":"Samsung Electronics, Formerly at Yonsei University","institution_ids":["https://openalex.org/I2250650973","https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397347","display_name":"Jun Yong Park","orcid":"https://orcid.org/0000-0001-6324-2224"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]},{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junyong Park","raw_affiliation_strings":["Department of Statistics and Data Science","Samsung Electronics, Formerly at Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science","institution_ids":[]},{"raw_affiliation_string":"Samsung Electronics, Formerly at Yonsei University","institution_ids":["https://openalex.org/I2250650973","https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044502209","display_name":"Minjeong Jeon","orcid":"https://orcid.org/0000-0002-5880-4146"},"institutions":[{"id":"https://openalex.org/I2801768934","display_name":"Defense Information School","ror":"https://ror.org/04t5yz624","country_code":"US","type":"education","lineage":["https://openalex.org/I1330347796","https://openalex.org/I2801768934"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minjeong Jeon","raw_affiliation_strings":["School of Education and Information Studies"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Education and Information Studies","institution_ids":["https://openalex.org/I2801768934"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043640771","display_name":"Ick Hoon Jin","orcid":"https://orcid.org/0000-0003-0647-5704"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ick Hoon Jin","raw_affiliation_strings":["Department of Statistics and Data Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39201863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"3","first_page":"4","last_page":"29"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.9326000213623047,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.9326000213623047,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.02410000003874302,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13542","display_name":"Grit, Self-Efficacy, and Motivation","score":0.003700000001117587,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/item-response-theory","display_name":"Item response theory","score":0.5976999998092651},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5701000094413757},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5175999999046326},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.4959999918937683},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.46860000491142273},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.46459999680519104},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.424699991941452},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.40950000286102295},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.40139999985694885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6528000235557556},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.5976999998092651},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5701000094413757},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.4959999918937683},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46860000491142273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4650999903678894},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.424699991941452},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.36399999260902405},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.358599990606308},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34040001034736633},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.3061000108718872},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C181447626","wikidata":"https://www.wikidata.org/wiki/Q1224359","display_name":"Differential item functioning","level":4,"score":0.2825999855995178},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27619999647140503},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.26809999346733093},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32614/rj-2025-021","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-021","pdf_url":"https://journal.r-project.org/articles/RJ-2025-021/RJ-2025-021.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The R Journal","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32614/rj-2025-021","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-021","pdf_url":"https://journal.r-project.org/articles/RJ-2025-021/RJ-2025-021.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The R Journal","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2961090989","display_name":null,"funder_award_id":"NRF-2020R1A2C1A01009881","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G3978225007","display_name":null,"funder_award_id":"RS-2023-00259934","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G7514333950","display_name":null,"funder_award_id":"RS-2023-00217705","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321314","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416977341.pdf","grobid_xml":"https://content.openalex.org/works/W4416977341.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,33,79],"item":[1,21,72],"response":[2,22,48],"model":[3,51,65],"in":[4,19,27,112],"latent":[5,30],"space":[6],"(LSIRM;":[7],"Jeon":[8],"et":[9],"al.":[10],"(2021))":[11],"uncovers":[12],"unobserved":[13],"interactions":[14],"between":[15],"respondents":[16],"and":[17,43,53,67,85,90,107],"items":[18],"the":[20,41,58,92,103,113,118,121],"data":[23,55,125],"by":[24],"embedding":[25],"both":[26],"a":[28],"shared":[29],"metric":[31],"space.":[32],"R":[34],"package":[35,60,80,122],"lsirm12pl":[36,59],"implements":[37],"Bayesian":[38],"estimation":[39],"of":[40,102,120],"LSIRM":[42],"its":[44],"extensions":[45,110],"for":[46],"various":[47],"types,":[49],"base":[50],"specifications,":[52],"missing":[54],"handling.":[56],"Furthermore,":[57],"provides":[61],"methods":[62],"to":[63,88],"improve":[64],"utilization":[66],"interpretation,":[68],"such":[69],"as":[70],"clustering":[71],"positions":[73],"on":[74],"an":[75,100],"estimated":[76,93],"interaction":[77],"map.":[78],"also":[81],"offers":[82],"convenient":[83],"summary":[84],"plotting":[86],"options":[87],"evaluate":[89],"process":[91],"results.":[94],"In":[95],"this":[96],"paper,":[97],"we":[98],"provide":[99],"overview":[101],"LSIRM's":[104],"methodological":[105],"foundation":[106],"describe":[108],"several":[109],"included":[111],"package.":[114],"We":[115],"then":[116],"demonstrate":[117],"use":[119],"with":[123],"real":[124],"examples":[126],"contained":[127],"within":[128],"it.":[129]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-04T00:00:00"}