DiffusionRimp-package: Data-imputation and density approximations for diffusion...

In DiffusionRimp: Inference and Analysis for Diffusion Processes via Data Imputation and Method of Lines

Description

A package for performing data imputation on discretely observed diffusion processes as well as calculating numerical approximations to transition and first passage time densities.

Details

Package:	DiffusionRimp
Type:	Package
Version:	0.1.0
Date:	2015-12-01
License:	GPL (>= 2)

Functions included in the package:

RS.impute	:	Perform inference on a diffusion model using the random walk Metropolis-Hastings algorithm using the data-imputation algorithm.
BiRS.impute	:	Perform inference on a bivariate diffusion model using the random walk Metropolis-Hastings algorithm using the data-imputation algorithm.
MOL.density	:	Calculate the transitional density of a diffusion model using the method of lines.
BiMOL.density	:	Calculate the transitional density of a bivariate diffusion model using the method of lines.
MOL.passage	:	Calculate the first passage time density of a time-homogeneous diffusion model with fixed barriers (i.e., a two-barrier first passage time problem).
BiMOL.passage	:	Calculate the first passage time density of a time-homogeneous bivariate diffusion model with fixed barriers (i.e., a four-barrier problem in two dimensions).
MOL.aic*	:	Calculate a pseudo-AIC value for a diffusion model using the method of lines.
BiMOL.aic*	:	Calculate pseudo-AIC value for a bivariate diffusion model using the method of lines.

* Functions use C++.

Author(s)

Etienne A.D. Pienaar [e-mail]

Examples

# example(RS.impute)
# example(MOL.density)
# example(MOL.passage)

DiffusionRimp documentation built on May 2, 2019, 2 p.m.