FKF.SP: 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.

Version: 0.1.2
Imports: mathjaxr, Rdpack, curl
Suggests: knitr, rmarkdown, stats, FKF, NFCP
Published: 2021-05-04
Author: Thomas Aspinall ORCID iD [aut, cre], Adrian Gepp ORCID iD [aut], Geoff Harris ORCID iD [aut], Simone Kelly ORCID iD [aut], Colette Southam ORCID iD [aut], Bruce Vanstone ORCID iD [aut], David Luethi [ctb], Philipp Erb [ctb], Simon Otziger [ctb], Paul Smith ORCID iD [ctb]
Maintainer: Thomas Aspinall <tomaspinall2512 at gmail.com>
BugReports: https://github.com/TomAspinall/FKF.SP/issues
License: GPL-3
URL: https://github.com/TomAspinall/FKF.SP
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: FKF.SP results

Downloads:

Reference manual: FKF.SP.pdf
Vignettes: Fast Kalman Filtering using Sequential Processing
Package source: FKF.SP_0.1.2.tar.gz
Windows binaries: r-devel: FKF.SP_0.1.2.zip, r-release: FKF.SP_0.1.2.zip, r-oldrel: FKF.SP_0.1.2.zip
macOS binaries: r-release: FKF.SP_0.1.2.tgz, r-oldrel: FKF.SP_0.1.2.tgz
Old sources: FKF.SP archive

Reverse dependencies:

Reverse imports: NFCP

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