{"id":32209993,"url":"https://github.com/vinecopulib/rvinecopulib","last_synced_at":"2025-10-22T06:22:42.613Z","repository":{"id":19311972,"uuid":"86737770","full_name":"vinecopulib/rvinecopulib","owner":"vinecopulib","description":"R interface to the vinecopulib C++ library","archived":false,"fork":false,"pushed_at":"2025-10-07T08:25:12.000Z","size":54606,"stargazers_count":35,"open_issues_count":5,"forks_count":10,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-10-07T10:24:05.482Z","etag":null,"topics":["copula","estimation","statistics","vine"],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vinecopulib.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2017-03-30T18:54:11.000Z","updated_at":"2025-06-13T13:04:08.000Z","dependencies_parsed_at":"2023-02-11T13:15:14.869Z","dependency_job_id":"70c03910-5fa9-4fa5-90b0-90f11815ee79","html_url":"https://github.com/vinecopulib/rvinecopulib","commit_stats":{"total_commits":180,"total_committers":4,"mean_commits":45.0,"dds":0.5444444444444445,"last_synced_commit":"d8a1e0fb5aba0889324b91df1323cc9cfda70d7f"},"previous_names":[],"tags_count":35,"template":false,"template_full_name":null,"purl":"pkg:github/vinecopulib/rvinecopulib","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinecopulib%2Frvinecopulib","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinecopulib%2Frvinecopulib/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinecopulib%2Frvinecopulib/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinecopulib%2Frvinecopulib/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vinecopulib","download_url":"https://codeload.github.com/vinecopulib/rvinecopulib/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinecopulib%2Frvinecopulib/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280391224,"owners_count":26322880,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-22T02:00:06.515Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["copula","estimation","statistics","vine"],"created_at":"2025-10-22T06:22:37.310Z","updated_at":"2025-10-22T06:22:42.598Z","avatar_url":"https://github.com/vinecopulib.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"rvinecopulib\n==========\n\n[![R-CMD-check](https://github.com/vinecopulib/rvinecopulib/actions/workflows/R-CMD-check.yaml/badge.svg?branch=main)](https://github.com/vinecopulib/rvinecopulib/actions/workflows/R-CMD-check.yaml)\n[![Coverage Status](https://img.shields.io/codecov/c/github/vinecopulib/rvinecopulib/main.svg)](https://app.codecov.io/github/vinecopulib/rvinecopulib?branch=main)\n[![CRAN version](https://www.r-pkg.org/badges/version/rvinecopulib)](https://cran.r-project.org/package=rvinecopulib) \n[![CRAN downloads](https://cranlogs.r-pkg.org/badges/rvinecopulib)](https://cran.r-project.org/package=rvinecopulib)\n\nVine copulas are a flexible class of dependence models consisting of bivariate \nbuilding blocks (see e.g., Aas et al., 2009). You can find a comprehensive \nlist of publications and other materials on [vine-copula.org](https://www.math.cit.tum.de/math/forschung/gruppen/statistics/vine-copula-models/).\n\nThis package is the [R](https://cran.r-project.org/) API to the C++ library \n[vinecopulib](https://github.com/vinecopulib/vinecopulib), a header-only \nC++ library for vine copula models based on [Boost](https://www.boost.org/) and \n[Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page).\n\nIt provides high-performance implementations of the core features of the popular\n[VineCopula R library](https://github.com/tnagler/VineCopula), in particular\ninference algorithms for both vine copula and bivariate copula models.\nAdvantages over VineCopula are  \n* a sleaker and more modern API,\n* shorter runtimes, especially in high dimensions,\n* nonparametric and multi-parameter families,\n* ability to model discrete variables.\n\nAs VineCopula, the package is primarily made for the statistical analysis of \n**vine copula models**. The package includes tools for parameter estimation, \nmodel selection, simulation, and visualization. Tools for estimation, selection \nand exploratory data analysis of **bivariate copula** models are also provided. \nPlease see the [API documentation](https://vinecopulib.github.io/rvinecopulib/) \nfor a detailed description of all functions.