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 <https://nwfsc-timeseries.github.io/> See the NEWS file for update information.

Total

42,741

Last month

931

Last week

174

Average per day

31

Daily downloads

Total downloads

Description file content

Package
MARSS
Type
Package
Title
Multivariate Autoregressive State-Space Modeling
Version
3.10.10
Date
2018-10-30
Depends
R (>= 3.1.0)
Imports
graphics, grDevices, KFAS (>= 1.0.1), mvtnorm, nlme, stats, utils
Suggests
Formula, Hmisc, lattice, lme4, maps, stringr, survival, xtable, broom, ggplot2
Author
Eli Holmes, Eric Ward, Mark Scheuerell, and Kellie Wills, NOAA, Seattle, USA
Maintainer
Elizabeth Holmes - NOAA Federal
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 See the NEWS file for update information.
License
GPL-2
LazyData
yes
BuildVignettes
yes
ByteCompile
TRUE
URL
BugReports
https://github.com/nwfsc-timeseries/MARSS/issues
NeedsCompilation
no
Packaged
2018-11-02 04:09:55 UTC; eli.holmes
Repository
CRAN
Date/Publication
2018-11-02 05:20:03 UTC

install.packages('MARSS')

3.10.10

a month ago

https://nwfsc-timeseries.github.io/MARSS

Elizabeth Holmes - NOAA Federal

GPL-2

Depends on

R (>= 3.1.0)

Imports

graphics, grDevices, KFAS (>= 1.0.1), mvtnorm, nlme, stats, utils

Suggests

Formula, Hmisc, lattice, lme4, maps, stringr, survival, xtable, broom, ggplot2

Discussions