Inference and Analysis for Jump Generalized Quadratic Diffusions

Tools for performing inference and analysis on a class of quadratic jump diffusion processes.


Inference and Analysis for Generalized Quadratic Jump Diffusions

What is DiffusionRjgqd?

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

Why use DiffusionRjgqd?

DiffusionRjgqd 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 DiffusionRjgqd?

Check out DiffusionRjgqd for the package source files, vignettes and other downloadable content or visit the DiffusionRjgqd 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 = 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("~/DiffusionRgqd_0.1.1.tar.gz", repos = NULL, type = "source”)

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

library(DiffpackRjgqd) 
example(JGQD.density)
example(JGQD.mcmc)

News

DiffusionRjgqd - NEWS

DiffusionRjgqd is available on CRAN and GitHub.

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

v0.1.0

Initial version of DiffusionRjgqd uploaded to CRAN and GitHub.

v0.1.1

Minor update of DiffusionRjgqd.

  • Improvements and fixes for minor issues in some functions.
  • Improvements and typos fixed in Vignettes.
  • Added decoding feature for jump detection.
  • Improved error handling.
  • Fixed bottleneck which caused slower run times for time-inhomogeneous models under MCMC.
  • Updated Methodology Paper and Replication materials.
  • Updated references.
  • Modified some datasets.

Reference manual

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install.packages("DiffusionRjgqd")

0.1.1 by Etienne A.D. Pienaar, 2 years ago


https://github.com/eta21


Report a bug at https://github.com/eta21/DiffusionRjgqd/issues


Browse source code at https://github.com/cran/DiffusionRjgqd


Authors: Etienne A.D. Pienaar [aut, cre] , Melvin M. Varughese [ctb]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp, RcppArmadillo, rgl, colorspace

Suggests DiffusionRgqd, knitr, coda, Quandl, R.rsp

Linking to Rcpp, RcppArmadillo


Suggested by DiffusionRimp.


See at CRAN