\n\nTable of contents\n-----------------\n\n- [rvinecopulib](#rvinecopulib)\n  - [Table of contents](#table-of-contents)\n  - [How to install](#how-to-install)\n  - [Package overview](#package-overview)\n    - [Bivariate copula modeling: bicop_dist and bicop](#bivariate-copula-modeling-bicopdist-and-bicop)\n    - [Vine copula modeling: vinecop_dist and vinecop](#vine-copula-modeling-vinecopdist-and-vinecop)\n    - [Bivariate copula families](#bivariate-copula-families)\n  - [References](#references)\n\n------------------------------------------------------------------------\n\n\nHow to install\n--------------\n\n\nYou can install:\n\n-   the stable release on CRAN:\n\n    ``` r\n    install.packages(\"rvinecopulib\")\n    ```\n\n-   the latest development version:\n\n    ``` r\n    remotes::install_github(\"vinecopulib/rvinecopulib\")\n    ```\n\n------------------------------------------------------------------------\n\nPackage overview\n----------------\n\nBelow, we list most functions and features you should know about. As usual in \ncopula models, data are assumed to be serially independent and lie in the unit\nhypercube. \n\n### Bivariate copula modeling: bicop_dist and bicop\n\n  * `bicop_dist`: Creates a bivariate copula by specifying the family, rotation \n    and parameters. Returns an object of class `bicop_dist`. The class has the\n    following methods:\n     \n     * `print`: a brief overview of the bivariate copula. \n            \n     * `plot`, `contour`: surface/perspective and contour plots of the copula\n        density. Possibly coupled with standard normal margins (default for\n        `contour`). \n        \n  * `dbicop`, `pbicop`, `rbicop`, `hbicop`: Density, distribution function, random \n    generation and H-functions (with their inverses) for bivariate copula \n    distributions. Additionally to the evaluation points, you can provide \n    either `family`, `rotation` and `parameter`, or an object of class \n    `bicop_dist`.\n\n  * `bicop`: Estimates parameters of a bivariate copula. Estimation can be done \n    by maximum likelihood (`par_method = \"mle\"`) or inversion of the empirical \n    Kendall's tau (`par_method = \"itau\"`, only available for one-parameter \n    families) for parametric families, and using local-likelihood \n    approximations of order zero/one/two for nonparametric models \n    (`nonpar_method=\"constant\"`/`nonpar_method=\"linear\"`/`nonpar_method=\"quadratic\"`). \n    If `family_set` is a vector of families, then the family is selected using\n    `selcrit=\"loglik\"`, `selcrit=\"aic\"` or `selcrit=\"bic\"`. The function \n    returns an object of classes `bicop` and `bicop_dist`.\n    The class `bicop` has the following following methods:\n    \n     * `print`: a more comprehensive overview of the bivariate copula model \n       with fit statistics. \n            \n     * `predict`, `fitted`: predictions and fitted values for a bivariate \n       copula model.\n       \n     * `nobs`, `logLik`, `AIC`, `BIC`: usual fit statistics.\n\n### Vine copula modeling: vinecop_dist and vinecop\n\n  * `vinecop_dist`: Creates a vine copula by specifying a nested list of \n    `bicop_dist` objects and a quadratic structure matrix. \n    Returns an object of class `vinecop_dist`. The class has the\n    following methods:\n     \n     * `print`, `summary`: a brief and more comprehensive overview of the vine \n       copula. \n            \n     * `plot`: plots of the vine structure. \n        \n  * `dvinecop`, `pvinecop`, `rvinecop`: Density, distribution function, random \n    generation for vine copula distributions. \n\n  * `vinecop`: automated fitting for vine copula models. The function inherits \n    the parameters of `bicop`. Optionally, a quadratic `matrix` can be used as \n    input to pre-specify the vine structure. `tree_crit` describes the \n    criterion for tree selection, one of `\"tau\"`, `\"rho\"`, `\"hoeffd\"` for \n    Kendall's tau, Spearman's rho, and Hoeffding's D, respectively.