{"id":"https://openalex.org/W7093103579","doi":"https://doi.org/10.32614/rj-2025-008","title":"CDsampling: An R Package for Constrained D-Optimal Sampling in Paid Research Studies","display_name":"CDsampling: An R Package for Constrained D-Optimal Sampling in Paid Research Studies","publication_year":2025,"publication_date":"2025-08-18","ids":{"openalex":"https://openalex.org/W7093103579","doi":"https://doi.org/10.32614/rj-2025-008"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2025-008","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-008","pdf_url":"https://journal.r-project.org/articles/RJ-2025-008/RJ-2025-008.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-2025-008/RJ-2025-008.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yifei Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yifei Huang","raw_affiliation_strings":["Department of Mathematics, Statistics, and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Statistics, and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Liping Tong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liping Tong","raw_affiliation_strings":["Advocate Aurora Health"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advocate Aurora Health","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Jie Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["Department of Mathematics, Statistics, and Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Statistics, and Computer Science","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.56305441,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"1","first_page":"160","last_page":"179"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.5759999752044678,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.5759999752044678,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.12070000171661377,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.10239999741315842,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6075999736785889},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5907999873161316},{"id":"https://openalex.org/keywords/fisher-information","display_name":"Fisher information","score":0.5612000226974487},{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.5320000052452087},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.48429998755455017},{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.46860000491142273},{"id":"https://openalex.org/keywords/sampling-design","display_name":"Sampling design","score":0.39719998836517334},{"id":"https://openalex.org/keywords/stratified-sampling","display_name":"Stratified sampling","score":0.36809998750686646}],"concepts":[{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6075999736785889},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5907999873161316},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.5612000226974487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.558899998664856},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.5320000052452087},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4959999918937683},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.48429998755455017},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.46860000491142273},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3993000090122223},{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.39719998836517334},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.36970001459121704},{"id":"https://openalex.org/C49898467","wikidata":"https://www.wikidata.org/wiki/Q1517706","display_name":"Stratified sampling","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36320000886917114},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2989000082015991},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2736999988555908},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2687000036239624},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32614/rj-2025-008","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-008","pdf_url":"https://journal.r-project.org/articles/RJ-2025-008/RJ-2025-008.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-2025-008","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-008","pdf_url":"https://journal.r-project.org/articles/RJ-2025-008/RJ-2025-008.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":[{"score":0.4219608008861542,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7093103579.pdf","grobid_xml":"https://content.openalex.org/works/W7093103579.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,26,32,43,51,82,103,113,117,121,130],"context":[2],"of":[3,45,120],"paid":[4],"research":[5],"studies":[6],"and":[7,58,95,105,135],"clinical":[8],"trials,":[9],"budget":[10],"considerations":[11],"often":[12],"require":[13],"patient":[14],"sampling":[15,75,79,110],"from":[16],"available":[17],"populations,":[18],"which":[19,30],"comes":[20],"with":[21,63,99,108],"inherent":[22],"constraints.":[23,66],"We":[24],"introduce":[25],"R":[27],"package":[28,49,104,115],"CDsampling,":[29],"is":[31,85],"first":[33],"to":[34,37,53,71],"our":[35],"knowledge":[36],"integrate":[38],"optimal":[39],"design":[40],"theories":[41],"within":[42],"framework":[44],"constrained":[46,73],"sampling.":[47],"This":[48],"offers":[50],"possibility":[52],"find":[54,72],"both":[55],"D-optimal":[56],"approximate":[57],"exact":[59],"allocations":[60],"for":[61,125,141],"samplings":[62],"or":[64],"without":[65],"Additionally,":[67],"it":[68],"provides":[69],"functions":[70,140],"uniform":[74],"as":[76],"a":[77,96],"robust":[78],"strategy":[80],"when":[81],"model":[83],"information":[84,123],"limited.":[86],"To":[87],"demonstrate":[88],"its":[89,142],"efficacy,":[90],"we":[91],"provide":[92],"simulated":[93],"examples":[94],"real-data":[97],"example":[98],"datasets":[100],"embedded":[101],"in":[102],"compare":[106],"them":[107],"classical":[109],"methods.":[111],"Furthermore,":[112],"CDsampling":[114],"revisits":[116],"theoretical":[118],"results":[119],"Fisher":[122],"matrix":[124],"generalized":[126],"linear":[127,132],"models":[128],"(including":[129],"regular":[131],"regression":[133],"model)":[134],"multinomial":[136],"logistic":[137],"models,":[138],"offering":[139],"computation.":[143]},"counts_by_year":[],"updated_date":"2026-03-13T14:20:09.374765","created_date":"2025-10-22T00:00:00"}