The goal of igcop is to provide tools for computing on the Integrated Gamma (IG) and Integrated Gamma Limit (IGL) copula families.
Currently under active development, and still developing a working version
igcop is not yet available on CRAN, but can be downloaded from this repository using devtools. Just execute this line of code in an R instance, after ensuring you have the devtools R package installed:
devtools::install_github("vincenzocoia/igcop")
The IG copula family is defined by parameters θ > 0 and k > 1, although computations are problematic for k < 2, although mostly just for k close to 1.
…
What makes the IG copula interesting is in regression analysis, where the response is the second variable. If the response is heavy-tailed, and is linked to a predictor via an IG copula, then its conditional distributions have lighter tails with a non-constant extreme value index across the predictor space. The IGL copula is interesting in a similar light, except its conditional distributions are all light-tailed (!) – meaning that the predictor is solely responsible for the heavy tail of the response variable.
This package piggybacks on the base R syntax for distributions, whose functions adopt the convention:
For IG and IGL copulas:
<prefix>
corresponds to one of:
r
for random number generation (currently not supported for conditional distributions),p
for cdf,d
for density, andq
for quantile (for conditional distributions only).<name>
corresponds to the possible names:
igcop
and iglcop
correspond to an IG copula and IGL copula, respectively.condigcop12
and condiglcop12
correspond to a conditional distribution of the first variable given the second, of an IG copula and IGL copula respectively.condigcop21
and condiglcop21
correspond to a conditional distribution of the second variable given the first, of an IG copula and IGL copula respectively (also available as condigcop
and condiglcop
to match the syntax of the CopulaModel package).All of these functions have a cpar
argument expecting the value of the copula parameters. For an IG copula, this is c(theta, k)
, and just k
for an IGL copula.
Here are some examples, starting with evaluating the density of an IG copula at (0.3, 0.6):
library(igcop) # digcop(0.3, 0.6, cpar = c(3, 2))
Computations are vectorized over both u
and v
(first and second variables). Here’s the cdf and density of an IGL copula at different values:
# u <- seq(0.1, 0.9, length.out = 9) # v <- seq(0.9, 0.5, length.out = 9) # piglcop(u, v, cpar = 4) # diglcop(0.2, v, cpar = 4)
It doesn’t make sense to talk about quantiles for a multivariate distribution, so this is only defined for conditional distributions. Note that the “2 given 1” distributions swap the u
and v
arguments to better align with the conditioning.
# qcondigcop(v, u, cpar = c(5, 3))
Generating 5 values from an IG copula:
# rigcop(5, cpar = c(5, 4))