HyperbolicDist and fBasics which cover
univariate generalized hyperbolic distributions and some of its special cases. However, the univariate case is contained
in this package as well because we aim to provide a uniform interface to deal with
generalized hyperbolic distribution. Recently an Rport of the S-Plus library QRMlib
was released. The package QRMlib contains fitting procedure for the NIG, hyp and
skewed Student-t case but not for the generalized hyperbolic case.
The package fMultivar implements
a fitting routine for multivariate skewed student-t distributions as well.plot(distribution.object).
Additionally, one can take advantage of generic programming sinceRprovides virtual
classes and some forms of polymorphism.Initialize:
ghyp Initialize a generalized hyperbolic distribution.
hyp Initialize a hyperbolic distribution.
NIG Initialize a normal inverse gaussian distribution.
VG Initialize a variance gamma distribution.
student.t Initialize a student-t distribution.
}
Density, distribution function, quantile function, expected shortfall and
random generation:
dghyp Density of a generalized hyperbolic distribution.
pghyp Distribution function of a generalized hyperbolic distribution.
qghyp Quantile of a univariate generalized hyperbolic distribution.
ESghyp Expected shortfall of a univariate generalized hyperbolic distribution.
rghyp Random generation of a generalized hyperbolic distribution.
}
Fit to data:
fit.ghypuv Fit a generalized hyperbolic distribution to univariate data.
fit.hypuv Fit a hyperbolic distribution to univariate data.
fit.NIGuv Fit a normal inverse gaussian distribution to univariate data.
fit.VGuv Fit a variance gamma distribution to univariate data.
fit.tuv Fit a skewed student-t distribution to univariate data.
fit.ghypmv Fit a generalized hyperbolic distribution to multivariate data.
fit.hypmv Fit a hyperbolic distribution to multivariate data.
fit.NIGmv Fit a normal inverse gaussian distribution to multivariate data.
fit.VGmv Fit a variance gamma distribution to multivariate data.
fit.tmv Fit a skewed student-t distribution to multivariate data.
stepAIC.ghyp Perform a model selection based on the AIC.
}
Portfolio optimization and utilities:
portfolio.optimize Calculate an optimal portfolio given a multivariate ghyp distribution.
mean Returns the expected value.
vcov Returns the variance(-covariance).
logLik Returns Log-Likelihood of fitted ghyp objects.
AIC Returns the Akaike's Information Criterion of fitted ghyp objects.
lik.ratio.test Performs a likelihood-ratio test on fitted ghyp distributions.
[ Extract certain dimensions of a multivariate ghyp distribution.
transform Transform a multivariate generalized hyperbolic distribution.
ghyp.moment Moments of the univariate ghyp distribution.
coef Parameters of a generalized hyperbolic distribution.
ghyp.data Data of a (fitted) generalized hyperbolic distribution.
ghyp.fit.info Information about the fitting procedure, log-likelihood and AIC value.
ghyp.name Returns the name of the ghyp distribution or a subclass of it.
summary Summary of a fitted generalized hyperbolic distribution.
}
Plot functions:
qqghyp Perform a quantile-quantile plot of a (fitted) univariate ghyp distribution.
hist Plot a histogram of a (fitted) univariate generalized hyperbolic distribution.
pairs Produce a matrix of scatterplots with quantile-quantile plots on the diagonal.
plot Plot the density of a univariate ghyp distribution.
lines Add the density of a univariate ghyp distribution to a graphics device.
}
Generalized inverse gaussian distribution:
dgig Density of a generalized inverse gaussian distribution
pgig Distribution function of a generalized inverse gaussian distribution
qgig Quantile of a generalized inverse gaussian distribution
ESgig Expected shortfall of a generalized inverse gaussian distribution
rgig Random generation of a generalized inverse gaussian distribution
}
Package vignette:
A document about generalized hyperbolic distributions can be found in the
doc folder of this package.
QRMlib