Package: MARSS
Type: Package
Title: Multivariate Autoregressive State-Space Modeling
Version: 3.4
Date: 2013-2-11
Depends: R (>= 2.15.0), MASS, mvtnorm, nlme, KFAS (>= 0.9.11)
Suggests: Hmisc, maps, xtable
Author: Eli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA
Maintainer: Elizabeth Holmes - NOAA Federal <eli.holmes@noaa.gov>
Description: 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 (lecture material
and labs) at
http://faculty.washington.edu/eeholmes/workshops.shtml
License: GPL-2
LazyData: yes
BuildVignettes: yes
Packaged: 2013-02-16 19:40:05 UTC; eli.holmes
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-02-18 17:01:48

Imports: nlme, mvtnorm, KFAS (>= 0.9.11), stats, utils, graphics
Description: 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