DiffusionRimp (version 0.1.2)

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

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++.

Examples

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