{"id":"https://openalex.org/W2790993737","doi":"https://doi.org/10.32614/rj-2015-019","title":"zoib: An R Package for Bayesian Inference for Beta Regression and Zero/One Inflated Beta Regression","display_name":"zoib: An R Package for Bayesian Inference for Beta Regression and Zero/One Inflated Beta Regression","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2790993737","doi":"https://doi.org/10.32614/rj-2015-019","mag":"2790993737"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2015-019","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2015-019","pdf_url":"https://journal.r-project.org/archive/2015/RJ-2015-019/RJ-2015-019.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":false,"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":"hybrid","oa_url":"https://journal.r-project.org/archive/2015/RJ-2015-019/RJ-2015-019.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100453123","display_name":"Fang Liu","orcid":"https://orcid.org/0000-0003-3028-5927"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fang Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5032281061","display_name":"Yunchuan Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yunchuan Kong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100453123"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.2319,"has_fulltext":true,"cited_by_count":116,"citation_normalized_percentile":{"value":0.96582752,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"7","issue":"2","first_page":"34","last_page":"34"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.5008000135421753,"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":0.5008000135421753,"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/beta","display_name":"BETA (programming language)","score":0.750557541847229},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6075419783592224},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5892612338066101},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.5386962294578552},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5345069169998169},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.5236915349960327},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4845661222934723},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4835391044616699},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.47286319732666016},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4637719988822937},{"id":"https://openalex.org/keywords/zero","display_name":"Zero (linguistics)","score":0.44882330298423767},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4073518216609955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32046377658843994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23408061265945435},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.08098125457763672}],"concepts":[{"id":"https://openalex.org/C2776174256","wikidata":"https://www.wikidata.org/wiki/Q830842","display_name":"BETA (programming language)","level":2,"score":0.750557541847229},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6075419783592224},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5892612338066101},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.5386962294578552},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5345069169998169},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.5236915349960327},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4845661222934723},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4835391044616699},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.47286319732666016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4637719988822937},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.44882330298423767},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4073518216609955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32046377658843994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23408061265945435},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.08098125457763672},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.32614/rj-2015-019","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2015-019","pdf_url":"https://journal.r-project.org/archive/2015/RJ-2015-019/RJ-2015-019.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":false,"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-1474","is_oa":false,"landing_page_url":"https://digitalcommons.unl.edu/r-journal/564","pdf_url":null,"source":{"id":"https://openalex.org/S4306513441","display_name":"Insecta mundi","issn_l":"0749-6737","issn":["0749-6737","1942-1354","1942-1362"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310316217","host_organization_name":"Center for Systematic Entomology","host_organization_lineage":["https://openalex.org/P4310316217"],"host_organization_lineage_names":["Center for Systematic Entomology"],"type":"journal"},"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-2015-019","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2015-019","pdf_url":"https://journal.r-project.org/archive/2015/RJ-2015-019/RJ-2015-019.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":false,"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":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2790993737.pdf","grobid_xml":"https://content.openalex.org/works/W2790993737.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W90334641","https://openalex.org/W128740895","https://openalex.org/W1549853756","https://openalex.org/W1591346633","https://openalex.org/W1975654088","https://openalex.org/W1989537995","https://openalex.org/W1998643818","https://openalex.org/W2034777510","https://openalex.org/W2037299692","https://openalex.org/W2040490772","https://openalex.org/W2041492013","https://openalex.org/W2045803758","https://openalex.org/W2089774007","https://openalex.org/W2104232463","https://openalex.org/W2148534890","https://openalex.org/W2335727437","https://openalex.org/W2549973236","https://openalex.org/W3122898048","https://openalex.org/W3124659230","https://openalex.org/W4399570610","https://openalex.org/W6661971546"],"related_works":["https://openalex.org/W3013650182","https://openalex.org/W4249605382","https://openalex.org/W2290902902","https://openalex.org/W4311995277","https://openalex.org/W1484024982","https://openalex.org/W614321472","https://openalex.org/W3096479702","https://openalex.org/W2167234030","https://openalex.org/W3019864246","https://openalex.org/W2772689174"],"abstract_inverted_index":{"The":[0,60,103,137],"beta":[1,21,43],"distribution":[2,14,22,67],"is":[3,63,110,126,139],"a":[4,9,28,65,98],"versatile":[5,142],"function":[6],"that":[7,32,68,93,144],"accommodates":[8],"broad":[10],"range":[11],"of":[12,100,123,131,178],"probability":[13,72,82],"shapes.":[15],"Beta":[16],"regression":[17,45,167],"based":[18,64],"on":[19],"the":[20,71,81,107,112,116,121,124,176,179],"can":[23,47,146],"be":[24,48],"used":[25],"to":[26,80],"model":[27,62,135,147,173],"response":[29],"variable":[30],"y":[31,51],"takes":[33,52],"values":[34,53],"in":[35,78,143],"open":[36],"unit":[37,56],"interval":[38,57],"(0,":[39,85],"1).":[40,86],"Zero/one":[41],"inflated":[42],"(ZOIB)":[44],"models":[46],"applied":[49],"when":[50],"from":[54],"closed":[55],"[0,":[58],"1].":[59],"ZOIB":[61,101],"piecewise":[66],"accounts":[69],"for":[70,97,172],"mass":[73],"at":[74,153],"0":[75,154],"and":[76,120,134,141,159,170],"1,":[77,156],"addition":[79],"density":[83],"within":[84],"This":[87],"paper":[88],"introduces":[89],"an":[90],"R":[91],"packagezoib":[92],"provides":[94],"Bayesian":[95],"inferences":[96],"class":[99],"models.":[102],"statistical":[104],"methodology":[105],"underlying":[106],"zoib":[108],"package":[109,117,125,138],"discussed,":[111],"functions":[113],"covered":[114],"by":[115],"are":[118],"outlined,":[119],"usage":[122],"illustrated":[127],"with":[128,149],"three":[129],"examples":[130],"different":[132],"data":[133,148,161],"types.":[136],"comprehensive":[140],"it":[145],"or":[150,155],"without":[151],"inflation":[152],"accommodate":[157],"clustered":[158],"correlated":[160],"via":[162,175],"latent":[163],"variables,":[164],"perform":[165],"penalized":[166],"as":[168],"needed,":[169],"allow":[171],"comparison":[174],"computation":[177],"DIC":[180],"criterion.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-10-10T00:00:00"}