{"id":"https://openalex.org/W7203663804","doi":"https://doi.org/10.32614/rj-2026-023","title":"exametrika: Test Data Engineering with R","display_name":"exametrika: Test Data Engineering with R","publication_year":2026,"publication_date":"2026-07-04","ids":{"openalex":"https://openalex.org/W7203663804","doi":"https://doi.org/10.32614/rj-2026-023"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2026-023","doi":"https://doi.org/10.32614/rj-2026-023","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2026-023","pdf_url":"https://journal.r-project.org/articles/RJ-2026-023/RJ-2026-023.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,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/I4402554076","host_organization_name":"R Foundation for Statistical Computing","host_organization_lineage":["https://openalex.org/I4402554076"],"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"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journal.r-project.org/articles/RJ-2026-023/RJ-2026-023.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137707812","display_name":"Koji Kosugi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I85283370","display_name":"Senshu University","ror":"https://ror.org/03jzxkg37","country_code":"JP","type":"education","lineage":["https://openalex.org/I85283370"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koji Kosugi","raw_affiliation_strings":["Senshu University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Senshu University","institution_ids":["https://openalex.org/I85283370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002116410","display_name":"Kojiro Shojima","orcid":"https://orcid.org/0000-0002-9074-1704","observed_orcids":["https://orcid.org/0000-0002-9074-1704"]},"institutions":[{"id":"https://openalex.org/I15922215","display_name":"National Center for University Entrance Examinations","ror":"https://ror.org/0182n4f20","country_code":"JP","type":"education","lineage":["https://openalex.org/I1319490839","https://openalex.org/I15922215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kojiro Shojima","raw_affiliation_strings":["The National Center for University Entrance Examinations"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The National Center for University Entrance Examinations","institution_ids":["https://openalex.org/I15922215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"2","first_page":"85","last_page":"102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.9907000064849854,"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":0.9907000064849854,"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/T14025","display_name":"Educational Technology and Assessment","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12884","display_name":"Educational Assessment and Pedagogy","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/3304","display_name":"Education"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/latent-class-analysis","display_name":"latent class analysis","score":0.7720000147819519},{"id":"https://openalex.org/keywords/biclustering","display_name":"biclustering","score":0.9580000042915344},{"id":"https://openalex.org/keywords/bayesian-network-model","display_name":"Bayesian network model","score":0.8339999914169312},{"id":"https://openalex.org/keywords/item-response-theory","display_name":"item response theory","score":0.6520000100135803},{"id":"https://openalex.org/keywords/classical-test-theory","display_name":"classical test theory","score":0.9340000152587891},{"id":"https://openalex.org/keywords/item-characteristics","display_name":"item characteristics","score":0.7300000190734863},{"id":"https://openalex.org/keywords/em-algorithm-parameter-estimation","display_name":"EM algorithm parameter estimation","score":0.6259999871253967}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6802999973297119},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5522000193595886},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5397999882698059},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5062999725341797},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4970000088214874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4964999854564667},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4918999969959259},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.48489999771118164},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4607999920845032},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4415000081062317},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.41589999198913574},{"id":"https://openalex.org/C144352353","wikidata":"https://www.wikidata.org/wiki/Q2920411","display_name":"Computerized adaptive testing","level":3,"score":0.4156999886035919},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.36000001430511475},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.2565000057220459},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32614/rj-2026-023","doi":"https://doi.org/10.32614/rj-2026-023","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2026-023","pdf_url":"https://journal.r-project.org/articles/RJ-2026-023/RJ-2026-023.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,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/I4402554076","host_organization_name":"R Foundation for Statistical Computing","host_organization_lineage":["https://openalex.org/I4402554076"],"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-2026-023","doi":"https://doi.org/10.32614/rj-2026-023","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2026-023","pdf_url":"https://journal.r-project.org/articles/RJ-2026-023/RJ-2026-023.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,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/I4402554076","host_organization_name":"R Foundation for Statistical Computing","host_organization_lineage":["https://openalex.org/I4402554076"],"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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality education","score":0.6164000034332275}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7203663804.pdf","grobid_xml":"https://content.openalex.org/works/W7203663804.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W778476381","https://openalex.org/W2025078084","https://openalex.org/W2192103675","https://openalex.org/W2895537499"],"related_works":["https://openalex.org/W4412951346","https://openalex.org/W4367318807","https://openalex.org/W6944239912","https://openalex.org/W2036390914","https://openalex.org/W1770100412","https://openalex.org/W4309867276","https://openalex.org/W4226298645","https://openalex.org/W1680939745","https://openalex.org/W4291200960","https://openalex.org/W2583378560"],"abstract_inverted_index":{"The":[0,23,130],"exametrika":[1,168],"package":[2,24,131],"implements":[3],"statistical":[4],"models":[5,27,40,53,105,110],"for":[6,18,135],"test":[7,21,33,107,119,154],"data":[8,120],"engineering":[9],"proposed":[10],"by":[11,166],"Shojima":[12],"(2022),":[13],"providing":[14],"a":[15],"comprehensive":[16],"framework":[17],"analyzing":[19],"educational":[20,153],"data.":[22,108],"offers":[25],"eleven":[26],"organized":[28],"into":[29],"three":[30],"categories:":[31],"classical":[32],"theory":[34],"methods":[35],"(CTT,":[36],"IRT),":[37],"latent":[38,44,75,79,82],"structure":[39,76],"(latent":[41],"class":[42,80],"analysis,":[43,46,81,84],"rank":[45,83],"biclustering,":[47,62,85],"and":[48,50,66,86,121,127,137,149],"Ranklustering),":[49],"advanced":[51,95],"network":[52,55,64,69,96,104],"(Bayesian":[54],"model,":[56,65],"locally":[57,60],"dependent":[58,61],"LRA,":[59],"bicluster":[63,68],"deep":[67],"model).":[70],"This":[71],"paper":[72],"focuses":[73],"on":[74],"models,":[77,97],"including":[78],"Ranklustering,":[87],"which":[88],"combines":[89],"rank-based":[90],"ordering":[91],"with":[92],"clustering.":[93],"For":[94],"we":[98,156],"demonstrate":[99,157],"the":[100,158,161,167],"application":[101],"of":[102,124,144,160],"Bayesian":[103],"to":[106,113,152],"These":[109],"are":[111],"designed":[112],"capture":[114],"complex":[115],"structures":[116],"inherent":[117],"in":[118],"deepen":[122],"understanding":[123],"examinee":[125],"abilities":[126],"item":[128],"characteristics.":[129],"employs":[132],"EM":[133],"algorithms":[134],"efficient":[136],"accurate":[138],"parameter":[139],"estimation.":[140],"Through":[141],"detailed":[142],"explanations":[143],"theoretical":[145],"backgrounds,":[146],"implementation":[147],"methods,":[148],"practical":[150],"applications":[151],"data,":[155],"utility":[159],"diverse":[162],"analytical":[163],"approaches":[164],"provided":[165],"package.":[169]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-08-19T00:00:00"}
