GQD.dic() summarizes the MCMC output from a list of GQD.mcmc() objects. This may be used to neatly summarize the MCMC output of various models fitted to a given dataset.
● Data Source:
CranContrib
● Keywords: deviance information criterion (DIC)
● Alias: GQD.dic
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BiGQD.mcmc() uses parametrised coefficients (provided by the user as R-functions) to construct a C++ program in real time that allows the user to perform Bayesian inference on the resulting diffusion model. Given a set of starting parameters and other input parameters, a MCMC chain is returned for further analysis. BiGQD.density generates approximate transitional densities for a class of bivariate diffusion processes with SDE:
● Data Source:
CranContrib
● Keywords: C++, MCMC, syntax
● Alias: BiGQD.mcmc
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GQD.passage uses the cumulant truncation procedure of Varughese (2013) in conjunction with a Volterra-type integral equation developed by Buonocore et al. (1987) in order to approximate the first passage time density of a time-homogeneous univariate GQD
● Data Source:
CranContrib
● Keywords: C++, first passage time, syntax
● Alias: GQD.passage
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GQD.plot() recognizes output objects calculated using routines from the DiffusionRgqd package and subsequently constructs an appropriate plot, for example a perspective plot of a transition density.
● Data Source:
CranContrib
● Keywords: plot
● Alias: GQD.plot
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BiGQD.mle() uses parametrised coefficients (provided by the user as R-functions) to construct a C++ program in real time that allows the user to perform maximum likelihood inference on the resulting diffusion model. BiGQD.density generates approximate transitional densities for a class of bivariate diffusion processes with SDE:
● Data Source:
CranContrib
● Keywords: C++, maximum likelihood, syntax
● Alias: BiGQD.mle
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GQD.aic() summarizes the MCMC output from a list of GQD.mle() objects. This may be used to neatly summarize the MCMC output of various models fitted to a given dataset.
● Data Source:
CranContrib
● Keywords: Akaike information criterion (AIC), Bayesian information criterion (BIC)
● Alias: GQD.aic
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GQD.mcmc() uses parametrised coefficients (provided by the user as R-functions) to construct a C++ program in real time that allows the user to perform Bayesian inference on the resulting jump diffusion model. Given a set of starting parameters, a MCMC chain is returned for further analysis.
● Data Source:
CranContrib
● Keywords: C++, mcmc, syntax
● Alias: GQD.mcmc
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This function was created as a filler in order for the package to build correctly.
● Data Source:
CranContrib
● Keywords:
● Alias: junkfunction
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Removes any existing coefficient functions from the current workspace.
● Data Source:
CranContrib
● Keywords: remove models
● Alias: GQD.remove
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BiGQD.density generates approximate transitional densities for bivariate generalized quadratic diffusions (GQDs). Given a starting coordinate, (Xs, Ys), the approximation is evaluated over a lattice Xt x Yt for an equispaced discretization (intervals of width delt) of the transition time horizon [s, t] . BiGQD.density generates approximate transitional densities for a class of bivariate diffusion processes with SDE:
● Data Source:
CranContrib
● Keywords: bivariate Edgeworth, bivariate saddlepoint, cumulants, moments, transition density
● Alias: BiGQD.density
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