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/>.
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 [aut, cre], Eric J. Ward [aut], Mark D. Scheuerell [aut], Kellie Wills [aut] Maintainer: Elizabeth Eli Holmes <eli.holmes at noaa.gov> BugReports: https://github.com/atsa-es/MARSS/issues License: GPL-2 URL: https://atsa-es.github.io/MARSS/ NeedsCompilation: no Additional_repositories: https://atsa-es.r-universe.dev Citation: MARSS citation info Materials: README NEWS In views: TimeSeries CRAN checks: MARSS results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=MARSS to link to this page.
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