rvinecopulib
rvinecopulib provides high-performance tools for bivariate and vine copula models. It covers model construction, estimation and selection, simulation, prediction, visualization, discrete and mixed data, and full multivariate distributions with fitted margins. The package is the R interface to the vinecopulib C++ library.
Main capabilities
- fit, select, evaluate, and simulate bivariate copula models, including parametric, nonparametric, rotated, and extreme-value families;
- fit and select high-dimensional vine copula models and structures, with automatic family, structure, truncation, and threshold selection;
- combine vine copulas with nonparametric, parametric, or custom marginal models to form complete multivariate distributions;
- handle continuous, integer-valued discrete, mixed, and zero-inflated data, with optional observation weights;
- perform conditional simulation and Rosenblatt transforms;
- compute likelihood scores and Hessians and evaluate models with observation-specific parameters;
- use parallel fitting, custom marginal families, and custom tree-selection criteria in extensible modeling workflows.
Installation
Install the stable release from CRAN:
install.packages("rvinecopulib")Install the development version from GitHub:
remotes::install_github("vinecopulib/rvinecopulib")Examples
rvinecopulib exposes the same modeling framework at three levels:
| Starting point | Main function | Model |
|---|---|---|
| Two uniform variables | bicop() | One bivariate copula |
| Uniform pseudo-observations | vinecop() | Dependence only |
| Observations on their original scale | vine() | Margins and dependence |
A bivariate copula
bicop() fits and selects a copula model for two uniform variables.
Fixed models can be created with bicop_dist().
u <- rbicop(200, family = "clayton", rotation = 90, parameters = 2)
bivariate_fit <- bicop(u, family_set = "par")
summary(bivariate_fit)
dbicop(u[1:5, ], bivariate_fit)
tail_dep(bivariate_fit)See the bivariate-copula article for implemented families, rotations, h-functions, dependence measures, and selection controls.
A vine copula on the copula scale
pseudo_obs() converts continuous observations to approximately uniform
scores. vinecop() then selects the vine structure, pair-copula
families, and parameters.
u <- pseudo_obs(as.matrix(USArrests))
copula_fit <- vinecop(u, family_set = "onepar")
summary(copula_fit)
simulated_u <- rvinecop(100, copula_fit)
dvinecop(simulated_u[1:5, ], copula_fit)See the vine-copula article for structure construction, selection, truncation, and model inspection.
A full distribution on the original scale
vine() fits one marginal distribution per variable and a vine copula
to their probability integral transforms. The default margins are
nonparametric; parametric and custom marginal families are also
supported.
n <- 150
latent <- rnorm(n)
x <- data.frame(
amount = exp(latent + rnorm(n, sd = 0.5)),
duration = exp(0.5 * latent + rnorm(n, sd = 0.7)),
count = rpois(n, exp(0.2 + 0.3 * latent))
)
fit <- vine(
x,
var_types = c("c", "c", "d"),
copula_controls = list(family_set = "onepar")
)
summary(fit)
rvine(5, fit)See the marginal-modeling article for parametric selection, custom families, ordered variables, zero inflation, and observation weights. The getting-started article develops the complete workflow.
The constructors bicop_dist(), vinecop_dist(), and vine_dist()
create models from components specified directly.
The complete API reference and all articles are available on the package website. Questions and bug reports are welcome in the GitHub issue tracker.