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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 <https://atsa-es.github.io/>.
Getting started
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Package details |
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| Author | Elizabeth Eli Holmes [aut, cre] (<https://orcid.org/0000-0001-9128-8393>), Eric J. Ward [aut] (<https://orcid.org/0000-0002-4359-0296>), Mark D. Scheuerell [aut] (<https://orcid.org/0000-0002-8284-1254>), Kellie Wills [aut] |
| Maintainer | Elizabeth Eli Holmes <eli.holmes@noaa.gov> |
| License | GPL-2 |
| Version | 3.11.9 |
| URL | https://atsa-es.github.io/MARSS/ |
| Package repository | View on CRAN |
| Installation |
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