{"id":"https://openalex.org/W7093073889","doi":"https://doi.org/10.32614/rj-2025-003","title":"LCCR: An R Package for Inference on Latent Class Models for Capture-Recapture Data with Covariates","display_name":"LCCR: An R Package for Inference on Latent Class Models for Capture-Recapture Data with Covariates","publication_year":2025,"publication_date":"2025-08-07","ids":{"openalex":"https://openalex.org/W7093073889","doi":"https://doi.org/10.32614/rj-2025-003"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2025-003","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-003","pdf_url":"https://journal.r-project.org/articles/RJ-2025-003/RJ-2025-003.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-2025-003/RJ-2025-003.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Francesco Bartolucci","orcid":null},"institutions":[{"id":"https://openalex.org/I27483092","display_name":"University of Perugia","ror":"https://ror.org/00x27da85","country_code":"IT","type":"education","lineage":["https://openalex.org/I27483092"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Bartolucci","raw_affiliation_strings":["Department of Economics, University of Perugia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Economics, University of Perugia","institution_ids":["https://openalex.org/I27483092"]}]},{"author_position":"last","author":{"id":null,"display_name":"Antonio Forcina","orcid":null},"institutions":[{"id":"https://openalex.org/I27483092","display_name":"University of Perugia","ror":"https://ror.org/00x27da85","country_code":"IT","type":"education","lineage":["https://openalex.org/I27483092"]},{"id":"https://openalex.org/I9568059","display_name":"University for Foreigners Perugia","ror":"https://ror.org/047302w76","country_code":"IT","type":"education","lineage":["https://openalex.org/I9568059"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Antonio Forcina","raw_affiliation_strings":["Formerly at University of Perugia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Formerly at University of Perugia","institution_ids":["https://openalex.org/I9568059","https://openalex.org/I27483092"]}]}],"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":0.58148428,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"1","first_page":"47","last_page":"66"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T13546","display_name":"Census and Population Estimation","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13546","display_name":"Census and Population Estimation","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11860","display_name":"HIV, Drug Use, Sexual Risk","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.7092000246047974},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.5820000171661377},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4796999990940094},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.47189998626708984},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.46000000834465027},{"id":"https://openalex.org/keywords/mixed-logit","display_name":"Mixed logit","score":0.43160000443458557},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4147000014781952},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.41119998693466187},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.4034000039100647},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.36149999499320984}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7092000246047974},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.5820000171661377},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5745000243186951},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5557000041007996},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4848000109195709},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4796999990940094},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C95057490","wikidata":"https://www.wikidata.org/wiki/Q6883984","display_name":"Mixed logit","level":3,"score":0.43160000443458557},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34389999508857727},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C94465730","wikidata":"https://www.wikidata.org/wiki/Q589603","display_name":"Logistic distribution","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.3125},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C148264743","wikidata":"https://www.wikidata.org/wiki/Q4919224","display_name":"Bivariate data","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.28220000863075256},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C205167067","wikidata":"https://www.wikidata.org/wiki/Q3300636","display_name":"Interval estimation","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32614/rj-2025-003","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-003","pdf_url":"https://journal.r-project.org/articles/RJ-2025-003/RJ-2025-003.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-2025-003","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-003","pdf_url":"https://journal.r-project.org/articles/RJ-2025-003/RJ-2025-003.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":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7093073889.pdf","grobid_xml":"https://content.openalex.org/works/W7093073889.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"detailed":[1],"description":[2],"of":[3,11,28,50,58,88,102,119,128,139,159,178,188],"the":[4,9,29,46,56,59,64,85,94,99,117,125,129,132,137,140,147,157,186,189,194],"R":[5],"package":[6,44,174],"LCCR":[7],"for":[8,33,84,116,156,193],"analysis":[10],"capture-recapture":[12,179],"data":[13,22,180,202],"relating":[14],"to":[15,154],"a":[16,71,81,110,182,201],"closed":[17],"population":[18,195],"is":[19,144],"provided.":[20],"The":[21,43,173],"that":[23,113],"can":[24,106,134],"be":[25,107],"analyzed":[26],"consist":[27],"full":[30],"capture":[31,60,89,96,104,121,130],"history":[32,61],"each":[34,91,103],"sample":[35],"unit":[36],"and":[37,48,67,185],"may":[38,114],"possibly":[39],"include":[40],"individual":[41],"covariates.":[42],"allows":[45,176],"specification":[47],"estimation":[49],"latent":[51,65],"class":[52,66,141],"models":[53],"in":[54,74,169,207],"which":[55,75,165],"distribution":[57,101,127,138],"conditional":[62,86,100,126],"on":[63,146],"covariates":[68,133,160],"follows":[69],"either":[70],"log-linear":[72],"model":[73,83,112,184],"bivariate":[76],"interactions":[77],"are":[78,166],"allowed,":[79],"or":[80],"logit":[82,111],"probability":[87],"at":[90],"occasion":[92,105],"given":[93],"previous":[95,120],"history.":[97],"Alternatively,":[98],"formulated":[108],"by":[109,161],"account":[115,155],"effect":[118],"occasions.":[122],"Apart":[123],"from":[124,181],"history,":[131],"also":[135,175],"affect":[136],"weights.":[142],"Estimation":[143],"based":[145],"unconditional":[148],"maximum":[149],"likelihood":[150,171],"method,":[151],"suitably":[152],"extended":[153],"presence":[158],"including":[162],"unit-specific":[163],"weights,":[164],"commonly":[167],"used":[168],"empirical":[170],"methods.":[172],"simulation":[177],"specified":[183],"computation":[187],"profile":[190],"confidence":[191],"interval":[192],"size.":[196],"For":[197],"illustration,":[198],"we":[199],"use":[200],"set":[203],"about":[204],"meningitis":[205],"cases":[206],"an":[208],"Italian":[209],"region.":[210]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-22T00:00:00"}