DiffusionRimp

Data-imputation and density approximations for diffusion processes.

What is DiffusionRimp?

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

Why use DiffusionRimp?

DiffusionRimp provides routines for exploring and analysing diffusion processes with highly non-linear dynamics. Although the analysis of non-linear diffusion processes is often quite challenging, the DiffusionRimp package makes it possible to analyse models with complicated dynamics with basic coding skills and experience in numerical analysis.

Get DiffusionRjgqd?

Check out DiffusionRimp for the package source files, vignettes and other downloadable content.

Installation Notes

Mac users may have to carry out some additional installation procedures in order for DiffusionRimp to operate optimally.

Mac users:

To install the latest version of Rcpp, the latest version of R is needed. To install RcppArmadillo, the Fortran version used by R needs to be updated. To install rgl, the computer needs to have X11 installed. Update R to the latest version. Run the following code:

install.packages("Rcpp", type = "source", dep = TRUE) 

Open a Terminal and run the following code:

curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2 sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

Back in R, run the following code:

install.packages("RcppArmadillo", dep = TRUE) 

Make sure you have X11 installed.

Go to Applications/Utilities and see if X11 is there. If not, you’ll need to install X11 or XQuartz. These are available from http://xquartz.macosforge.org/landing/

Back in R, run the following code:

install.packages(“rgl", dep = TRUE) 

Download the DiffusionRjgqd package and run the code:

install.packages("~/DiffusionRimp_0.1.2.tar.gz", repos = NULL, type = "source”)

Run the following code in R to see if the package works:

library(DiffpackRimp) 
example(MOL.density)