MARSS (2.4)

Multivariate Autoregressive State-Space Modeling.

The MARSS package fits constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models to multivariate time series data via priamarily an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided. Functions are provided for parametric and innovations bootstrapping, Kalman filter and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the hessian approximation and via bootstrapping and calculation of auxilliary residuals for detecting outliers and shocks. The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates. Type RShowDoc("UserGuide", package="MARSS") at the R command line to open the MARSS user guide.

Maintainer: Unknown
Author(s): Eli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA

License: GPL-2

Uses: KFAS, MASS, mvtnorm, nlme, time
Reverse suggests: bayesdfa, freqdom, freqdom.fda, MAR1

Released about 8 years ago.