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.