MARSS-package: Multivariate Autoregressive State-Space Model Estimation
Description
The MARSS package fits constrained and unconstrained multivariate autoregressive time-series models to multivariate time series data. To open the user guide from the command line, type RShowDoc("UserGuide",package="MARSS"). To open an overview page with package information, type RShowDoc("index",package="MARSS").
The MARSS model is
[object Object],[object Object],[object Object]
The parameters, hidden state processes (x), and observations (y) are matrices:
- x(t) is m x 1
- y(t) is n x 1 (m<=n)< li="">
- Z is n x m
- B is m x m
- U is m x 1
- Q is m x m
- A is n x 1
- R is n x n
- x0 is m x 1
- V0 is m x m
The package functions estimate the model parameters using an EM algorithm (primarily but see MARSSoptim). Parameters may be constrained to have shared elements (elements which are constrained to have the same value) or fixed elements (with the other elements estimated). The states and smoothed state estimates are provided via a Kalman filter and smoother. Bootstrapping, confidence interval estimation, bias estimation, model selection and simulation functions are provided. The main user interface to the package is the top-level function MARSS.Details
Important MARSS functions:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]References
The MARSS user guide: Holmes, E. E., E. J. Ward, and M. D. Scheuerell (2012) Analysis of multivariate time-series using the MARSS package. NOAA Fisheries, Northwest Fisheries Science
Center, 2725 Montlake Blvd E., Seattle, WA 98112 Type RShowDoc("UserGuide",package="MARSS") to open a copy.
Type RShowDoc("index",package="MARSS") to see all the package documentation, tutorials, and case study scripts.