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
- 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