\n    Additionally, `threshold` allows to threshold the `tree_crit` and \n    `trunc_lvl` to truncate the vine copula, with `threshold_sel` and \n    `trunc_lvl_sel` to automatically select both parameters. The function \n    returns an object of classes `vinecop` and `vinecop_dist`.\n    The class has the `vinecop` has the following following methods:\n    \n     * `print`, `summary`: a brief and more comprehensive overview of the vine \n       copula with additional fit statistics information.\n            \n     * `predict`, `fitted`: predictions and fitted values for a vine \n       copula model.\n       \n     * `nobs`, `logLik`, `AIC`, `BIC`: usual fit statistics.\n\n### Bivariate copula families\n\nIn this package several bivariate copula families are included for bivariate \nand multivariate analysis using vine copulas. It provides \nfunctionality of elliptical (Gaussian and Student-t) as well as Archimedean \n(Clayton, Gumbel, Frank, Joe, BB1, BB6, BB7 and BB8) copulas to cover a large\nrange of dependence patterns. For Archimedean copula families,\nrotated versions are included to cover negative dependence as well. \nAdditionally, nonparametric families are also supported.\n\n| type          | name                  | name in R  |\n| ------------- | --------------------- | ---------- |\n| -             | Independence          | \"indep\"    |\n| Elliptical    | Gaussian              | \"gaussian\" |\n| \"             | Student t             | \"t\"        |\n| Archimedean   | Clayton               | \"clayton\"  |\n| \"             | Gumbel                | \"gumbel\"   |\n| \"             | Frank                 | \"frank\"    |\n| \"             | Joe                   | \"joe\"      |\n| \"             | Clayton-Gumbel (BB1)  | \"bb1\"      |\n| \"             | Joe-Gumbel (BB6)      | \"bb6\"      |\n| \"             | Joe-Clayton (BB7)     | \"bb7\"      |\n| \"             | Joe-Frank (BB8)       | \"bb8\"      |\n| Nonparametric | Transformation kernel | \"tll\"      |\n\nNote that several convenience vectors of families are included:\n* `\"all\"` contains all the families\n* `\"parametric\"` contains the parametric families (all except `\"tll\"`)\n* `\"nonparametric\"` contains the nonparametric families (`\"indep\"` and `\"tll\"`)\n* `\"one_par\"` contains the parametric families with a single parameter\n(`\"gaussian\"`, `\"clayton\"`, `\"gumbel\"`, `\"frank\"`, and `\"joe\"`)\n* `\"two_par\"` contains the parametric families with two parameters\n(`\"t\"`, `\"bb1\"`, `\"bb6\"`, `\"bb7\"`, and `\"bb8\"`)\n* `\"elliptical\"` contains the elliptical families\n* `\"archimedean\"` contains the archimedean families\n* `\"BB\"` contains the BB families\n* `\"itau\"` families for which estimation by Kendall's tau inversion is available\n(`\"indep\"`,`\"gaussian\"`, `\"t\"`,`\"clayton\"`, `\"gumbel\"`, `\"frank\"`, `\"joe\"`)\n\nThe following table shows the parameter ranges of bivariate copula families with \none or two parameters:\n\n| Copula family        | `par[1]`   | `par[2]`   |\n| :------------------- | :--------- | :--------- |\n| Gaussian             | `(-1, 1)`  | -          |\n| Student t            | `(-1, 1)`  | `(2,Inf)`  |\n| Clayton              | `(0, Inf)` | -          |\n| Gumbel               | `[1, Inf)` | -          |\n| Frank                | `R \\ {0}`  | -          |\n| Joe                  | `(1, Inf)` | -          |\n| Clayton-Gumbel (BB1) | `(0, Inf)` | `[1, Inf)` |\n| Joe-Gumbel (BB6)     | `[1 ,Inf)` | `[1, Inf)` |\n| Joe-Clayton (BB7)    | `[1, Inf)` | `(0, Inf)` |\n| Joe-Frank (BB8)      | `[1, Inf)` | `(0, 1]`   |\n\n------------------------------------------------------------------------\n\nReferences\n----------\n\nAas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions \nof multiple dependence. Insurance: Mathematics and Economics 44 (2), 182-198.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvinecopulib%2Frvinecopulib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvinecopulib%2Frvinecopulib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvinecopulib%2Frvinecopulib/lists"}