Add to ORKGSupport in R 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 R to help the prospective user to make an informed choice.Partial support from grants ECO2008-05622 (MCyT) and IT-347-10 (Basque Government
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
This article presents a robust augmented Kalman filter that extends the data cleaning filter (Masrel...
We give an overview of some of the software tools available in R, either as built- in functions or c...
We give an overview of some of the software tools available in R, either as built- in functions or c...
This paper uses several examples to show how the econometrics program RATS can be used to analyze st...
In this paper the Kalman filter and regression approaches for estimating linear state space models a...
AbstractThe paper reviews and generalizes recent filtering and smoothing algorithms for observations...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
We describe an R package focused on Bayesian analysis of dynamic linear models. The main features of...
This thesis illustrates two approaches for the evaluation of forecasting, filtering and smoothing f...
In the Bachelor’s thesis we describe the Kalman filtering algorithm for linear-Gaussian state space...
Kalman filter is one of the best filter utilized as a part of the state estimation taking into accou...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
This article presents a robust augmented Kalman filter that extends the data cleaning filter (Masrel...
We give an overview of some of the software tools available in R, either as built- in functions or c...
We give an overview of some of the software tools available in R, either as built- in functions or c...
This paper uses several examples to show how the econometrics program RATS can be used to analyze st...
In this paper the Kalman filter and regression approaches for estimating linear state space models a...
AbstractThe paper reviews and generalizes recent filtering and smoothing algorithms for observations...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
We describe an R package focused on Bayesian analysis of dynamic linear models. The main features of...
This thesis illustrates two approaches for the evaluation of forecasting, filtering and smoothing f...
In the Bachelor’s thesis we describe the Kalman filtering algorithm for linear-Gaussian state space...
Kalman filter is one of the best filter utilized as a part of the state estimation taking into accou...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
The research is interested in studying a modern mathematical topic of great importance in contempora...
This article presents a robust augmented Kalman filter that extends the data cleaning filter (Masrel...