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The main purpose of this package is to allow fitting of mixture distributions with GAMLSS models.


This package contains the distributions for GAMLSS modelling.


Bi-variate data fitting is done by two stochastic components: the marginal distributions and the dependency structure. The dependency structure is modeled through a copula. An algorithm was implemented considering seven families of copulas (Generalized Archimedean Copulas), the best fitting can be obtained looking all copula's options (totally positive of order 2 and stochastically increasing models).


Environment for teaching "Financial Engineering and Computational Finance"


Environment for teaching "Financial Engineering and Computational Finance"


Environment for teaching "Financial Engineering and Computational Finance" NOTE: SEVERAL PARTS ARE STILL PRELIMINARY AND MAY BE CHANGED IN THE FUTURE. THIS TYPICALLY INCLUDES FUNCTION AND ARGUMENT NAMES, AS WELL AS DEFAULTS FOR ARGUMENTS AND RETURN VALUES. Please donate,, to support future activities of the Rmetrics association.


Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.


Provides functions for the bayesian analysis of extreme value models, using MCMC methods.


Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.


S4 Classes and Methods for distributions