svine: Stationary vine distribution models

In svines: Stationary Vine Copula Models

Stationary vine distribution models

Description

Automated fitting or creation of custom S-vine distribution models

Usage

svine(
  data,
  p,
  margin_families = univariateML::univariateML_models,
  selcrit = "aic",
  ...
)

Arguments

data	a matrix or data.frame of data.
p	the Markov order.
margin_families	either a vector of univariateML::univariateML_models to select from (used for every margin) or a list with one entry for every variable. Can also be "empirical" for empirical cdfs.
selcrit	criterion for family selection, either "loglik", "aic", "bic", "mbicv".
...	arguments passed to svinecop().

Value

Returns the fitted model as an object with classes svine and svine_dist. A list with entries

• ⁠$margins⁠: list of marginal models from univariateML::univariateML_models,

• ⁠$copula⁠: an object of svinecop_dist.

See Also

svine_dist, svine_loglik, svine_sim, svine_bootstrap_models

Examples

# load data set
data(returns)  

# fit parametric S-vine model with Markov order 1
fit <- svine(returns[1:100, 1:3], p = 1, family_set = "parametric")
fit 
summary(fit)
plot(fit$copula)
contour(fit$copula)
logLik(fit)

pairs(svine_sim(500, rep = 1, fit))

svines documentation built on April 4, 2025, 5:02 a.m.