KFAS: Kalman filter and smoothers for exponential family state space models

Package KFAS provides functions for Kalman filtering, state, disturbance and simulation smoothing, forecasting and simulation of state space models. All functions can use exact diffuse initialisation when distributions of some or all elements of initial state vector are unknown. Filtering, state smoothing and simulation functions use sequential processing algorithm, which is faster than standard approach, and it also allows singularity of prediction error variance matrix. KFAS also contains function for computing the likelihood of exponential family state space models and function for state smoothing of exponential family state space models.

Version: 0.6.1
Depends: R (≥ 2.8.0)
Published: 2011-02-23
Author: Jouni Helske
Maintainer: Jouni Helske <jouni.helske at jyu.fi>
License: GPL (≥ 2)
In views: TimeSeries
CRAN checks: KFAS results

Downloads:

Package source: KFAS_0.6.1.tar.gz
MacOS X binary: KFAS_0.6.1.tgz
Windows binary: KFAS_0.6.1.zip
Reference manual: KFAS.pdf
Old sources: KFAS archive

Reverse dependencies:

Reverse depends: MARSS
Reverse suggests: dlmodeler