Bicop

class Bicop(*args, **kwargs)

A class for bivariate copula models.

The model is fully characterized by the family, rotation (one of 0, 90, 180, 270), a matrix of parameters, and variable types (two strings, one for each variable, either "c" for continuous or "d" for discrete).

Implemented families (see BicopFamily):

| type          | full name             | string identifier     |
|---------------|-----------------------|-----------------------|
| -             | Independence          | "indep"               |
| Elliptical    | Gaussian              | "gaussian"            |
|               | Student t             | "student"             |
| Archimedean   | Clayton               | "clayton"             |
|               | Gumbel                | "gumbel"              |
|               | Frank                 | "frank"               |
|               | Joe                   | "joe"                 |
|               | Clayton-Gumbel (BB1)  | "bb1"                 |
|               | Joe-Gumbel (BB6)      | "bb6"                 |
|               | Joe-Clayton (BB7)     | "bb7"                 |
|               | Joe-Frank (BB8)       | "bb8"                 |
| Extreme-Value | Tawn                  | "tawn"                |
| Nonparametric | Transformation kernel | "tll"                 |

Attributes

`family`	The copula family.
`nobs`	The number of observations (only for fitted objects).
`npars`	The number of parameters (for nonparametric families, a conceptually similar definition).
`parameters`	The copula parameter(s).
`parameters_lower_bounds`	Gets lower bounds for copula parameters.
`parameters_upper_bounds`	Gets upper bounds for copula parameters.
`rotation`	The copula rotation.
`tau`	The Kendall's tau.
`var_types`	The type of the two variables.

Methods

`__init__`	Default constructor for the `Bicop` class.
`aic`	Evaluates the Akaike information criterion (AIC).
`bic`	Evaluates the Bayesian information criterion (BIC).
`cdf`	Evaluates the copula distribution.
`fit`	Fits a bivariate copula (with fixed family) to data.
`from_data`	Instantiates from data.
`from_family`	Instantiates a specific bivariate copula model.
`from_file`	Instantiates from a JSON file.
`from_json`	Instantiates from a JSON-like str object.
`hfunc1`	Evaluates the first h-function.
`hfunc2`	Evaluates the second h-function.
`hinv1`	Evaluates the inverse of the first h-function.
`hinv2`	Evaluates the inverse of the second h-function.
`loglik`	Evaluates the log-likelihood.
`mbic`	Evaluates the modified Bayesian information criterion (mBIC).
`parameters_to_tau`	Converts the copula parameters to Kendall's \(tau\).
`pdf`	Evaluates the copula density.
`plot`	Generates a plot for the Bicop object.
`select`	Selects the best fitting model.
`simulate`	Simulates from a bivariate copula.
`str`	Summarizes the model into a string (can be used for printing).
`tau_to_parameters`	Converts a Kendall's \(\tau\) into copula parameters for one-parameter families.
`to_file`	Write the copula object into a JSON file.
`to_json`	Convert the copula into a nlohmann::json object.