Description: This is a flexible implementation of the Kalman filter. It
is entirely written in C and relies fully on linear algebra
subroutines contained in BLAS and LAPACK. Due to the speed of
the filter, the fitting of high-dimensional linear state space
models to large datasets becomes possible. This package also
contains a plot function for the visualization of the state
vector and graphical diagnostics of the residuals.

Author: David Luethi, Philipp Erb, Simon Otziger
Description: This is a fast and flexible implementation of the Kalman
filter, which can deal with NAs. It is entirely written in C
and relies fully on linear algebra subroutines contained in
BLAS and LAPACK. Due to the speed of the filter, the fitting of
high-dimensional linear state space models to large datasets
becomes possible. This package also contains a plot function
for the visualization of the state vector and graphical

Packaged: 2014-02-10 15:38:44 UTC; aleksandarspasojevic
X-CRAN-Comment: Orphaned on 2018-02-11 as email address bounced when

Authors@R: c(person(given = "David", family="Luethi", role=c("aut")),
person(given = "Philipp", family="Erb", role=c("aut")),
person(given = "Simon", family="Otziger", role=c("aut")),
Suggests: RUnit, knitr, rmarkdown, covr, bookdown

Suggests: RUnit, knitr, rmarkdown, covr, pkgdown

filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in
Suggests: knitr, rmarkdown, covr, pkgdown, testthat (>= 3.0.0)

Title: Fast Kalman Filtering Through Sequential Processing
Authors@R: c(person("Thomas", "Aspinall", email = "tomaspinall2512@gmail.com",
person("Adrian", "Gepp", email = "adgepp@bond.edu.au",
person("Geoff", "Harris", email = "gharris@bond.edu.au",
person("Simone", "Kelly", email = "skelly@bond.edu.au",
person("Colette", "Southam", email = "csoutham@bond.edu.au",
person("Bruce", "Vanstone", email = "bvanston@bond.edu.au",
person(given = "David", family="Luethi", role=c("ctb")),
person(given = "Philipp", family="Erb", role=c("ctb")),
person(given = "Simon", family="Otziger", role=c("ctb")),
person(given = "Paul", family = "Smith", role = c("ctb"),
Description: 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).
Packaged: 2020-12-16 23:39:50 UTC; Thomas Aspinall
Author: Thomas Aspinall [aut, cre] (<https://orcid.org/0000-0002-6968-1989>),