Kalman Filter and Smoother for Exponential Family State Space Models

State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) for details.


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Reference manual

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install.packages("KFAS")

1.3.4 by Jouni Helske, 16 days ago


Report a bug at https://github.com/helske/KFAS/issues


Browse source code at https://github.com/cran/KFAS


Authors: Jouni Helske [aut, cre]


Documentation:   PDF Manual  


Task views: Time Series Analysis


GPL (>= 2) license


Imports stats

Suggests knitr, lme4, MASS, Matrix, testthat


Imported by MARSS, TSPred, networkTomography, partialAR, partialCI, sarima, tsPI, walker.

Depended on by rucm.

Suggested by KFKSDS, bssm, ggfortify, tscount.


See at CRAN