Matlab Copula Toolbox.zip

### Overview Contains many tools useful for copula modeling in Matlab that do not exist directly in the Statistics and Machine Learning toolbox. Highlights are: - Smooth empirical copula density estimation via Beta-Kernels for any D>=2. - Empirical copula function estimation for any D>=2 - Sampling from calculated empirical copula for D>=2 - Clayton/Frank/Gumbel copula PDF and sampling for D>=2 ### Directory Structure - **algorithms/** - contains the core copula algorithms. | File | Description | | --- | --- | | *claytoncopulapdf.m* | Computes the Clayton Copula's PDF for D>=2 | | *claytoncopularnd.m* | Samples from a D>=2 Clayton Copula | | *computeEmpiricalDiscreteProb.m* | Computes empirical multinomial distribution | | *continueRv.m* | Continues realizations of a discrete RV (see http://dx.doi.org/10.1016/j.jmva.2004.01.004) | | *empcopulaval.m* | Computes value of an empirical copula at a specified point in unit hypercube | | *empcopulapdf.m* | Computes empirical copula density given pseudo-observations | | *empcopulacdf.m* | Computes empirical copula function given pseudo-observations | | *empcopularnd.m* | Generates samples from an empirical copula | | *estMteDensity.m* | KDE with trucanted exponential distribution | | *frankcopulapdf.m* | Computes the Frank Copula's PDF for D>=2 | | *frankcopularnd.m* | Samples from a D>=2 Frank Copula | | *gumbelcopulapdf.m* | Computes the Gumbel Copula's PDF for D>=2 | | *gumbelcopularnd.m* | Samples from a D>=2 Gumbel Copula | | *hyperFunctionError.m* | Computes error between two hyper functions of the same dimensionality | | *log1mexp.m* | Convenience function for log(1-exp(a)) | | *logserrnd.m* | Samples from the Log-Series distribution | | *pobs.m* | Computes pseudo-observations for a given (multivariate) random vector | | *stable1rnd.m* | Samples from the Stable Distribution | | *etstablernd.m* | Samples from the Exponentially Tilted Distribution | | *logrnd.m* | Samples from the Log Distribution | - **simulations/** - contains simulation code which uses the algorithms developed