{"id":"https://openalex.org/W3092198449","doi":"https://doi.org/10.32614/rj-2020-025","title":"CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R","display_name":"CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3092198449","doi":"https://doi.org/10.32614/rj-2020-025","mag":"3092198449"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2020-025","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2020-025","pdf_url":"https://journal.r-project.org/archive/2020/RJ-2020-025/RJ-2020-025.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/archive/2020/RJ-2020-025/RJ-2020-025.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013878664","display_name":"Tao Sun","orcid":"https://orcid.org/0000-0001-8828-1174"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Sun","raw_affiliation_strings":["59 Zhongguancun Street Beijing, China Department of Biostatistics University of Pittsburgh Pittsburgh, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"59 Zhongguancun Street Beijing, China Department of Biostatistics University of Pittsburgh Pittsburgh, U.S.A","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072424476","display_name":"Ying Ding","orcid":"https://orcid.org/0000-0003-1352-1000"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Ding","raw_affiliation_strings":["59 Zhongguancun Street Beijing, China Department of Biostatistics University of Pittsburgh Pittsburgh, U.S.A","Department of Biostatistics University of Pittsburgh 130 De Soto Street Pittsburgh, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"59 Zhongguancun Street Beijing, China Department of Biostatistics University of Pittsburgh Pittsburgh, U.S.A","institution_ids":["https://openalex.org/I170201317"]},{"raw_affiliation_string":"Department of Biostatistics University of Pittsburgh 130 De Soto Street Pittsburgh, U.S.A","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":1.6006,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.85356146,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"12","issue":"1","first_page":"266","last_page":"266"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.989799976348877,"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/T10136","display_name":"Statistical Methods and Inference","score":0.989799976348877,"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/T13398","display_name":"Data Analysis with R","score":0.980400025844574,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9745000004768372,"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/copula","display_name":"Copula (linguistics)","score":0.870663046836853},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.8591941595077515},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5642471313476562},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5289473533630371},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4338500499725342},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3461924195289612},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3236082196235657}],"concepts":[{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.870663046836853},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.8591941595077515},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5642471313476562},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5289473533630371},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4338500499725342},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3461924195289612},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3236082196235657}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.32614/rj-2020-025","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2020-025","pdf_url":"https://journal.r-project.org/archive/2020/RJ-2020-025/RJ-2020-025.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"},{"id":"pmh:oai:digitalcommons.unl.edu:r-journal-1171","is_oa":false,"landing_page_url":"https://digitalcommons.unl.edu/r-journal/169","pdf_url":null,"source":{"id":"https://openalex.org/S4306400577","display_name":"Lincoln (University of Nebraska)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114395901","host_organization_name":"University of Nebraska\u2013Lincoln","host_organization_lineage":["https://openalex.org/I114395901"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"The R Journal","raw_type":"text"}],"best_oa_location":{"id":"doi:10.32614/rj-2020-025","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2020-025","pdf_url":"https://journal.r-project.org/archive/2020/RJ-2020-025/RJ-2020-025.