{ "_id": "6a12c5ecacfb0bcc41d1fb62", "Package": "CopulaREMADA", "Version": "1.7.5", "Date": "2025-05-27", "Title": "Copula Mixed Models for Multivariate Meta-Analysis of Diagnostic\nTest Accuracy Studies", "Authors@R": "person(given = c(\"Aristidis\", \"K.\"),\nfamily = \"Nikoloulopoulos\",\nrole = c(\"aut\", \"cre\"),\nemail = \"A.Nikoloulopoulos@uea.ac.uk\",\ncomment = c(ORCID = \"0000-0003-0853-0084\"))", "Author": "Aristidis K. Nikoloulopoulos [aut, cre] (ORCID:\n)", "Maintainer": "Aristidis K. Nikoloulopoulos ", "Description": "The bivariate copula mixed model for meta-analysis of\ndiagnostic test accuracy studies in Nikoloulopoulos (2015)\n and Nikoloulopoulos (2018)\n. The vine copula mixed model\nfor meta-analysis of diagnostic test accuracy studies\naccounting for disease prevalence in Nikoloulopoulos (2017)\n and also accounting for\nnon-evaluable subjects in Nikoloulopoulos (2020)\n. The hybrid vine copula mixed model\nfor meta-analysis of diagnostic test accuracy case-control and\ncohort studies in Nikoloulopoulos (2018)\n. The D-vine copula mixed model\nfor meta-analysis and comparison of two diagnostic tests in\nNikoloulopoulos (2019) . The\nmultinomial quadrivariate D-vine copula mixed model for\nmeta-analysis of diagnostic tests with non-evaluable subjects\nin Nikoloulopoulos (2020) . The\none-factor copula mixed model for joint meta-analysis of\nmultiple diagnostic tests in Nikoloulopoulos (2022)\n. The multinomial six-variate\n1-truncated D-vine copula mixed model for meta-analysis of two\ndiagnostic tests accounting for within and between studies\ndependence in Nikoloulopoulos (2024)\n. The 1-truncated D-vine copula\nmixed models for meta-analysis of diagnostic accuracy studies\nwithout a gold standard (Nikoloulopoulos, 2025)\n.", "License": "GPL (>= 2)", "Packaged": { "Date": "2026-05-24 09:28:11 UTC", "User": "root" }, "NeedsCompilation": "no", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-05-27 16:20:05 UTC", "RemoteUrl": "https://github.com/cran/CopulaREMADA", "RemoteRef": "HEAD", "RemoteSha": "cb8bbb0e1f744b0e7e93722aab7ea53a9af071bc", "MD5sum": "0c16eca85b1c0d270d7c8cd33b99d865", "_user": "cran", "_type": "src", "_file": "CopulaREMADA_1.7.5.tar.gz", "_fileid": "857e207784374c697594cc5281c4768f62e73fc7dd401bd0e3ec054b86528082", "_filesize": 526439, "_sha256": "857e207784374c697594cc5281c4768f62e73fc7dd401bd0e3ec054b86528082", "_created": "2026-05-24T09:28:11.000Z", "_published": "2026-05-24T09:33:32.913Z", "_distro": "noble", "_jobs": [ { "job": 77587123533, "time": 194, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7184053386" }, { "job": 77587123538, "time": 186, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7184052597" }, { "job": 77586860517, "time": 297, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7184034093" }, { "job": 77587123516, "time": 126, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7184047089" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/26357505427", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/CopulaREMADA", "_commit": { "id": "cb8bbb0e1f744b0e7e93722aab7ea53a9af071bc", "author": "Aristidis K. Nikoloulopoulos ", "committer": "cran-robot ", "message": "version 1.7.5\n", "time": 1748362805 }, "_maintainer": { "name": "Aristidis K. Nikoloulopoulos", "email": "a.nikoloulopoulos@uea.ac.uk", "orcid": "0000-0003-0853-0084" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "statmod", "role": "Depends" }, { "package": "matlab", "role": "Depends" }, { "package": "tensor", "role": "Depends" }, { "package": "mc2d", "role": "Depends" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2025-22", "n": 