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: | 1.3.1 |
Depends: | R (≥ 3.1.0) |
Imports: | stats |
Suggests: | MASS, testthat, knitr, lme4 |
Published: | 2018-01-04 |
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 |
Reference manual: | KFAS.pdf |
Vignettes: |
KFAS: Exponential Family State Space Models in R |
Package source: | KFAS_1.3.1.tar.gz |
Windows binaries: | r-devel: KFAS_1.3.1.zip, r-release: KFAS_1.3.1.zip, r-oldrel: KFAS_1.3.1.zip |
OS X El Capitan binaries: | r-release: KFAS_1.3.1.tgz |
OS X Mavericks binaries: | r-oldrel: KFAS_1.3.1.tgz |
Old sources: | KFAS archive |
Reverse depends: | rucm |
Reverse imports: | dcmr, dlmodeler, MARSS, networkTomography, tsPI, TSPred, walker |
Reverse suggests: | bssm, ggfortify, KFKSDS, tscount |
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