kalmanfs: Kalman Filtering and Smoothing
In vsartor/bnsa: Bayesian Non-Structured Analysis
Description
Usage
Arguments
Value
Description
Implements a basic Kalman filter and smoother for dynamic linear models,
considering vague priors.
Usage
1
kalmanfs(Y, Ft, Gt, Vt, Wt)
Arguments
Y
A T by m matrix containing T observations of an m-variate series.
Ft
The observational matrix.
Gt
The evolutional matrix.
Vt
The observational covariance matrix.
Wt
The evolutional covariance matrix.
Value
A list with posterior means 's' and covariances 'S'.
vsartor/bnsa documentation built on May 17, 2019, 12:04 p.m.
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Description Usage Arguments Value
Description
Implements a basic Kalman filter and smoother for dynamic linear models, considering vague priors.
Usage
1 | kalmanfs(Y, Ft, Gt, Vt, Wt)
|
Arguments
Y |
A T by m matrix containing T observations of an m-variate series. |
Ft |
The observational matrix. |
Gt |
The evolutional matrix. |
Vt |
The observational covariance matrix. |
Wt |
The evolutional covariance matrix. |
Value
A list with posterior means 's' and covariances 'S'.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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