FKF.SP: Fast Kalman Filtering Through Sequential Processing

Fast and flexible Kalman filtering and smoothing 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/smoother.

Version:	0.3.3
Imports:	mathjaxr
Suggests:	knitr, rmarkdown, stats, FKF, NFCP
Published:	2024-10-30
DOI:	10.32614/CRAN.package.FKF.SP
Author:	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]
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

Documentation:

Reference manual:	FKF.SP.pdf
Vignettes:	Fast Kalman Filtering using Sequential Processing (source, R code)

Downloads:

Package source:	FKF.SP_0.3.3.tar.gz
Windows binaries:	r-devel: FKF.SP_0.3.3.zip, r-release: FKF.SP_0.3.3.zip, r-oldrel: FKF.SP_0.3.3.zip
macOS binaries:	r-release (arm64): FKF.SP_0.3.3.tgz, r-oldrel (arm64): FKF.SP_0.3.3.tgz, r-release (x86_64): FKF.SP_0.3.3.tgz, r-oldrel (x86_64): FKF.SP_0.3.3.tgz
Old sources:	FKF.SP archive

Reverse dependencies:

Reverse imports:

NFCP

Linking:

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