1 } ], "_tags": [ { "name": "1.7.5", "date": "2025-05-27" } ], "_stars": 2, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 706, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/CopulaREMADA" }, "_mentions": 1, "_searchresults": 11, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/CopulaREMADA.html", "manual.pdf" ], "_realowner": "cran", "_cranurl": false, "_releases": [ { "version": "0.5-1", "date": "2015-02-22" }, { "version": "0.9", "date": "2015-06-12" }, { "version": "1.0", "date": "2016-09-13" }, { "version": "1.1", "date": "2018-06-22" }, { "version": "1.2", "date": "2019-02-08" }, { "version": "1.3", "date": "2019-03-25" }, { "version": "1.5", "date": "2022-02-09" }, { "version": "1.5.1", "date": "2022-08-07" }, { "version": "1.6.2", "date": "2024-04-08" }, { "version": "1.7.3", "date": "2024-10-17" }, { "version": "1.7.5", "date": "2025-05-27" } ], "_exports": [ "CopulaREMADA.beta", "CopulaREMADA.norm", "countermonotonicCopulaREMADA.beta", "countermonotonicCopulaREMADA.norm", "countermonotonicity.vuong", "cvinesim", "dbvn", "dcln", "dcln180", "dcln270", "dcln90", "dfrk", "dtrvinesim", "dvine6dsim", "dvinesim", "FactorCopulaREMADA.beta", "FactorCopulaREMADA.norm", "hybridCopulaREMADA.beta", "hybridCopulaREMADA.norm", "imperfect.fivevariateVineCopulaREMADA.beta.comprehensive", "imperfect.fivevariateVineCopulaREMADA.norm.comprehensive", "imperfect.quadrivariateVineCopulaREMADA.beta.comprehensive", "imperfect.quadrivariateVineCopulaREMADA.norm.comprehensive", "imperfect.trivariateVineCopulaREMADA.beta", "imperfect.trivariateVineCopulaREMADA.beta.comprehensive", "imperfect.trivariateVineCopulaREMADA.norm", "imperfect.trivariateVineCopulaREMADA.norm.comprehensive", "imperfectCopulaREMADA.beta", "imperfectCopulaREMADA.norm", "imperfectREMADA.beta", "imperfectREMADA.norm", "multinom6dVineCopulaREMADA.beta", "multinom6dVineCopulaREMADA.norm", "multinomVineCopulaREMADA.beta", "multinomVineCopulaREMADA.norm", "pcondbvn", "pcondcln", "pcondcln180", "pcondcln270", "pcondcln90", "pcondfrk", "qcondbvn", "qcondcln", "qcondcln180", "qcondcln270", "qcondcln90", "qcondfrk", "quadVineCopulaREMADA.beta", "quadVineCopulaREMADA.norm", "quadVineCopulaREMADA.norm.beta", "rcln", "rcln180", "rcln270", "rcln90", "rCopulaREMADA.beta", "rCopulaREMADA.norm", "rFactorCopulaREMADA.beta", "rFactorCopulaREMADA.norm", "rfrk", "rimperfect.trivariateVineCopulaREMADA.beta", "rimperfect.trivariateVineCopulaREMADA.norm", "rmultinom6dVineCopulaREMADA.beta", "rmultinom6dVineCopulaREMADA.norm", "rmultinomVineCopulaREMADA.beta", "rmultinomVineCopulaREMADA.norm", "rVineCopulaREMADA.beta", "rVineCopulaREMADA.norm", "SROC", "SROC.beta", "SROC.norm", "tau2par.bvn", "tau2par.cln", "tau2par.cln180", "tau2par.cln270", "tau2par.cln90", "tau2par.frk", "tmultinomVineCopulaREMADA.beta", "tmultinomVineCopulaREMADA.norm", "tvine.vuong.beta", "tvine.vuong.norm", "tvine2.vuong.beta", "tvine2.vuong.norm", "tVineCopulaREMADA.beta", "tVineCopulaREMADA.norm", "vine.vuong.beta", "vine.vuong.norm", "VineCopulaREMADA.beta", "VineCopulaREMADA.norm", "vuong.beta", "vuong.norm" ], "_datasets": [ { "name": "arthritis", "title": "The rheumatoid arthritis data", "object": "arthritis", "file": "arthritis.rda", "class": [ "data.frame" ], "fields": [ "TP1", "FP1", "FN1", "TN1", "TP2", "FP2", "FN2", "TN2" ], "rows": 22, "table": true, "tojson": true }, { "name": "betaDG", "title": "The beta-D-Glucan-data", "object": "betaDG", "file": "betaDG.