SsfPack was recently reviewed in Journal of Statistical Software by Matteo M. Pelagatti.
SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of time series models in state space form. SsfPack provides functions for likelihood evaluation and signal extraction of arbitrary user specified linear Gaussian state space models, allowing the estimation of models ranging from simple time-invariant univariate forms to complicated time-varying multivariate specifications. Basic functions are available for prediction, moment smoothing and simulation smoothing. Additionally, functions are provided which put standard models such as autoregressive moving average (ARMA), unobserved components (UC) and cubic spline models in state space form.
The functions from SsfPack can be easily used for implementing, fitting and analysing linear Gaussian models relevant to many areas of econometrics, statistics and time series analysis. Further, SsfPack provides tools for estimating many non-Gaussian and nonlinear models using implement simulation based estimation methods such as importance sampling and Markov chain Monte Carlo (MCMC) methods.
SsfPack is primarily developed as a module for the object-oriented matrix programming language Ox. The library is written in C, which greatly improves execution speed compared to a direct Ox implementation. A free version of SsfPack for academic research and teaching purposes can be downloaded from this website.
The SsfPack website at http://www.ssfpack.com is designed and maintained by S.J. Koopman and K.M. Lee. Please send questions, comments and remarks about the website to firstname.lastname@example.org, with "ssfpack" in the subject line to avoid spam-filtering.