Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 4285
Title: MARSS: multivariate autoregressive state-space models for analyzing time-series data
Author: E. E. Holmes, E. J. Ward, Kellie Wills
Publication Year: 2012
Journal: R Journal
Volume: 4
Issue: 1
Pages: 11-19
Keywords: multivariate,interactions,community ecology,time series
Abstract:

 MARSS is a package for fitting multivariate autoregressive state-space models to time-series data. The MARSS package implements state-space models in a maximum likelihood (ML) framework. The core functionality of MARSS is based on likelihood maximization using the Kalman filter/smoother, combined with an EM algorithm. To make comparisons with other packages available, parameter estimation is also permitted via direct search routines available in 'optim'. The MARSS package allows data to contain missing values and allows a wide variety of model structures and constraints to be specified (such as fixed or shared parameters). In addition to model-fitting, the package provides bootstrapping routines for simulating data and generating confidence intervals, and multiple options for calculating model selection criteria (such as AIC).

URL1: The next link will exit from NWFSC web site http://journal.r-project.org/archive/2012-1/RJournal_2012-1_Holmes~et~al.pdf
Theme: Ecosystem Approach to Management for the California Current Large Marine Ecosystem
Foci: Characterize linkages between climatic conditions and biotic responses.
Characterize ecological interactions (e.g. predation, competition, parasitism, disease, etc.) within and among species to support ecosystem approach to management.