The goal of coperate is to facilitate the modelling of parametric
copula-based models. Resolving a copula model involves more than one
step such as fitting a maximum likelihood model. It more often first
requires choosing appropriate parametric copula families, their
orientations, and sometimes even restricting the canonical parameter
spaces.
coperate cooperates with you to do this by
- providing an extensive built-in selection of parametric copula models;
- creating user-defined parametric copula families (optionally);
- operating on the copula parameter space to restrict, relax, and alter the space; and
- computing distributional quantities from a copula model,
This package intentionally does not include model-fitting functionality, such as MLE and CNQR, but is intended to be useful as a back-end for such fitting.
The entities that this package deal with can be usefully categorized as copulas, parameters, and operations.
- Copulas: A bundle of properties that describe a copula. Things like kendall’s tau, CCEVI, tail dependence, even the cdf at a point can be considered a property. Not all properties uniquely define a copula, but the “canonical” ones will ought to be distribution-related functions. Ideally, other properties will be defined, like kendall’s tau (even density), but could be calculated if need be
- Parameters: Variables that disambiguate a copula. Includes
family name, canonical parameters, symmetries~~, and possibly
extensions~~. A family is a collection of copulas continuously
related, indexed by a continuous set of canonical parameters.
This family might be extended by some parametric transformation – for example, adding a skew, or considering the interpolated version of a DJ copula family.A symmetry is a re-orientation of a copula by reflection and/or permutation. - Operations: include things like relaxing, shrinking, or mutating the parameter space, similar to how a data frame might be filtered, expanded, or mutated.
- Closures?: Possibly include transformations of a copula to another copula. For example, a threshold copula (as in the DJ paper), or extreme value copula, or skew copula.
This package cannot include a comprehensive list of any one of these. The user should be allowed to make their own. But this package ought to start with the basics of these.
coperate is not on CRAN yet, so the best way to install it is:
devtools::install_github("vincenzocoia/coperate")Please note that the coperate project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.