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN" ], "rows": 8, "table": true, "tojson": true }, { "name": "coronary", "title": "The coronary CT angiography data", "object": "coronary", "file": "coronary.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN", "NEP", "NEN" ], "rows": 26, "table": true, "tojson": true }, { "name": "CT", "title": "The computing tomography data", "object": "CT", "file": "CT.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN" ], "rows": 17, "table": true, "tojson": true }, { "name": "Down", "title": "The down syndrome data", "object": "Down", "file": "Down.rda", "class": [ "data.frame" ], "fields": [ "down_n_00", "down_n_01", "down_n_10", "down_n_11", "nodown_n_00", "nodown_n_01", "nodown_n_10", "nodown_n_11" ], "rows": 11, "table": true, "tojson": true }, { "name": "LAG", "title": "The lymphangiography data", "object": "LAG", "file": "LAG.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN" ], "rows": 17, "table": true, "tojson": true }, { "name": "mgrid", "title": "A list containing four-dimensional arrays", "object": "mgrid6d", "file": "mgrid6d.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "mgrid15", "title": "A list containing four-dimensional arrays", "object": "mgrid15", "file": "mgrid15.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "mgrid30", "title": "A list containing four-dimensional arrays", "object": "mgrid30", "file": "mgrid30.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "mgrid50", "title": "A list containing four-dimensional arrays", "object": "mgrid50", "file": "mgrid50.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "mgrid5d15", "title": "A list containing five-dimensional arrays", "object": "mgrid5d15", "file": "mgrid5d15.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "mgrid5d30", "title": "A list containing five-dimensional arrays", "object": "mgrid5d30", "file": "mgrid5d30.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "MK2016", "title": "The coronary CT angiography data in Menke and Kowalski (2016).", "object": "MK2016", "file": "MK2016.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN", "NEP", "NEN" ], "rows": 30, "table": true, "tojson": true }, { "name": "MRI", "title": "The magnetic resonance imaging data", "object": "MRI", "file": "MRI.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN" ], "rows": 10, "table": true, "tojson": true }, { "name": "OGT", "title": "The orale glucose tolerance data", "object": "OGT", "file": "OGT.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN" ], "rows": 10, "table": true, "tojson": true }, { "name": "Pap", "title": "The Pap test data", "object": "Pap", "file": "Pap.rda", "class": [ "data.frame" ], "fields": [ "y11", "y10", "y01", "y00" ], "rows": 59, "table": true, "tojson": true }, { "name": "telomerase", "title": "The telomerase data", "object": "telomerase", "file": "telomerase.rda", "class": [ "data.frame" ], "fields": [ "TP", "FP", "FN", "TN" ], "rows": 10, "table": true, "tojson": true } ], "_help": [ { "page": "CopulaREMADA-package", "title": "Copula Mixed Models for Multivariate Meta-Analysis of Diagnostic Test Accuracy Studies", "topics": [ "CopulaREMADA-package" ] }, { "page": "arthritis", "title": "The rheumatoid arthritis data", "topics": [ "arthritis" ] }, { "page": "betaDG", "title": "The beta-D-Glucan-data", "topics": [ "betaDG" ] }, { "page": "CopulaREMADA", "title": "Maximum