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Abstract
Support in <b>R</b> for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation smoothing and other functionality. This paper reviews some of the offerings in <b>R</b> to help the prospective user to make an informed choice.
Highlights
The Kalman filter is an important algorithm, for which relatively little support existed in R (R Development Core Team 2010) up until fairly recently
There are several packages available from the Comprehensive R Archive Network (CRAN) offering general Kalman filter capabilities, plus a number of functions scattered in other packages which cater to special models or problems
Sequential processing requires that the components of yt be uncorrelated or else that the “decorrelation” described just prior to equation (14) is carried out. This is done by the kf function which at each step of the Kalman filter iteration, checks for diagonality Ht
Summary
The Kalman filter is an important algorithm, for which relatively little support existed in R (R Development Core Team 2010) up until fairly recently. In the sequel we focus on linear Gaussian models and their estimation, which is what the packages we review offer in common (and the foundation on which most anything else rests). Other functionalities present in some of the packages examined include filtering and estimation of non-Gaussian models, simulation and disturbance smoothing and functions to help with the Bayesian analysis of dynamic linear models, etc. Other functionalities present in some of the packages examined include filtering and estimation of non-Gaussian models, simulation and disturbance smoothing and functions to help with the Bayesian analysis of dynamic linear models, etc. none of which are assessed
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