sspir: State Space Models in R
In ClausDethlefsen/sspir: State Space Models in R

Description What sspir does not include Other software Author(s) See Also

The main contribution of this package is to give a formula language for specifying dynamic generalized linear models. That is, an extension of glm formulae by marking terms with tvar to specify that their coefficients are time-varying. The package also provides (extended) Kalman filter and Kalman smoother for models within Gaussian, Poisson and binomial families. To get started, try demo(gas), demo(vandrivers) and demo(mumps).

Also, read the paper "C. Dethlefsen and S. Lundbye-Christensen. Formulating state space models in r with focus on longitudinal regression models. Journal of Statistical Software, 16(1), 2006."

Optimization To keep full generality, the Kalman filter and smoother use the SS functions Fmat, Gmat, Vmat, Wmat. The special cases where these matrices are time-invariant, the algorithms can be considerably speeded up by implementing these special cases eg. in C. For simple models, see StructTS.
Diffuse initialization We use m_0 and C_0 as initialization of the state process. This may cause numeric problems which may be solved using diffuse initialization (see Durbin and Koopman (2000)).
ARIMA models These are not directly supported, but since they can be expressed as state space models, it is possible to specify them as SS objects.
Importance sampling We use iterated extended Kalman smoothing and use the likelihood from the approximating Gaussian state space model. This may be improved using importance sampling.
Multivariate observations We have plans including methods for combining ssm objects for different time-series so that they can possibly share components in the latent process.
Multi-process models We have plans for including methods for combining ssm objects defining different models for the same time-series. Providing prior probabilities for these models, it is possible to calculate posterior probabilites for the models, thereby discriminating between the models.
Markov chain Monte Carlo A state space model may be part of a hierarchical model giving priors on hyper-parameters. Inference may be done using MCMC methods and for the state space model, the Forwards filtering, Backwards sampling method may be used. We plan to include at least some support for this.
Missing values Missing values are not allowed in the covariates.
FamilyCurrently, only three combinations of distribution/link functions are supported: Gaussian/identity, Poisson/log, Binomial/logit. To add new variations, edit the function getFamily

Ox/SsfPack SsfPack is a package for Ox by SJ Koopman, N Shephard and JA Doornik. Multivariate Gaussian state space models with some support for non-Gaussian models. http://www.ssfpack.com/. Closely related to S+FinMetrics, a package for S-Plus, developed by Insightful. http://www.insightful.com/products/finmetrics/default.asp.
StructTS StructTS by BD Ripley is a part of R. Univariate Gaussian state space models of the class structural time series models.
KEE Kalman Estimating Equations is a package for S-Plus by SJ Knudsen. Poisson-Tweedie log-linear State-Space model. http://www.statdem.sdu.dk/~sjk/kee/index.html.
IEKS IEKS library is a package for S-Plus and R by B Klein. Implementation of the iterated extended Kalman filter and smoother to discrete data from an exponential family. The latent process is assumed to be linear and Gaussian. http://genetics.agrsci.dk/~bmk/.
Matlab Kalman filter toolbox for Matlab by K Murphy. http://www.ai.mit.edu/~murphyk/Software/Kalman/kalman.html.
Bats BATS software by A Pole, M West and J Harrison. http://www.isds.duke.edu/~mw/bats.html.
dse Dynamic Systems Estimation library (dse1 and dse2) are packages for R by P Gilbert. Multivariate Gaussian state space models with focus on ARMA models. See http://www.bank-banque-canada.ca/pgilbert.
CTSM Continuous Time Stochastic Modelling (CTSM) is a stand alone program by NR Kristensen, LE Christiansen and H Madsen. Stochastic differential equations, linear and non-linear Gaussian state space models. http://www.imm.dtu.dk/ctsm/.