MOL.density
(Package: DiffusionRimp) :
Approximate the Transition Density of a Scalar Diffusion with Arbitrary Drift and Volatility Specification
For scalar diffusions with drift mu=function(X,t){} and diffusion sig=function(X,t){}, MOL.density approximates the transition density of a scalar diffusion on a lattice [xlims[1],xlims[2]] x [s,t] with N spatial nodes and time discretization delt, via the method of lines. The method of lines approximates the solution of the Fokker-Planck equation by an N-dimensional system of ordinary differential equations (ODEs) evaluated on [s,t].
● Data Source:
CranContrib
● Keywords:
● Alias: MOL.density
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This function was created as a filler in order for the package to build correctly.
● Data Source:
CranContrib
● Keywords:
● Alias: junkfunction3
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MOL.passage
(Package: DiffusionRimp) :
Approximate the First Passage Time Density of a Two-Barrier Problem for Time-Homogeneous Scalar Diffusions.
For scalar diffusions with drift mu=function(X){} and diffusion sig=function(X){}, moving in relation to lower and upper bounds barriers[1] and barriers[2] respectively, MOL.passage() approximates a solution to the partial differential equation (PDE) that governs the evolution of the survaval distribution of the first passage time density via the method of lines (MOL).
● Data Source:
CranContrib
● Keywords:
● Alias: MOL.passage
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MOL.plot() recognizes output objects calculated using routines from the DiffusionRimp package and subsequently constructs an appropriate plot, for example a perspective plot of a transition density.
● Data Source:
CranContrib
● Keywords: plot
● Alias: MOL.plot
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BiMOL.density
(Package: DiffusionRimp) :
Approximate the Transition Density of a Bivariate Diffusion with Arbitrary Drift and Volatility Specification
BiMOL.density approximates the transition density of a bivariate diffusion on a lattice [xlims[1],xlims[2]] x [ylims[1],ylims[2]] x [s,t] with N x N spatial nodes and time discretization delt, via the method of lines. The method of lines approximates the solution of the Fokker-Planck equation by an N x N-dimensional system of ordinary differential equations (ODEs) evaluated on [s,t].
● Data Source:
CranContrib
● Keywords:
● Alias: BiMOL.density
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RS.estimates() calculates parameter estimates from .impute() model objects.
● Data Source:
CranContrib
● Keywords:
● Alias: RS.estimates
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MOL.aic() approximates the likelihood function for a diffusion model under a given dataset and parameter vector.
● Data Source:
CranContrib
● Keywords:
● Alias: MOL.aic
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BiRS.impute
(Package: DiffusionRimp) :
Brownian Bridge Data Imputation for Bivariate Diffusion Processes.
BiRS.impute performs inference on bivariate diffusion processes with quite arbitrary drift functionals by imputing missing sample paths with Brownian bridges. The procedure was developed by Roberts and Stramer (2001) and subsequently extended in later papers (). Currently, the diffusion is assumed to take on the form
● Data Source:
CranContrib
● Keywords:
● Alias: BiRS.impute
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BiMOL.passage
(Package: DiffusionRimp) :
Approximate the First Passage Time Density of a Four-Barrier Problem for Time-Homogeneous Bivariate Diffusions.
BiMOL.passage() approximates a solution to partial differential equation (PDE) that governs the evolution of the survaval distribution of the first passage time density of a bivariate diffusion passing through fixed thresholds barriers[1] or barriers[2] in the X-dimension and barriers[3] or barriers[4] in the Y-dimension.
● Data Source:
CranContrib
● Keywords:
● Alias: BiMOL.passage
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RS.impute
(Package: DiffusionRimp) :
Brownian Bridge Data Imputation for Scalar Diffusion Processes.
RS.impute performs inference on bivariate diffusion processes with quite arbitrary drift functionals by imputing missing sample paths with Brownian bridges. The procedure was developed by Roberts and Stramer (2001) and subsequently extended in later papers (Dellaportas et al, 2006; Kalogeropoulos et al., 2011). Currently, the diffusion is assumed to take on the form
● Data Source:
CranContrib
● Keywords:
● Alias: RS.impute
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providing 
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