gragusa/MARSS: Multivariate Autoregressive State-Space Modeling
Version 3.9

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.

Getting started

Package details

AuthorEli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA
Date of publication2014-03-21 01:22:51
MaintainerElizabeth Holmes - NOAA Federal <[email protected]>
LicenseGPL-2
Version3.9
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("gragusa/MARSS")
gragusa/MARSS documentation built on May 14, 2017, 3:01 p.m.