Description: Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.

Title: Multivariate Autoregressive State-Space Modeling
Author: Eli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA
Description: The MARSS package fits constrained and unconstrained
linear multivariate autoregressive state-space (MARSS) models

to multivariate time series data.  Type show.doc(MARSS, manual)
at the R command line to open the MARSS User Guide.
Packaged: 2010-10-18 23:09:27 UTC; xholmesel

Package: MARSS
Type: Package
Title: Multivariate Autoregressive State-Space Modeling
Version: 2.3
Date: 2011-7-31
Depends: MASS, mvtnorm, nlme, time, KFAS
Author: Eli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA
Maintainer: eli.holmes@noaa.gov <eli.holmes@noaa.gov>
Description: 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.
License: GPL-2
LazyLoad: yes
LazyData: yes
BuildVignettes: yes
Packaged: 2011-07-31 05:24:15 UTC; xholmesel
Repository: CRAN
Date/Publication: 2011-07-31 06:42:07

Package: MARSS
Type: Package
Title: Multivariate Autoregressive State-Space Modeling
Version: 2.4
Date: 2011-7-31
Depends: MASS, mvtnorm, nlme, time, KFAS
Author: Eli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA
Maintainer: eli.holmes@noaa.gov <eli.holmes@noaa.gov>
Description: 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.
License: GPL-2
LazyLoad: yes
LazyData: yes
BuildVignettes: yes
Packaged: 2011-07-31 18:06:27 UTC; xholmesel
Repository: CRAN
Date/Publication: 2011-08-01 05:13:23