pomp 

I have some contribution to pomp, a tool for working with partially observed Markov processes developed mainly by Aaron King. It provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a platform for the implementation of new inference methods.

is2

I am actively working on an R package called iterated smoothing is2.
With my adviser Edward Ionides, I have written an R package that implement  three second-order iterated smoothing algorithms, exploiting efficiently estimation of the observed information matrix to increase convergence rate. Inherited from R package pompis2 can virtually be applied to any model developed by pomp
Since it is on Rforge, it is straightforward to install from R using  install.packages("is2", repos="http://R-Forge.R-project.org")
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pis2

Motivated by the favorable theoretical properties of particle Markov Chain Monte Carlo methods, I implement second-order particle iterated smoothing, which can improve the estimations in term of i) shortening the burn-in period, (ii) accelerating the mixing of the Markov chain at the stationary period, and (iii) simplifying tuning. It is in my github repository so you can download and install it from the source.



RcppSQMC


Sequential Quasi-Monte Carlo was originally developed by Mathew Gerber and Nicolas Chopin. Its theory sound promising. Currently, I find no R package that do quasi-Monte Carlo sampling and using C and R codes of Mathew Gerber and Nicolas Chopin is not straightforward. Therefore, I wrap the original code by Rcpp and make the interface with R easier. I intend to develop some iterated filtering and smoothing algorithms based on quasi-Monte Carlo sampling instead of Monte Carlo sampling. You can find the code on my github repository.