I am looking for a simple code example of how to run a Particle Filter in R. The pomp package appears to support the state space math bit, but the examples are a little tricky to follow programmatically for a simple OO developer such as myself, particularly how to load the observed data into a pomp object.

Lets say I have a csv file with 1 column of noisy data as input, and I would like to run it through a Particle Filter in order to hopefully clean it up, with the output being the estimations, to another csv file.

` y <- read.csv("C:/Dev/VeryCleverStatArb/inputData.csv", header=FALSE) #CSV to Pomp object ??? #Run Particle Filter #Write estimates to csv. `

The main difficulty with the examples is loading csv data into a pomp object.

A very simple state space model should be good enough for now.

Any ideas for the R-curious?

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#### Best Answer

EDIT: It seems that most particle filter packages are gone now. However, I have been playing with LaplacesDemon (a Bayesian MCMC package) and it does have the PMC (Population Monte Carlo) function which implements PMC, which is a type of particle filter. Maybe too much machinery for a quick particle filter kind of thing, but a package well worth learning.

You can find package and tutorials at CRAN.

ORIGINAL: To be honest, in the simplest case, `pomp`

is hard to use. It's very flexible for anything you might want to do, but it's like using a space ship to go to the grocery store.

Have you tried looking at Kalman filters (if your data might satisfy assumptions of the Kalman filter), including base functions `tsSmooth`

and `StructTS`

(univariate only), and package `dlm`

? I'd also take a look at `loess`

and other smoothers.

I *hope* I'm wrong and someone hops on here with a quick, "Here's how to do it for simple univariate data such as you have with some modest assumptions." I'd love to be able to use the package myself.

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