Fast Kalman Filtering Through Sequential Processing

Fast and flexible Kalman filtering implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter.


Reference manual

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0.1.2 by Thomas Aspinall, 6 months ago

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Authors: Thomas Aspinall [aut, cre] , Adrian Gepp [aut] , Geoff Harris [aut] , Simone Kelly [aut] , Colette Southam [aut] , Bruce Vanstone [aut] , David Luethi [ctb] , Philipp Erb [ctb] , Simon Otziger [ctb] , Paul Smith [ctb]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-3 license

Imports mathjaxr, Rdpack, curl

Suggests knitr, rmarkdown, stats, FKF, NFCP

Imported by NFCP.

See at CRAN