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

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

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

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

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

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

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