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.


Reference manual

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1.4.4 by Jouni Helske, a month 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 CausalMBSTS, mbsts, rucm.

Suggested by KFKSDS, bssm, ggfortify.

See at CRAN