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.

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Description file content

Package
KFAS
Version
1.3.7
Date
2019-06-10
Title
Kalman Filter and Smoother for Exponential Family State Space Models
Depends
R (>= 3.1.0)
Imports
stats
Suggests
knitr, lme4, MASS, Matrix, testthat
Description
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) for details.
License
GPL (>= 2)
BugReports
https://github.com/helske/KFAS/issues
VignetteBuilder
knitr
RoxygenNote
6.1.1
Encoding
UTF-8
ByteCompile
true
NeedsCompilation
yes
Packaged
2019-06-10 07:06:40 UTC; jouhe21
Author
Jouni Helske [aut, cre] ()
Maintainer
Jouni Helske
Repository
CRAN
Date/Publication
2019-06-10 08:50:03 UTC

install.packages('KFAS')

1.3.7

2 months ago

Jouni Helske

GPL (>= 2)

Depends on

R (>= 3.1.0)

Imports

stats

Suggests

knitr, lme4, MASS, Matrix, testthat

Discussions