CRAN/E | MARSS

MARSS

Multivariate Autoregressive State-Space Modeling

Installation

About

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 .

Citation MARSS citation info
atsa-es.github.io/MARSS/
Bug report File report

Key Metrics

Version 3.11.9
R ≥ 3.5.0
Published 2024-02-19 38 days ago
Needs compilation? no
License GPL-2
CRAN checks MARSS results

Downloads

Yesterday 74 +222%
Last 7 days 262 -3%
Last 30 days 1.212 -0%
Last 90 days 3.300 +48%
Last 365 days 11.443 -41%

Maintainer

Maintainer

Elizabeth Eli Holmes

eli.holmes@noaa.gov

Authors

Elizabeth Eli Holmes

aut / cre

Eric J. Ward

aut

Mark D. Scheuerell

aut

Kellie Wills

aut

Material

README
NEWS
Reference manual
Package source

In Views

TimeSeries

Additional repos

atsa-es.r-universe.dev

Vignettes

Learning MARSS
Quick Start Guide
EM_Derivation
Residuals
User Guide

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

MARSS archive

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

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