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

State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient 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. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

Version: 1.5.1
Depends: R (≥ 3.1.0)
Imports: stats
Suggests: knitr, lme4, MASS, Matrix, testthat
Published: 2023-09-05
DOI: 10.32614/CRAN.package.KFAS
Author: Jouni Helske ORCID iD [aut, cre]
Maintainer: Jouni Helske <jouni.helske at iki.fi>
BugReports: https://github.com/helske/KFAS/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/helske/KFAS
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_1.5.1.tar.gz
Windows binaries: r-devel: KFAS_1.5.1.zip, r-release: KFAS_1.5.1.zip, r-oldrel: KFAS_1.5.1.zip
macOS binaries: r-release (arm64): KFAS_1.5.1.tgz, r-oldrel (arm64): KFAS_1.5.1.tgz, r-release (x86_64): KFAS_1.5.1.tgz, r-oldrel (x86_64): KFAS_1.5.1.tgz
Old sources: KFAS archive

Reverse dependencies:

Reverse depends: CausalMBSTS, rucm
Reverse imports: countSTAR, MARSS, mbsts, RGAP, sectorgap, tsPI, TSPred, tspredit, walker
Reverse suggests: bssm, ggfortify, sarima


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