A complete model on the data scale | A dependence model on the copula scale | A bivariate model | Fixed and fitted models | Selection controls | Reproducibility and persistence | Where to go next
Package: rvinecopulib 1.0.0.1.0
Thomas Nagler
rvinecopulib: High Performance Algorithms for Vine Copula Modeling
Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 1.0.0). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.
Authors:
rvinecopulib_1.0.0.1.0.tar.gz
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rvinecopulib_1.0.0.1.0.tar.gz(r-4.7-arm64)rvinecopulib_1.0.0.1.0.tar.gz(r-4.7-x86_64)rvinecopulib_1.0.0.1.0.tar.gz(r-4.6-arm64)rvinecopulib_1.0.0.1.0.tar.gz(r-4.6-x86_64)
rvinecopulib_1.0.0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rvinecopulib/json (API)
| # Install 'rvinecopulib' in R: |
| install.packages('rvinecopulib', repos = c('https://vinecopulib.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/vinecopulib/rvinecopulib/issues
Pkgdown/docs site:https://vinecopulib.github.io
copulaestimationstatisticsvinecpp
Last updated from:2ac9d35c1b. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 235 | ||
| linux-devel-x86_64 | OK | 236 | ||
| source / vignettes | OK | 407 | ||
| linux-release-arm64 | OK | 247 | ||
| linux-release-x86_64 | OK | 267 | ||
| macos-release-arm64 | OK | 261 | ||
| macos-release-x86_64 | OK | 472 | ||
| macos-oldrel-arm64 | OK | 219 | ||
| macos-oldrel-x86_64 | OK | 361 | ||
| windows-devel-arm64 | OK | 333 | ||
| windows-devel-x86_64 | OK | 319 | ||
| windows-release-arm64 | OK | 318 | ||
| windows-release-x86_64 | OK | 277 | ||
| windows-oldrel-x86_64 | OK | 313 | ||
| wasm-release | OK | 185 |
Exports:as_marginas_rvine_matrixas_rvine_structureas.bicopbicopbicop_distblomqvist_betacvine_structuredbicopdmargindvinedvine_structuredvinecopemp_cdffit_marginget_all_familiesget_all_ktausget_all_pair_copulasget_all_parametersget_familyget_ktauget_matrixget_pair_copulaget_parametersget_structurehbicophessianinverse_rosenblattis.rvine_matrixis.rvine_structurekde1d_familyktau_to_parmargin_distmargin_familymargin_infomBICVpairs_copula_datapar_to_ktaupbicoppmarginpseudo_obspvinepvinecopqmarginrbicoprosenblattrvinervine_matrixrvine_matrix_simrvine_structurervine_structure_simrvinecopscoresstats_margintail_deptruncate_modelunivariateML_familyvinevine_distvinecopvinecop_distzero_inflated
Dependencies:assertthatBHkde1dlatticerandtoolboxRcppRcppEigenRcppThreadrngWELLwdm
Getting started with rvinecopulib
Last update: 2026-09-18
Started: 2026-09-18
Bivariate copula models
Implemented families | Construct a known model | Evaluate and simulate | Conditional distributions and h-functions | Dependence measures | Fit and select from data | Observation weights and missing values | Discrete variables | Observation-specific parameters | Related documentation
Last update: 2026-09-18
Started: 2026-09-18
Conditional simulation and Rosenblatt transforms
Select a conditioning-aware structure | Simulate conditionally on the copula scale | Condition on the original data scale | Reorient an existing model transiently | Discrete conditioning variables | Rosenblatt transforms | Reproducibility | Related documentation
Last update: 2026-09-18
Started: 2026-09-18
Discrete, mixed, and zero-inflated data
Discrete variables on the original scale | Ordered factors | Zero-inflated variables | Copula-scale data layouts | What the density means | Simulation | Rosenblatt transforms with atoms | Missing observations | Related documentation
Last update: 2026-09-18
Started: 2026-09-18
Marginal models
Default nonparametric margins | Observation weights | Parametric selection with univariateML | Custom fitted families | The fitted-margin protocol | Integer-valued discrete variables | Continuous variables with an atom at zero | Fixed margins and persistence | Related documentation
Last update: 2026-09-18
Started: 2026-09-18
Scores, Hessians, and varying parameters
Bivariate scores and Hessians | Vine parameter ordering | Stepwise and full likelihood derivatives | Reuse density intermediates | Observation-specific bivariate parameters | Observation-specific vine parameters | From derivatives to uncertainty estimates | Related documentation
