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. 'FKF.SP' was built upon the existing 'FKF' package and was designed to generally increase the computational efficiency of Kalman filtering when independence is assumed in the measurement error of observations. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8).

Version: 0.1.0
Imports: mathjaxr, Rdpack, curl
Suggests: knitr, rmarkdown, stats, FKF
Published: 2020-12-18
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
CRAN checks: FKF.SP results

Downloads:

Reference manual: FKF.SP.pdf
Vignettes: Fast Kalman Filtering using Sequential Processing
Package source: FKF.SP_0.1.0.tar.gz
Windows binaries: r-devel: FKF.SP_0.1.0.zip, r-release: FKF.SP_0.1.0.zip, r-oldrel: FKF.SP_0.1.0.zip
macOS binaries: r-release: FKF.SP_0.1.0.tgz, r-oldrel: FKF.SP_0.1.0.tgz

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