Description: Fast and flexible Kalman filtering implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter.
Suggests: knitr, rmarkdown, stats, FKF, NFCP
Packaged: 2021-01-29 06:25:37 UTC; Thomas Aspinall

Packaged: 2021-05-04 06:49:45 UTC; Thomas Aspinall

Packaged: 2021-11-12 10:10:01 UTC; Thomas Aspinall

Packaged: 2022-03-21 09:58:47 UTC; Thomas Aspinall

Description: Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter/smoother.
Packaged: 2022-10-05 08:42:32 UTC; Thomas Aspinall

Packaged: 2022-10-09 08:01:10 UTC; Thomas Aspinall

Maintainer: David P. Hofmeyr <dhofmeyr@sun.ac.za>
Description: Implements the method of Hofmeyr, D.P. (2019) <10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations.
Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods
for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected