Package: KFAS
Version: 0.6.1
Date: 2011-02-23
Title: Kalman filter and smoothers for exponential family state space
models.
Author: Jouni Helske <jouni.helske@jyu.fi>
Maintainer: Jouni Helske <jouni.helske@jyu.fi>
Depends: R (>= 2.8.0)
Description: 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.
License: GPL (>= 2)
Packaged: 2011-02-23 14:48:04 UTC; jovetale
Repository: CRAN
Date/Publication: 2011-02-23 16:18:39

Title: Kalman Filter and Smoother for Exponential Family State Space
smoothing, forecasting and simulation of Gaussian, Poisson and
Binomial state space models with exact diffuse initialization

Collate: 'KFAS-package.R' 'KFS.R' 'SSModel.R' 'approxSSM.R'

Description: Package KFAS provides functions for Kalman filtering, smoothing,
forecasting and simulation of multivariate exponential family state space
models with exact diffuse initialization when distributions of some or all
elements of initial state vector are unknown.

Description: Functions for Kalman filtering, smoothing,
models with exact diffuse initialization and sequential processing.

Description: 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