likelhood estimation for copula mixed models for diagnostic test accurracy studies", "topics": [ "CopulaREMADA", "CopulaREMADA.beta", "CopulaREMADA.norm", "countermonotonicCopulaREMADA", "countermonotonicCopulaREMADA.beta", "countermonotonicCopulaREMADA.norm" ] }, { "page": "coronary", "title": "The coronary CT angiography data", "topics": [ "coronary" ] }, { "page": "CT", "title": "The computing tomography data", "topics": [ "CT" ] }, { "page": "cvinesim", "title": "Simulation from a trivariate C-vine copula", "topics": [ "cvinesim" ] }, { "page": "dcop", "title": "Bivariate copula densities", "topics": [ "dbvn", "dcln", "dcln180", "dcln270", "dcln90", "dcop", "dfrk" ] }, { "page": "Down", "title": "The down syndrome data", "topics": [ "Down" ] }, { "page": "dvine6dsim", "title": "Simulation from a six-variate 1-truncated D-vine copula", "topics": [ "dvine6dsim" ] }, { "page": "dvinesim", "title": "Simulation from a (truncated) quadrivariate D-vine copula", "topics": [ "dtrvinesim", "dvinesim" ] }, { "page": "FactorCopulaREMADA", "title": "Maximum likelihood estimation of 1-factor copula mixed models for joint meta-analysis of T diagnostic tests", "topics": [ "FactorCopulaREMADA", "FactorCopulaREMADA.beta", "FactorCopulaREMADA.norm" ] }, { "page": "hybridCopulaREMADA", "title": "Maximum likelhood estimation for hybrid copula mixed models for combining case-control and cohort studies in meta-analysis of diagnostic tests", "topics": [ "hybridCopulaREMADA", "hybridCopulaREMADA.beta", "hybridCopulaREMADA.norm" ] }, { "page": "imperfect.fivevariateVineCopulaREMADA", "title": "Maximum likelihood estimation of 5-variate 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard", "topics": [ "imperfect.fivevariateVineCopulaREMADA.beta.comprehensive", "imperfect.fivevariateVineCopulaREMADA.norm.comprehensive" ] }, { "page": "imperfect.quadriateVineCopulaREMADA", "title": "Maximum likelihood estimation of quadrivariate 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard", "topics": [ "imperfect.quadrivariateVineCopulaREMADA.beta.comprehensive", "imperfect.quadrivariateVineCopulaREMADA.norm.comprehensive" ] }, { "page": "imperfect.trivariateVineCopulaREMADA", "title": "Maximum likelihood estimation of trivariate 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard", "topics": [ "imperfect.trivariateVineCopulaREMADA", "imperfect.trivariateVineCopulaREMADA.beta", "imperfect.trivariateVineCopulaREMADA.beta.comprehensive", "imperfect.trivariateVineCopulaREMADA.norm", "imperfect.trivariateVineCopulaREMADA.norm.comprehensive" ] }, { "page": "imperfect.CopulaREMADA", "title": "Maximum likelihood estimation of bivariate copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard", "topics": [ "imperfectCopulaREMADA", "imperfectCopulaREMADA.beta", "imperfectCopulaREMADA.norm" ] }, { "page": "imperfect.REMADA", "title": "Maximum likelihood estimation of univariate mixed models for meta-analysis of diagnostic accuracy studies without a gold standard", "topics": [ "imperfectREMADA", "imperfectREMADA.beta", "imperfectREMADA.norm" ] }, { "page": "LAG", "title": "The lymphangiography data", "topics": [ "LAG" ] }, { "page": "mgrid", "title": "A list containing four-dimensional arrays", "topics": [ "mgrid", "mgrid15", "mgrid30", "mgrid50" ] }, { "page": "mgrid5d", "title": "A list containing five-dimensional arrays", "topics": [ "mgrid5d", "mgrid5d15", "mgrid5d30" ] }, { "page": "mgrid6d", "title": "A list containing six-dimensional