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Multivariate Autoregressive State-Space Modeling with R

MARSS stands for Multivariate Auto-Regressive(1) State-Space. The MARSS package is an R package for estimating 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 to multivariate time-series data via maximum-likelihood using primarily an EM algorithm.

cran version github rstudio mirror downloads License: CC0-1.0


To install MARSS from CRAN:


The latest release on GitHub may be ahead of the CRAN release. To install the latest release on GitHub:

install.packages("devtools") # if needed

The master branch on GitHub has work leading up to a GitHub release. The code here may be broken though usually prelim work is done on a development branch before merging. To install the master branch:


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


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 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.

NOAA Disclaimer

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