{"id":"https://openalex.org/W7093078813","doi":"https://doi.org/10.32614/rj-2025-020","title":"MSmix: An R Package for clustering partial rankings via mixtures of Mallows Models with Spearman distance","display_name":"MSmix: An R Package for clustering partial rankings via mixtures of Mallows Models with Spearman distance","publication_year":2025,"publication_date":"2025-09-25","ids":{"openalex":"https://openalex.org/W7093078813","doi":"https://doi.org/10.32614/rj-2025-020"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2025-020","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-020","pdf_url":"https://journal.r-project.org/articles/RJ-2025-020/RJ-2025-020.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-2025-020/RJ-2025-020.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Marta Crispino","orcid":null},"institutions":[{"id":"https://openalex.org/I103737770","display_name":"Bank of Italy","ror":"https://ror.org/03v2nbx43","country_code":"IT","type":"funder","lineage":["https://openalex.org/I103737770"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Marta Crispino","raw_affiliation_strings":["Department of Economics, Statistics and Research,Bank of Italy Rome,\nItaly"],"affiliations":[{"raw_affiliation_string":"Department of Economics, Statistics and Research,Bank of Italy Rome,\nItaly","institution_ids":["https://openalex.org/I103737770"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Cristina Mollica","orcid":null},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Cristina Mollica","raw_affiliation_strings":["Department of Statistical Sciences, Sapienza University of Rome"],"affiliations":[{"raw_affiliation_string":"Department of Statistical Sciences, Sapienza University of Rome","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lucia Modugno","orcid":null},"institutions":[{"id":"https://openalex.org/I103737770","display_name":"Bank of Italy","ror":"https://ror.org/03v2nbx43","country_code":"IT","type":"funder","lineage":["https://openalex.org/I103737770"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Lucia Modugno","raw_affiliation_strings":["Department of Economics, Statistics and Research, Bank of Italy"],"affiliations":[{"raw_affiliation_string":"Department of Economics, Statistics and Research, Bank of Italy","institution_ids":["https://openalex.org/I103737770"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I103737770"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77192025,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"2","first_page":"206","last_page":"231"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.32510000467300415,"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":0.32510000467300415,"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/T10991","display_name":"Game Theory and Voting Systems","score":0.07580000162124634,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.051500000059604645,"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":0.6467000246047974},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5849000215530396},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5561000108718872},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.5302000045776367},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4657000005245209},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4438000023365021},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.43619999289512634},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.38589999079704285},{"id":"https://openalex.org/keywords/software-package","display_name":"Software package","score":0.3662000000476837}],"concepts":[{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.6467000246047974},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5849000215530396},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5561000108718872},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.5302000045776367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4941999912261963},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4717000126838684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4672999978065491},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4438000023365021},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C3020440742","wikidata":"https://www.wikidata.org/wiki/Q1176855","display_name":"Software package","level":3,"score":0.3662000000476837},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C176222170","wikidata":"https://www.wikidata.org/wiki/Q5157340","display_name":"Computational statistics","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2939999997615814},{"id":"https://openalex.org/C26359558","wikidata":"https://www.wikidata.org/wiki/Q7269462","display_name":"Quasi-maximum likelihood","level":4,"score":0.29260000586509705},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.29100000858306885},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2896000146865845},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27219998836517334},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C203223496","wikidata":"https://www.wikidata.org/wiki/Q4681344","display_name":"Additive model","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32614/rj-2025-020","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-020","pdf_url":"https://journal.r-project.org/articles/RJ-2025-020/RJ-2025-020.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-2025-020","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-020","pdf_url":"https://journal.r-project.org/articles/RJ-2025-020/RJ-2025-020.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/F4320322149","display_name":"Banca d'Italia","ror":"https://ror.org/03v2nbx43"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7093078813.pdf","grobid_xml":"https://content.openalex.org/works/W7093078813.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"MSmix":[0],"is":[1,27,55,124],"a":[2,46],"recently":[3],"developed":[4],"R":[5],"package":[6,26,74,115],"implementing":[7],"maximum":[8],"likelihood":[9],"estimation":[10,33,53],"of":[11,14,42,49,81,107,113],"finite":[12],"mixtures":[13],"Mallows":[15],"models":[16],"with":[17,35,121],"Spearman":[18],"distance":[19],"for":[20,78,95,101],"full":[21],"and":[22,45,69,89,116,131],"partial":[23,43],"rankings.":[24],"The":[25,51,73,111],"designed":[28],"to":[29,38,64,128],"implement":[30],"computationally":[31],"tractable":[32],"routines,":[34],"the":[36,66,70,82,105,108,114],"ability":[37],"handle":[39],"arbitrary":[40],"forms":[41],"rankings":[44],"large":[47],"number":[48],"items.":[50],"frequentist":[52],"task":[54],"accomplished":[56],"via":[57,85,126],"Expectation-Maximization":[58],"algorithms,":[59],"integrating":[60],"data":[61],"augmentation":[62],"strategies":[63],"recover":[65],"unobserved":[67],"heterogeneity":[68],"missing":[71],"ranks.":[72],"also":[75],"provides":[76],"functionalities":[77],"uncertainty":[79],"quantification":[80],"parameter":[83],"estimates,":[84],"diverse":[86],"bootstrap":[87],"methods":[88,94],"asymptotic":[90],"confidence":[91],"intervals.":[92],"Generic":[93],"S3":[96],"class":[97],"objects":[98],"are":[99],"constructed":[100],"more":[102],"effectively":[103],"managing":[104],"output":[106],"main":[109],"routines.":[110],"usefulness":[112],"its":[117],"computational":[118],"performance":[119],"compared":[120],"competing":[122],"software":[123],"illustrated":[125],"applications":[127],"both":[129],"simulated":[130],"original":[132],"real":[133],"ranking":[134],"datasets.":[135]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-22T00:00:00"}
