# FKF.SP

Fast and flexible Kalman filtering and smoothing implementation
utilizing sequential processing, designed for efficient parameter
estimation through maximum likelihood estimation or
expectation-maximization. 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.

## Installation

You can install the released version of FKF.SP from CRAN with:

`install.packages("FKF.SP")`

And the development version from GitHub with:

`devtools::install_github("TomAspinall/FKF.SP")`

which contains source code for the package starting with version
0.1.0.