KFAS: R Package for Exponential Family State Space Models

Package KFAS provides tools for modelling exponential family state space models such as structural time series, ARIMA models, generalized linear models and generalized linear mixed models.

Paper at JSS

If you use KFAS in your paper, please cite it properly, see citation("KFAS") in R, or above link to the paper.

See also bssm package if your are more interested in Bayesian inference on state space models.

Main features

• Kalman filtering
• Fixed interval smoothing (Kalman smoothing)
• Simulation smoothing of Gaussian models
• Importance sampling based inference of non-Gaussian models
• Exact diffuse initialization
• Sequential processing (univariate treatment of multivariate models)
• Multivariate models with mixed distributions

Most of the algorithms are based on book "Time Series Analysis by State Space Methods" and related articles by J. Durbin and S.J. Koopman.

KFAS is available at CRAN. You can install the latest development version from the GitHub using the devtools package:

install.packages("devtools")
library(devtools)
install_github("helske/KFAS")

See

• help(KFAS) in R for examples, and many more examples under different functions, as well as the Paper at JSS.
• ChangeLog for changes.