Last update: 2026-09-18
Started: 2026-09-18
Vine copula models and structures
Read an R-vine structure | Construct a vine copula from components | Fit and select a model | Inspect the selected trees | Control structure selection | Fix all or part of a structure | Truncation and thresholding | Refit an existing model | Related documentation
Last update: 2026-09-18
Started: 2026-09-18
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Normalize an object to the fitted-margin protocol | as_margin |
| Coerce various kind of objects to R-vine structures and matrices | as_rvine_matrix as_rvine_matrix.list as_rvine_matrix.matrix as_rvine_matrix.rvine_matrix as_rvine_matrix.rvine_structure as_rvine_structure as_rvine_structure.list as_rvine_structure.matrix as_rvine_structure.rvine_matrix as_rvine_structure.rvine_structure |
| Convert list to bicop object | as.bicop |
| Fit and select bivariate copula models | bicop |
| Dependence measures of a bivariate copula | bicop_dependence blomqvist_beta tail_dep |
| Bivariate copula models | bicop_dist |
| Bivariate copula distributions | bicop_distributions dbicop dbicop_dist hbicop hbicop_dist hessian.bicop_dist pbicop pbicop_dist rbicop rbicop_dist scores.bicop_dist |
| Predictions and fitted values for a bivariate copula model | bicop_predict_and_fitted fitted.bicop predict.bicop predict.bicop_dist |
| Corrected Empirical CDF | emp_cdf |
| Extracts components of 'bicop_dist' and 'vinecop_dist' objects | getters get_all_families get_all_ktaus get_all_pair_copulas get_all_parameters get_family get_ktau get_matrix get_pair_copula get_parameters get_structure |
| Define a kde1d margin family | kde1d_family |
| Create a fitted or fixed custom margin | margin_dist |
| Define a custom margin family | margin_family |
| Margin-family fitting protocol | fit_margin margin_family_protocol |
| Fitted marginal distribution protocol | dmargin margin_info margin_protocol pmargin qmargin |
| Modified vine copula Bayesian information criterion (mBICv) | mBICV |
| Exploratory pairs plot for copula data | pairs_copula_data |
| Conversion between Kendall's tau and parameters | ktau_to_par par_to_ktau |
| Parameter uncertainty | confint.bicop_dist confint.vinecop_dist parameter_uncertainty vcov.bicop_dist vcov.vinecop_dist |
| Plotting tools for 'bicop_dist' and 'bicop' objects | contour.bicop contour.bicop_dist plot.bicop plot.bicop_dist |
| Plotting R-vine structures | plot.rvine_matrix plot.rvine_structure |
| Plotting 'vinecop_dist' and 'vinecop' objects. | contour.vinecop contour.vinecop_dist plot.vinecop plot.vinecop_dist |
| Pseudo-Observations | pseudo_obs |
| Rosenblatt and inverse Rosenblatt transforms | inverse_rosenblatt rosenblatt |
| R-vine structure | cvine_structure dvine_structure is.rvine_matrix is.rvine_structure rvine_matrix rvine_structure |
| Simulate R-vine structures | rvine_matrix_sim rvine_structure_sim |
| High Performance Algorithms for Vine Copula Modeling | rvinecopulib-package rvinecopulib |
| Create a fixed margin from a stats distribution | stats_margin |
| Truncate a vine copula model | truncate_model truncate_model.rvine_matrix truncate_model.rvine_structure truncate_model.vinecop_dist truncate_model.vine_dist |
| Define a univariateML margin family | univariateML_family |
| Vine copula models | vine vine_dist |
| Vine based distributions | dvine dvine_dist pvine pvine_dist rvine rvine_dist vine_distributions |
| Predictions and fitted values for a vine copula model | fitted.vine predict.vine vine_predict_and_fitted |
| Fitting vine copula models | vinecop |
| Vine copula models | vinecop_dist |
| Vine copula distributions | dvinecop dvinecop_dist hessian hessian.vinecop_dist pvinecop pvinecop_dist rvinecop rvinecop_dist scores scores.vinecop_dist vinecop_distributions |
| Predictions and fitted values for a vine copula model | fitted.vinecop predict.vinecop vinecop_predict_and_fitted |
| Declare zero-inflated data | zero_inflated |