arrays", "topics": [ "mgrid6d" ] }, { "page": "MK2016", "title": "The coronary CT angiography data in Menke and Kowalski (2016).", "topics": [ "MK2016" ] }, { "page": "MRI", "title": "The magnetic resonance imaging data", "topics": [ "MRI" ] }, { "page": "mutinom6dVineCopulaREMADA", "title": "Maximum likelhood estimation for multinomial six-variate 1-truncated D-vine copula mixed models for meta-analysis of two diagnostic tests accounting for within and between studies dependence", "topics": [ "multinom6dVineCopulaREMADA", "multinom6dVineCopulaREMADA.beta", "multinom6dVineCopulaREMADA.norm" ] }, { "page": "mutinomVineCopulaREMADA", "title": "Maximum likelhood estimation for multinomial quadrivariate (truncated) D-vine copula mixed models for diagnostic test accurracy studies accounting for non-evaluable outcomes", "topics": [ "multinomVineCopulaREMADA", "multinomVineCopulaREMADA.beta", "multinomVineCopulaREMADA.norm", "tmultinomVineCopulaREMADA", "tmultinomVineCopulaREMADA.beta", "tmultinomVineCopulaREMADA.norm" ] }, { "page": "OGT", "title": "The orale glucose tolerance data", "topics": [ "OGT" ] }, { "page": "Pap", "title": "The Pap test data", "topics": [ "Pap" ] }, { "page": "pcondcop", "title": "Bivariate copula conditional distribution functions", "topics": [ "pcondbvn", "pcondcln", "pcondcln180", "pcondcln270", "pcondcln90", "pcondcop", "pcondfrk" ] }, { "page": "qcondcop", "title": "Bivariate copula conditional quantile functions", "topics": [ "qcondbvn", "qcondcln", "qcondcln180", "qcondcln270", "qcondcln90", "qcondcop", "qcondfrk" ] }, { "page": "quadVineCopulaREMADA", "title": "Maximum likelihood estimation of quadrivariate D-vine copula mixed models for joint meta-analysis and comparison of two diagnostic tests", "topics": [ "quadVineCopulaREMADA", "quadVineCopulaREMADA.beta", "quadVineCopulaREMADA.norm", "quadVineCopulaREMADA.norm.beta" ] }, { "page": "rcop", "title": "Simulation from parametric bivariate copula families", "topics": [ "rbvn", "rcln", "rcln180", "rcln270", "rcln90", "rcop", "rfrk" ] }, { "page": "rCopulaREMADA", "title": "Simulation from copula mixed models for diagnostic test accuaracy studies", "topics": [ "rCopulaREMADA", "rCopulaREMADA.beta", "rCopulaREMADA.norm" ] }, { "page": "rFactorCopulaREMADA", "title": "Simulation from 1-factor copula mixed models for joint meta-analysis of T diagnostic tests", "topics": [ "rFactorCopulaREMADA", "rFactorCopulaREMADA.beta", "rFactorCopulaREMADA.norm" ] }, { "page": "rimperfect.trivariateVineCopulaREMADA", "title": "Simulation from trivariate 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard", "topics": [ "rimperfect.trivariateVineCopulaREMADA", "rimperfect.trivariateVineCopulaREMADA.beta", "rimperfect.trivariateVineCopulaREMADA.norm" ] }, { "page": "rmultinom6dVineCopulaREMADA", "title": "Simulation from multinomial six-variate 1-truncated D-vine copula mixed models for meta-analysis of two diagnostic tests accounting for within and between studies dependence", "topics": [ "rmultinom6dVineCopulaREMADA", "rmultinom6dVineCopulaREMADA.beta", "rmultinom6dVineCopulaREMADA.norm" ] }, { "page": "rmultinomVineCopulaREMADA", "title": "Simulation from multinomial quadrivariate (truncated) D-vine copula mixed models for diagnostic test accurracy studies accounting for non-evaluable outcomes", "topics": [ "rmultinomVineCopulaREMADA", "rmultinomVineCopulaREMADA.beta", "rmultinomVineCopulaREMADA.norm" ] }, { "page": "rVineCopulaREMADA", "title": "Simulation from trivariate vine