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, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <>.

Version: 3.11.9
Depends: R (≥ 3.5.0)
Imports: generics (≥ 0.1.2), graphics, grDevices, KFAS (≥ 1.0.1), mvtnorm, nlme, stats, utils
Suggests: forecast, ggplot2, Hmisc, knitr, marssTMB
Published: 2024-02-19
DOI: 10.32614/CRAN.package.MARSS
Author: Elizabeth Eli Holmes ORCID iD [aut, cre], Eric J. Ward ORCID iD [aut], Mark D. Scheuerell ORCID iD [aut], Kellie Wills [aut]
Maintainer: Elizabeth Eli Holmes <eli.holmes at>
License: GPL-2
NeedsCompilation: no
Citation: MARSS citation info
Materials: README NEWS
In views: TimeSeries
CRAN checks: MARSS results


Reference manual: MARSS.pdf
Vignettes: Learning MARSS
Quick Start Guide
User Guide


Package source: MARSS_3.11.9.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MARSS_3.11.9.tgz, r-oldrel (arm64): MARSS_3.11.9.tgz, r-release (x86_64): MARSS_3.11.9.tgz, r-oldrel (x86_64): MARSS_3.11.9.tgz
Old sources: MARSS archive

Reverse dependencies:

Reverse suggests: dsem, freqdom, freqdom.fda


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