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 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: 1.2.5
Depends: R (≥ 3.1.0)
Imports: stats
Suggests: MASS, testthat, knitr, lme4
Published: 2016-12-23
Author: Jouni Helske
Maintainer: Jouni Helske <jouni.helske at jyu.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


Reference manual: KFAS.pdf
Vignettes: KFAS: Exponential Family State Space Models in R
Package source: KFAS_1.2.5.tar.gz
Windows binaries: r-devel: KFAS_1.2.5.zip, r-release: KFAS_1.2.5.zip, r-oldrel: KFAS_1.2.5.zip
OS X Mavericks binaries: r-release: KFAS_1.2.5.tgz, r-oldrel: KFAS_1.2.5.tgz
Old sources: KFAS archive

Reverse dependencies:

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


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