DiffusionRgqd

Inference and Analysis for Generalized Quadratic Diffusions

What is DiffusionRgqd?

DiffusionRgqd is collection of tools for performing inference and analysis on scalar and bivariate time-inhomogeneous diffusion processes with quadratic drift and diffusion terms in R.

Why use DiffusionRgqd?

DiffusionRgqd provides a simple interface that requires minimal mathematical input in order to perform analysis on non-linear, time-inhomogeneous diffusion processes. The package also makes use of C++ in order to maximize the computational efficiency of inference routines. As such it is possible to conduct inference on a plethora of models in a desktop environment without incurring excessively long computation times.

Get DiffusionRgqd?

Check out DiffusionRgqd for the package source files, vignettes and other downloadable content or visit the DiffusionRgqd CRAN page.

Installation Notes

Mac users may have to carry out some additional installation procedures in order for DiffusionRjgqd 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=T)

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=T)

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=T)

Download the DiffusionRjgqd package and run the code:

install.packages("~/DiffusionRgqd_1.0.tar.gz", repos = NULL, type = "source”)

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

library(DiffpackRgqd) example(GQD.density) example(GQD.mcmc)