{"id":"https://openalex.org/W4410764942","doi":"https://doi.org/10.32614/rj-2024-017","title":"BCClong: An R Package for Bayesian Consensus Clustering for Multiple Longitudinal Features","display_name":"BCClong: An R Package for Bayesian Consensus Clustering for Multiple Longitudinal Features","publication_year":2025,"publication_date":"2025-03-09","ids":{"openalex":"https://openalex.org/W4410764942","doi":"https://doi.org/10.32614/rj-2024-017"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2024-017","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2024-017","pdf_url":"https://journal.r-project.org/articles/RJ-2024-017/RJ-2024-017.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-2024-017/RJ-2024-017.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008244304","display_name":"Zhiwen Tan","orcid":"https://orcid.org/0000-0003-1840-660X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhiwen Tan","raw_affiliation_strings":["Department of Public Health Sciences"],"affiliations":[{"raw_affiliation_string":"Department of Public Health Sciences","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109061225","display_name":"Chang Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang Shen","raw_affiliation_strings":["Department of Electrical and Computer Engineering"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055558951","display_name":"Zihang Lu","orcid":"https://orcid.org/0000-0002-3749-4476"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zihang Lu","raw_affiliation_strings":["Department of Public Health Sciences"],"affiliations":[{"raw_affiliation_string":"Department of Public Health Sciences","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008244304"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05194517,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"2","first_page":"41","last_page":"61"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.7303060293197632},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6731246709823608},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6141963005065918},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.540439784526825},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48471832275390625},{"id":"https://openalex.org/keywords/longitudinal-data","display_name":"Longitudinal data","score":0.44356557726860046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3165733218193054},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.27024808526039124},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.1227857768535614},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08323150873184204},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.050391197204589844}],"concepts":[{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.7303060293197632},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6731246709823608},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6141963005065918},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.540439784526825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48471832275390625},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.44356557726860046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3165733218193054},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27024808526039124},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.1227857768535614},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08323150873184204},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.050391197204589844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32614/rj-2024-017","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2024-017","pdf_url":"https://journal.r-project.org/articles/RJ-2024-017/RJ-2024-017.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-2024-017","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2024-017","pdf_url":"https://journal.r-project.org/articles/RJ-2024-017/RJ-2024-017.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":[{"id":"https://openalex.org/F4320321832","display_name":"Queen's University","ror":"https://ror.org/02y72wh86"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410764942.pdf","grobid_xml":"https://content.openalex.org/works/W4410764942.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1519488069","https://openalex.org/W1549853756","https://openalex.org/W1934579301","https://openalex.org/W1951724000","https://openalex.org/W1974198014","https://openalex.org/W1979742136","https://openalex.org/W2015558203","https://openalex.org/W2021793765","https://openalex.org/W2057765075","https://openalex.org/W2095763169","https://openalex.org/W2098439089","https://openalex.org/W2102651926","https://openalex.org/W2121201519","https://openalex.org/W2123222757","https://openalex.org/W2135497302","https://openalex.org/W2159135906","https://openalex.org/W2159325249","https://openalex.org/W2168934042","https://openalex.org/W2537229494","https://openalex.org/W2572590842","https://openalex.org/W2791334082","https://openalex.org/W2806456507","https://openalex.org/W2904833574","https://openalex.org/W2911352402","https://openalex.org/W3101294569","https://openalex.org/W3101380508","https://openalex.org/W3137145701","https://openalex.org/W3205527824","https://openalex.org/W3208201488","https://openalex.org/W4220916000","https://openalex.org/W4248679889","https://openalex.org/W4306820107","https://openalex.org/W4328105806","https://openalex.org/W4387326446","https://openalex.org/W4388622580","https://openalex.org/W4399524164","https://openalex.org/W4399612446","https://openalex.org/W4399635823","https://openalex.org/W4399721808","https://openalex.org/W6600144256"],"related_works":["https://openalex.org/W2588528840","https://openalex.org/W2199594781","https://openalex.org/W2767663474","https://openalex.org/W2294414050","https://openalex.org/W2744974730","https://openalex.org/W2285903080","https://openalex.org/W4386076172","https://openalex.org/W4306311884","https://openalex.org/W2584267197","https://openalex.org/W1574003721"],"abstract_inverted_index":{"It":[0],"is":[1,41],"very":[2],"common":[3],"nowadays":[4],"for":[5,19,56],"a":[6,44,50,143],"study":[7],"to":[8,28,42,61,92],"collect":[9],"multiple":[10,57],"longitudinal":[11,58,84,90,111],"features":[12,17,66,120],"and":[13,32,82,132],"appropriately":[14],"integrating":[15],"these":[16],"simultaneously":[18],"defining":[20],"individual":[21],"subgroups":[22],"(i.e.,":[23],"clusters)":[24],"becomes":[25],"increasingly":[26],"crucial":[27],"understanding":[29],"population":[30],"heterogeneity":[31],"predicting":[33],"future":[34],"outcomes.":[35],"The":[36],"aim":[37],"of":[38,77,104],"this":[39],"paper":[40],"describe":[43],"new":[45],"package,":[46],"BCClong,":[47],"which":[48],"implements":[49],"Bayesian":[51],"consensus":[52,133],"clustering":[53,76,124],"(BCC)":[54],"model":[55],"features.":[59],"Compared":[60],"existing":[62],"packages,":[63],"several":[64],"key":[65],"make":[67],"the":[68,116,122,128,150],"BCClong":[69],"package":[70],"appealing:":[71],"(a)":[72],"it":[73,87,114],"allows":[74,88],"simultaneous":[75],"mixed-type":[78],"(e.g.,":[79],"continuous,":[80],"discrete":[81],"categorical)":[83],"features,":[85],"(b)":[86],"each":[89],"feature":[91],"be":[93],"collected":[94],"from":[95],"different":[96],"sources":[97],"with":[98,145],"measurements":[99],"taken":[100],"at":[101],"distinct":[102],"sets":[103],"time":[105],"points":[106],"(known":[107],"as":[108],"irregularly":[109],"sampled":[110],"data),":[112],"(c)":[113],"relaxes":[115],"assumption":[117],"that":[118],"all":[119],"have":[121],"same":[123],"structure":[125],"by":[126],"estimating":[127],"feature-specific":[129],"(local)":[130],"clusterings":[131],"(global)":[134],"clustering.":[135],"Using":[136],"two":[137],"real":[138],"data":[139],"examples,":[140],"we":[141],"provide":[142],"tutorial":[144],"step-by-step":[146],"instructions":[147],"on":[148],"using":[149],"package.":[151]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}