Sequential Monte Carlo / Particle Filter is a (to quote the Wikipedia page I just linked to) sophisticated model estimation technique based on simulation. They are related to both Kalman Filters, and Markov Chain Monte Carlo methods.

Adam Johansen has a rather nice set of C++ classes documentated in his 2009 paper in the Journal of Statistical Software (JSS). I started to play with these classes and realized that, once again, this would make perfect sense in an R extension built with the Rcpp package by Romain and myself (and in JSS too). So I put a first prototype onto R-Forge and emailed Adam who, to my pleasant surprise, was quite interested. And a couple of emails, and commits later, we are happy to present a very first release 0.1.0.

I wrote a few words on a RcppSMC page on my website where you can find a few more details. But in short, we already have example functions demonstrating the backend classes by reproducing examples from

Johansen (2009)

and his example 5.1 via pfLineartBS() for a linear bootstrap example;

Doucet, Briers and Senecal (2006)

and their (optimal) block-sampling particle filter for a linear Gaussian model (serving as an illustration as the setup does of course have an analytical solution) via the function blockpfGaussianOpt()

Gordon, Salmond and Smith (1993)

and their ubiqitous nonlinear state space model via the function pfNonlinBS().

And to illustrate just why Rcpp is so cool for this, here is a little animation of a callback from the C++ code when doing the filtering on Adam’s example 5.1. By passing a simple plotting function, written in R, to the C++ code, we can get a plot updated on every iteration. Here I cheated a little and used our old plot function with fixed ranges, the package now uses a more general function:

The animation is of course due to ImageMagick glueing one hundred files into a single animated gif.

More information about RcppSMC is on its page, and we intend to add more examples and extensions over time.