MARSS stands for Multivariate Auto-Regressive(1) State-Space. The MARSS R package estimates the parameters of linear MARSS models with Gaussian errors. This class of model is extremely important in the study of linear stochastic dynamical systems, and these models are important in many different fields, including economics, engineering, genetics, physics and ecology. The model class has different names in different fields, for example in some fields they are termed dynamic linear models (DLMs) or vector autoregressive (VAR) state-space models. The MARSS package allows you to easily fit time-varying constrained and unconstrained MARSS models with or without covariates via maximum-likelihood using an EM algorithm or BFGS. Fast fitting with TMB is available with the companion package marssTMB.

cran version github rstudio mirror downloads


To install MARSS from CRAN:


The latest release on GitHub may be ahead of the CRAN release. To install the latest release on GitHub. You can install from our r-universe repository:

install.packages('MARSS', repos = c('', ''))

or install from GitHub

install.packages("remotes") # if needed

To install an R package from GitHub, you need to be able to build an R package on your machine. If you are on Windows, that means you may need to install Rtools. In more recent versions of R, it seems like the Rtools dependency for Windows users has been removed, so try installing. If you get an error about no gcc installation, it means you need Rtools. On a Mac, installation should work fine; you do not need to install anything.

If you are on a Windows machine and get an error saying ‘loading failed for i386’ or similar, then try

options(devtools.install.args = "--no-multiarch")

If R asks you to update packages, and then proceeds to fail at installation because of a warning that a package was built under a later R version than you have on your computer, use






If you use MARSS results in publications, please cite the primary citation:

Holmes, E. E., Ward, E. J. and Wills, K. (2012) MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data. The R Journal. 4(1):11-19

You can also cite the package and user guide:

Elizabeth E. Holmes, Eric J. Ward, Mark D. Scheuerell and Kellie Wills (2020). MARSS: Multivariate Autoregressive State-Space Modeling. R package version 3.11.4.

Holmes, E. E., M. D. Scheuerell, and E. J. Ward (“, year,”) Analysis of multivariate time-series using the MARSS package. Version “, meta$Version,”. NOAA Fisheries, Northwest Fisheries Science Center, 2725 Montlake Blvd E., Seattle, WA 98112, DOI: 10.5281/zenodo.5781847

Type citation("MARSS") at the command line to get the most up to data citations.


To see our publications using MARSS models, see the Applied Time Series Analysis website.


See inst/ for instructions on creating a release from the repository.


The MARSS package as a whole is distributed under GPL-3 (GNU GENERAL PUBLIC LICENSE version 3).

In addition this software has the following license addendum:

Software code created by U.S. Government employees is not subject to copyright in the United States (17 U.S.C. §105). The United State s/Department of Commerce reserve all rights to seek and obtain copyright protection in countries other than the United States for Software authored in its entirety by the Department of Commerce. To this end, the Department of Commerce hereby grants to Recipient a royalty-free, nonexclusive license to use, copy, and create derivative works of the Software outside of the United States.

NOAA Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.