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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3092198449.pdf","grobid_xml":"https://content.openalex.org/works/W3092198449.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W390686421","https://openalex.org/W573849129","https://openalex.org/W1484726329","https://openalex.org/W1834081490","https://openalex.org/W1866990630","https://openalex.org/W1911601650","https://openalex.org/W1944095157","https://openalex.org/W1978290902","https://openalex.org/W1992105816","https://openalex.org/W2001452592","https://openalex.org/W2047164911","https://openalex.org/W2049821595","https://openalex.org/W2061984794","https://openalex.org/W2087971008","https://openalex.org/W2092648747","https://openalex.org/W2093849599","https://openalex.org/W2094336602","https://openalex.org/W2122414444","https://openalex.org/W2325574687","https://openalex.org/W2328015960","https://openalex.org/W2328176873","https://openalex.org/W2334535890","https://openalex.org/W2337474841","https://openalex.org/W2403080995","https://openalex.org/W2561970890","https://openalex.org/W2566154985","https://openalex.org/W2582743722","https://openalex.org/W2595554554","https://openalex.org/W2609732036","https://openalex.org/W2905159338","https://openalex.org/W2921315344","https://openalex.org/W2972513703","https://openalex.org/W3121519025","https://openalex.org/W3147894994","https://openalex.org/W3158113069","https://openalex.org/W4234829730","https://openalex.org/W4235092540","https://openalex.org/W4241781167","https://openalex.org/W4244113778","https://openalex.org/W4249107966","https://openalex.org/W4255255215","https://openalex.org/W4288400169","https://openalex.org/W4300705184","https://openalex.org/W4399537407","https://openalex.org/W4399551621","https://openalex.org/W4399570722","https://openalex.org/W4399572398","https://openalex.org/W4399613607","https://openalex.org/W4399649685"],"related_works":["https://openalex.org/W2202466617","https://openalex.org/W2990837948","https://openalex.org/W3127236696","https://openalex.org/W2080551413","https://openalex.org/W2756923888","https://openalex.org/W4287838929","https://openalex.org/W2359012312","https://openalex.org/W4288017135","https://openalex.org/W2767176313","https://openalex.org/W3177005804"],"abstract_inverted_index":{"Bivariate":[0],"time-to-event":[1],"data":[2,76,165],"frequently":[3],"arise":[4],"in":[5,48,83,172],"research":[6],"areas":[7],"such":[8],"as":[9],"clinical":[10],"trials":[11],"and":[12,54,73,100,107,126,154],"epidemiological":[13],"studies,":[14],"where":[15],"the":[16,26,50,55,64,121,145,169],"occurrence":[17],"of":[18,91,103],"two":[19,51,163],"events":[20],"are":[21,30,58,158],"correlated.":[22],"In":[23,112],"many":[24],"cases,":[25],"exact":[27],"event":[28],"times":[29],"unknown":[31],"due":[32],"to":[33,167],"censoring.":[34],"The":[35,134],"copula":[36,93,99],"model":[37,119],"is":[38,69,137],"a":[39,84,89,96,116,140],"popular":[40],"approach":[41],"for":[42,71,109,120],"modeling":[43,72],"correlated":[44],"bivariate":[45,75],"censored":[46],"data,":[47],"which":[49,68],"marginal":[52,110],"distributions":[53],"betweenmargin":[56],"dependence":[57],"modeled":[59],"separately.":[60],"This":[61],"article":[62],"presents":[63],"R":[65],"package":[66],"CopulaCenR,":[67],"designed":[70],"testing":[74],"under":[77],"right":[78],"or":[79],"(general)":[80],"interval":[81],"censoring":[82],"regression":[85,104,146],"setting.":[86],"It":[87],"provides":[88],"variety":[90],"Archimedean":[92],"functions":[94,171],"including":[95],"flexible":[97],"two-parameter":[98],"different":[101],"types":[102],"models":[105,129],"(parametric":[106],"semiparametric)":[108],"distributions.":[111],"particular,":[113],"it":[114],"implements":[115],"semiparametric":[117],"transformation":[118],"margins":[122],"with":[123],"proportional":[124,127],"hazards":[125],"odds":[128],"being":[130],"its":[131],"special":[132],"cases.":[133],"numerical":[135],"optimization":[136],"based":[138],"on":[139],"novel":[141],"two-step":[142],"algorithm.":[143],"For":[144],"parameters,":[147],"three":[148],"likelihood-based":[149],"tests":[150],"(Wald,":[151],"generalized":[152],"score":[153],"likelihood":[155],"ratio":[156],"tests)":[157],"also":[159],"provided.":[160],"We":[161],"use":[162],"real":[164],"examples":[166],"illustrate":[168],"key":[170],"CopulaCenR.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