copula mixed models for diagnostic test accuaracy studies accounting for disease prevalence and non-evaluable results", "topics": [ "rVineCopulaREMADA", "rVineCopulaREMADA.beta", "rVineCopulaREMADA.norm" ] }, { "page": "SROC", "title": "Summary receiver operating characteristic curves for copula mixed effect models for bivariate meta-analysis of diagnostic test accuracy studies", "topics": [ "SROC", "SROC.beta", "SROC.norm" ] }, { "page": "tau2par", "title": "Mapping of Kendall's tau and copula parameter", "topics": [ "tau2par", "tau2par.bvn", "tau2par.cln", "tau2par.cln180", "tau2par.cln270", "tau2par.cln90", "tau2par.frk" ] }, { "page": "telomerase", "title": "The telomerase data", "topics": [ "telomerase" ] }, { "page": "vineVuong", "title": "Vuong's test for the comparison of non-nested vine copula mixed models for diagnostic test accuaracy studies", "topics": [ "tvine.vuong.beta", "tvine.vuong.norm", "tvine2.vuong.beta", "tvine2.vuong.norm", "vine.vuong", "vine.vuong.beta", "vine.vuong.norm" ] }, { "page": "VineCopulaREMADA", "title": "Maximum likelhood estimation for (truncated) vine copula mixed models for diagnostic test accurracy studies accounting for disease prevalence and non-evaluable outcomes", "topics": [ "tVineCopulaREMADA", "tVineCopulaREMADA.beta", "tVineCopulaREMADA.norm", "VineCopulaREMADA", "VineCopulaREMADA.beta", "VineCopulaREMADA.norm" ] }, { "page": "Vuong", "title": "Vuong's test for the comparison of non-nested copula mixed models for diagnostic test accuaracy studies", "topics": [ "countermonotonicity.vuong", "vuong", "vuong.beta", "vuong.norm" ] } ], "_rundeps": [ "abind", "backports", "boot", "broom", "car", "carData", "cli", "colorspace", "corrplot", "cowplot", "cpp11", "Deriv", "doBy", "dplyr", "farver", "forecast", "Formula", "fracdiff", "generics", "ggplot2", "ggpubr", "ggrepel", "ggsci", "ggsignif", "glue", "gridExtra", "gtable", "isoband", "labeling", "lattice", "lifecycle", "lme4", "lmtest", "magrittr", "MASS", "matlab", "Matrix", "MatrixModels", "mc2d", "mgcv", "microbenchmark", "minqa", "modelr", "mvtnorm", "nlme", "nloptr", "nnet", "numDeriv", "pbkrtest", "pillar", "pkgconfig", "polynom", "purrr", "quantreg", "R6", "rbibutils", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "Rdpack", "reformulas", "rlang", "rstatix", "S7", "scales", "SparseM", "statmod", "stringi", "stringr", "survival", "tensor", "tibble", "tidyr", "tidyselect", "timeDate", "urca", "utf8", "vctrs", "viridisLite", "withr", "zoo" ], "_score": 1.3424226808222064, "_indexed": true, "_nocasepkg": "copularemada", "_universes": [ "cran" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.7.5", "date": "2026-05-24T09:31:58.000Z", "distro": "noble", "commit": "cb8bbb0e1f744b0e7e93722aab7ea53a9af071bc", "fileid": "6b479752245aba16302a316bc0606be108348d3b01b330e429e1a4b9e44fc4b5", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/26357505427" }, { "r": "4.6.0", "os": "linux", "version": "1.7.5", "date": "2026-05-24T09:31:57.000Z", "distro": "noble", "commit": "cb8bbb0e1f744b0e7e93722aab7ea53a9af071bc", "fileid": "0f97c1b0b1c2ae6ee4a59b0d8a66dd78940671e8a7fb6ac33d115e6dca84f80a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/26357505427" }, { "r": "4.6.0", "os": "wasm", "version": "1.7.5", "date": "2026-05-24T09:32:03.000Z", "commit": "cb8bbb0e1f744b0e7e93722aab7ea53a9af071bc", "fileid": "d868ba728487c07cc61e149f68e19fc8cdeb215c1a81bca6a9cb3ba1f753e807", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/26357505427" } ] }