KFAS: Kalman Filter and Smoother for Exponential Family State Space Models

State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. KFAS (Helske 2017) <doi:10.18637/jss.v078.i10> includes fast functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions.

Version: 1.2.9
Depends: R (≥ 3.1.0)
Imports: stats
Suggests: MASS, testthat, knitr, lme4
Published: 2017-08-21
Author: Jouni Helske
Maintainer: Jouni Helske <jouni.helske at iki.fi>
BugReports: https://github.com/helske/KFAS/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: KFAS citation info
Materials: ChangeLog
In views: TimeSeries
CRAN checks: KFAS results

Downloads:

Reference manual: KFAS.pdf
Vignettes: KFAS: Exponential Family State Space Models in R
Package source: KFAS_1.2.9.tar.gz
Windows binaries: r-devel: KFAS_1.2.9.zip, r-release: KFAS_1.2.9.zip, r-oldrel: KFAS_1.2.9.zip
OS X El Capitan binaries: r-release: KFAS_1.2.9.tgz
OS X Mavericks binaries: r-oldrel: KFAS_1.2.9.tgz
Old sources: KFAS archive

Reverse dependencies:

Reverse depends: rucm
Reverse imports: dcmr, dlmodeler, MARSS, networkTomography, tsPI, TSPred, walker
Reverse suggests: bssm, ggfortify, KFKSDS, tscount

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