MARSS: Multivariate Autoregressive State-Space Modeling

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data. Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary 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. Online workshops (lectures and computer labs) at http://faculty.washington.edu/eeholmes/workshops.shtml See the NEWS file for update information.

Version: 3.9
Depends: R (≥ 2.15.0)
Imports: nlme, mvtnorm, KFAS (≥ 1.0.1), stats, utils, graphics
Suggests: Hmisc, maps, xtable, stringr
Published: 2014-03-21
Author: Eli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA
Maintainer: Elizabeth Holmes - NOAA Federal <eli.holmes at noaa.gov>
License: GPL-2
NeedsCompilation: no
Citation: MARSS citation info
Materials: NEWS
In views: TimeSeries
CRAN checks: MARSS results

Downloads:

Reference manual: MARSS.pdf
Vignettes: EM Derivation
Quick Start Guide
User Guide
Package source: MARSS_3.9.tar.gz
OS X binary: MARSS_3.9.tgz
Windows binary: MARSS_3.9.zip
Old sources: MARSS archive

Reverse dependencies:

Reverse suggests: MAR1