{"id":"https://openalex.org/W2530989284","doi":"https://doi.org/10.32614/rj-2016-004","title":"rTableICC: An R Package for Random Generation of 22K and RC Contingency Tables","display_name":"rTableICC: An R Package for Random Generation of 22K and RC Contingency Tables","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2530989284","doi":"https://doi.org/10.32614/rj-2016-004","mag":"2530989284"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2016-004","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2016-004","pdf_url":"https://journal.r-project.org/archive/2016/RJ-2016-004/RJ-2016-004.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/2016/RJ-2016-004/RJ-2016-004.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060834675","display_name":"Haydar Demirhan","orcid":"https://orcid.org/0000-0002-8565-4710"},"institutions":[{"id":"https://openalex.org/I66514158","display_name":"Hacettepe University","ror":"https://ror.org/04kwvgz42","country_code":"TR","type":"education","lineage":["https://openalex.org/I66514158"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Haydar Demirhan","raw_affiliation_strings":["Hacettepe University Department of Statistics, Beytepe 06800 Ankara Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hacettepe University Department of Statistics, Beytepe 06800 Ankara Turkey","institution_ids":["https://openalex.org/I66514158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5060834675"],"corresponding_institution_ids":["https://openalex.org/I66514158"],"apc_list":null,"apc_paid":null,"fwci":1.278,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.78623719,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"8","issue":"1","first_page":"48","last_page":"48"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11428","display_name":"Advanced Mathematical Identities","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2602","display_name":"Algebra and Number Theory"},"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/T11428","display_name":"Advanced Mathematical Identities","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2602","display_name":"Algebra and Number Theory"},"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/T11269","display_name":"Algorithms and Data Compression","score":0.9919999837875366,"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/T11166","display_name":"Analytic Number Theory Research","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/2602","display_name":"Algebra and Number Theory"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.8452942371368408},{"id":"https://openalex.org/keywords/contingency-table","display_name":"Contingency table","score":0.7831885814666748},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6132376790046692},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.611538290977478},{"id":"https://openalex.org/keywords/intraclass-correlation","display_name":"Intraclass correlation","score":0.5893187522888184},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.5745810270309448},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.4753398895263672},{"id":"https://openalex.org/keywords/poisson-sampling","display_name":"Poisson sampling","score":0.47206002473831177},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.46297284960746765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4382178783416748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.38091009855270386},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.33976709842681885},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.23035794496536255},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.10302388668060303}],"concepts":[{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.8452942371368408},{"id":"https://openalex.org/C91998498","wikidata":"https://www.wikidata.org/wiki/Q1541178","display_name":"Contingency table","level":2,"score":0.7831885814666748},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6132376790046692},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.611538290977478},{"id":"https://openalex.org/C104709138","wikidata":"https://www.wikidata.org/wiki/Q1671540","display_name":"Intraclass correlation","level":3,"score":0.5893187522888184},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.5745810270309448},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.4753398895263672},{"id":"https://openalex.org/C82152865","wikidata":"https://www.wikidata.org/wiki/Q7208505","display_name":"Poisson sampling","level":5,"score":0.47206002473831177},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.46297284960746765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4382178783416748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38091009855270386},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33976709842681885},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.23035794496536255},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.10302388668060303},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C171606756","wikidata":"https://www.wikidata.org/wiki/Q506132","display_name":"Psychometrics","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.32614/rj-2016-004","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2016-004","pdf_url":"https://journal.r-project.org/archive/2016/RJ-2016-004/RJ-2016-004.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:alma.61RMIT_INST:11246840830001341","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402074","display_name":"RMIT Research Repository (RMIT University Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I82951845","host_organization_name":"RMIT University","host_organization_lineage":["https://openalex.org/I82951845"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:alma61RMIT.INST:11246840830001341","is_oa":false,"landing_page_url":"https://researchrepository.rmit.edu.au/esploro/outputs/journalArticle/rTableICC-An-R-Package-for-Random/9921859298501341","pdf_url":null,"source":{"id":"https://openalex.org/S4306402074","display_name":"RMIT Research Repository (RMIT University Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I82951845","host_organization_name":"RMIT University","host_organization_lineage":["https://openalex.org/I82951845"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:digitalcommons.unl.edu:r-journal-1502","is_oa":false,"landing_page_url":"https://digitalcommons.unl.edu/r-journal/536","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-2016-004","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2016-004","pdf_url":"https://journal.r-project.org/archive/2016/RJ-2016-004/RJ-2016-004.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/W2530989284.pdf","grobid_xml":"https://content.openalex.org/works/W2530989284.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W1863334437","https://openalex.org/W1868037044","https://openalex.org/W1982712545","https://openalex.org/W1986344907","https://openalex.org/W2002374079","https://openalex.org/W2042166402","https://openalex.org/W2069334301","https://openalex.org/W2074673068","https://openalex.org/W2091611882","https://openalex.org/W2092636824","https://openalex.org/W2801358783","https://openalex.org/W2801464996","https://openalex.org/W4206202898","https://openalex.org/W4243746943","https://openalex.org/W4302768905","https://openalex.org/W4399572270"],"related_works":["https://openalex.org/W2597604935","https://openalex.org/W1967798276","https://openalex.org/W2038123373","https://openalex.org/W2163520539","https://openalex.org/W128440123","https://openalex.org/W2115155766","https://openalex.org/W4240096849","https://openalex.org/W2554293138","https://openalex.org/W2973581637","https://openalex.org/W2022411062"],"abstract_inverted_index":{"In":[0,129],"this":[1],"paper,":[2],"we":[3],"describe":[4],"the":[5,72,132,136],"R":[6],"package":[7,48,67,115,137],"rTableICC":[8],"that":[9,83,98],"provides":[10,139],"an":[11],"interface":[12],"for":[13,71,120,143],"random":[14,50,121],"generation":[15,51,73,123],"of":[16,52,74,84],"22K":[17,85],"and":[18,42,82],"RC":[19,100],"contingency":[20,53,133],"tables":[21,54,77,86,101],"constructed":[22],"over":[23,55,78],"either":[24],"intraclass-correlated":[25],"or":[26,58,89,105,110],"uncorrelated":[27],"individuals.":[28],"Intraclass":[29],"correlations":[30,61],"arise":[31],"in":[32],"studies":[33],"where":[34],"sampling":[35,64,80,91,107],"units":[36],"include":[37,68],"more":[38],"than":[39],"one":[40],"individual":[41],"these":[43],"individuals":[44,56],"are":[45],"correlated.":[46],"The":[47,66,114],"implements":[49],"with":[57,109],"without":[59,111],"intraclass":[60,112],"under":[62,87,102],"various":[63],"plans.":[65,92],"two":[69,96],"functions":[70,97],"K":[75],"22":[76],"product-multinomial":[79],"schemes":[81],"Poisson":[88,106],"multinomial":[90,104],"It":[93],"also":[94,116,138],"contains":[95],"generate":[99],"product-multinomial,":[103],"plans":[108],"correlations.":[113],"includes":[117],"a":[118,125],"function":[119],"number":[122],"from":[124],"given":[126],"probability":[127],"distribution.":[128],"addition":[130],"to":[131],"table":[134],"format,":[135],"raw":[140],"data":[141],"required":[142],"further":[144],"estimation":[145],"purposes.":[146]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}