{"id":"https://openalex.org/W4416977291","doi":"https://doi.org/10.32614/rj-2025-029","title":"lqmix: an R Package for Longitudinal Data Analysis via Linear Quantile Mixtures","display_name":"lqmix: an R Package for Longitudinal Data Analysis via Linear Quantile Mixtures","publication_year":2025,"publication_date":"2025-11-25","ids":{"openalex":"https://openalex.org/W4416977291","doi":"https://doi.org/10.32614/rj-2025-029"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2025-029","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-029","pdf_url":"https://journal.r-project.org/articles/RJ-2025-029/RJ-2025-029.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-2025-029/RJ-2025-029.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080632239","display_name":"Marco Alf\u00f2","orcid":"https://orcid.org/0000-0001-7651-6052"},"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":"Marco Alf\u00f3","raw_affiliation_strings":["Department of Statistics, Sapienza, University of Rome"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Sapienza, University of Rome","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044999410","display_name":"Maria Francesca Marino","orcid":"https://orcid.org/0000-0002-5841-7613"},"institutions":[{"id":"https://openalex.org/I4210106463","display_name":"System Science Applications (United States)","ror":"https://ror.org/01gg29e32","country_code":"US","type":"company","lineage":["https://openalex.org/I4210106463"]},{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT","US"],"is_corresponding":false,"raw_author_name":"Maria Francesca Marino","raw_affiliation_strings":["Department of Statistics, Computer Science, Applications, University\nof Florence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Computer Science, Applications, University\nof Florence","institution_ids":["https://openalex.org/I45084792","https://openalex.org/I4210106463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043196497","display_name":"Maria Giovanna Ranalli","orcid":"https://orcid.org/0000-0001-7005-8572"},"institutions":[{"id":"https://openalex.org/I27483092","display_name":"University of Perugia","ror":"https://ror.org/00x27da85","country_code":"IT","type":"education","lineage":["https://openalex.org/I27483092"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Maria Giovanna Ranalli","raw_affiliation_strings":["Department of Political Science, University of Perugia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Political Science, University of Perugia","institution_ids":["https://openalex.org/I27483092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083878887","display_name":"Nicola Salvati","orcid":"https://orcid.org/0000-0002-4160-9387"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicola Salvati","raw_affiliation_strings":["Department of Economics, University of Pisa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Economics, University of Pisa","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"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":0.3149689,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"3","first_page":"188","last_page":"211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.6880000233650208,"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":0.6880000233650208,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.07859999686479568,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.0737999975681305,"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/context","display_name":"Context (archaeology)","score":0.6146000027656555},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.5916000008583069},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.5619999766349792},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5192999839782715},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.5171999931335449},{"id":"https://openalex.org/keywords/log-linear-model","display_name":"Log-linear model","score":0.48350000381469727},{"id":"https://openalex.org/keywords/longitudinal-data","display_name":"Longitudinal data","score":0.42179998755455017},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4147999882698059},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.4043000042438507}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6326000094413757},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6146000027656555},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.5916000008583069},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.5619999766349792},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5192999839782715},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.5171999931335449},{"id":"https://openalex.org/C70519679","wikidata":"https://www.wikidata.org/wiki/Q6666755","display_name":"Log-linear model","level":3,"score":0.48350000381469727},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4357999861240387},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.39149999618530273},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3596000075340271},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.30480000376701355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.30070000886917114},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C153720581","wikidata":"https://www.wikidata.org/wiki/Q5532490","display_name":"Generalized linear mixed model","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.32614/rj-2025-029","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-029","pdf_url":"https://journal.r-project.org/articles/RJ-2025-029/RJ-2025-029.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:iris.uniroma1.it:11573/1762530","is_oa":true,"landing_page_url":"https://hdl.handle.net/11573/1762530","pdf_url":"https://iris.uniroma1.it/bitstream/11573/1762530/1/Alf%c3%b2_lqmix%3a-%20RPackage_2025.pdf","source":{"id":"https://openalex.org/S4377196107","display_name":"IRIS Research product catalog (Sapienza University of Rome)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.32614/rj-2025-029","is_oa":true,"landing_page_url":"https://doi.org/10.32614/rj-2025-029","pdf_url":"https://journal.r-project.org/articles/RJ-2025-029/RJ-2025-029.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":[{"id":"https://openalex.org/G4956079712","display_name":null,"funder_award_id":"2018-2022","funder_id":"https://openalex.org/F4320321873","funder_display_name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca"},{"id":"https://openalex.org/G7091469379","display_name":null,"funder_award_id":"2023-2027","funder_id":"https://openalex.org/F4320321873","funder_display_name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca"},{"id":"https://openalex.org/G8140762014","display_name":null,"funder_award_id":"2018-2022","funder_id":"https://openalex.org/F4320326523","funder_display_name":"Universit\u00e0 degli Studi di Perugia"}],"funders":[{"id":"https://openalex.org/F4320321873","display_name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","ror":"https://ror.org/0166hxq48"},{"id":"https://openalex.org/F4320326523","display_name":"Universit\u00e0 degli Studi di Perugia","ror":"https://ror.org/00x27da85"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416977291.pdf","grobid_xml":"https://content.openalex.org/works/W4416977291.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,123],"analysis":[1,124],"of":[2,22,29,36,54,77,88,125],"longitudinal":[3,83],"data":[4],"gives":[5],"the":[6,27,34,45,52,86,113,135],"chance":[7],"to":[8,51,131],"observe":[9],"how":[10],"units'":[11],"behavior":[12],"changes":[13],"over":[14],"time,":[15],"but":[16],"it":[17],"also":[18],"poses":[19],"a":[20,67,75,103,120,126],"series":[21],"issues.":[23],"These":[24],"have":[25],"been":[26],"focus":[28],"an":[30,108],"extensive":[31],"literature":[32],"in":[33,44,73,85,102],"context":[35,53],"linear":[37,40,55,78],"and":[38],"generalized":[39],"regression,":[41],"moving":[42],"also,":[43],"last":[46],"ten":[47],"years":[48],"or":[49],"so,":[50],"quantile":[56,79],"regression":[57,80],"for":[58,82],"continuous":[59],"responses.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64],"present":[65],"`lqmix`,":[66],"novel":[68],"`R`":[69],"package":[70,136],"that":[71],"assists":[72],"estimating":[74],"class":[76],"models":[81],"data,":[84],"presence":[87],"time-constant":[89],"and/or":[90],"time-varying,":[91],"unit-specific,":[92],"random":[93],"coefficients,":[94],"with":[95],"unspecified":[96],"distribution.":[97],"Model":[98],"parameters":[99],"are":[100,117],"estimated":[101],"maximum":[104],"likelihood":[105],"framework":[106],"via":[107,119],"extended":[109],"EM":[110],"algorithm,":[111],"while":[112],"corresponding":[114],"standard":[115],"errors":[116],"derived":[118],"block-bootstrap":[121],"procedure.":[122],"benchmark":[127],"dataset":[128],"is":[129],"used":[130],"give":[132],"details":[133],"on":[134],"functions.":[137]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-04T00:00:00"}