MARSS

Multivariate Autoregressive State-Space Modeling with R

https://github.com/atsa-es/marss

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
  • Committers with academic emails
    5 of 8 committers (62.5%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (17.9%) to scientific vocabulary

Keywords

cran multivariate-timeseries r-package state-space-models statistics time-series

Keywords from Contributors

ecology extremes spatial-analysis spatiotemporal
Last synced: 9 months ago · JSON representation

Repository

Multivariate Autoregressive State-Space Modeling with R

Basic Info
Statistics
  • Stars: 52
  • Watchers: 5
  • Forks: 27
  • Open Issues: 33
  • Releases: 15
Topics
cran multivariate-timeseries r-package state-space-models statistics time-series
Created about 9 years ago · Last pushed 11 months ago
Metadata Files
Readme License

README.md

MARSS stands for Multivariate Auto-Regressive(1) State-Space. The MARSS R package estimates 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 via maximum-likelihood using an EM algorithm or BFGS. Fast fitting with TMB is available with the companion package marssTMB.

cran version github rstudio mirror downloads

INSTALL {#install}

To install MARSS from CRAN:

install.packages("MARSS") library(MARSS)

The latest release on GitHub may be ahead of the CRAN release. To install the latest release on GitHub. You can install from our r-universe repository: install.packages('MARSS', repos = c('https://atsa-es.r-universe.dev', 'https://cloud.r-project.org')) or install from GitHub install.packages("remotes") # if needed remotes::install_github("atsa-es/MARSS@*release")

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 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 Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS=TRUE)

DOCUMENTATION and TUTORIALS {#documentation}

  • Quick Start Guide.
  • User Manual - The extensive user manual included in the package has many examples of how to fit MARSS models to a variety of data sets.
  • ATSA lab book - Many applications are also covered in our Applied Time Series Analysis book developed from the labs in our course.
  • ATSA course website - We have lectures and all material from our course on our course website. Select the Lectures tab to find the lecture material and videos of lectures.

ISSUES and BUG REPORTS {#bugs}

Issues? https://github.com/atsa-es/MARSS/issues

CITATION {#cite}

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.

PUBLICATIONS {#pubs}

To see our publications using MARSS models, see the Applied Time Series Analysis website.

Developers

See inst/DEVELOPER_NOTES.md for instructions on creating a release from the repository.

License

The MARSS package as a whole is distributed under GPL-3 (GNU GENERAL PUBLIC LICENSE version 3).

In addition this software has the following license addendum:

Software code created by U.S. Government employees is not subject to copyright in the United States (17 U.S.C. 105). The United State s/Department of Commerce reserve all rights to seek and obtain copyright protection in countries other than the United States for Software authored in its entirety by the Department of Commerce. To this end, the Department of Commerce hereby grants to Recipient a royalty-free, nonexclusive license to use, copy, and create derivative works of the Software outside of the United States.

NOAA Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is provided on an as is basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

Owner

  • Name: Applied Time Series Analysis
  • Login: atsa-es
  • Kind: organization
  • Email: atsa.uw@gmail.com

Time series analysis code, books and teaching material for Applied Time-Series Analysis for Fisheries and Environmental Sciences.

GitHub Events

Total
  • Watch event: 4
  • Push event: 2
Last Year
  • Watch event: 4
  • Push event: 2

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 1,463
  • Total Committers: 8
  • Avg Commits per committer: 182.875
  • Development Distribution Score (DDS): 0.013
Past Year
  • Commits: 55
  • Committers: 3
  • Avg Commits per committer: 18.333
  • Development Distribution Score (DDS): 0.182
Top Committers
Name Email Commits
Eli Holmes e****s@n****v 1,444
Eli Holmes e****s@u****u 10
Eli Holmes e****s@g****m 3
Kathryn Doering k****g@n****v 2
mdscheuerell m****l@n****v 1
ericward-noaa e****d@n****v 1
Eric Ward e****d 1
Eric Ward 5****a 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 96
  • Total pull requests: 22
  • Average time to close issues: 3 months
  • Average time to close pull requests: 37 minutes
  • Total issue authors: 15
  • Total pull request authors: 2
  • Average comments per issue: 1.26
  • Average comments per pull request: 0.0
  • Merged pull requests: 22
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • eeholmes (76)
  • joacorapela (4)
  • slarge (2)
  • mdscheuerell (2)
  • ashaffer (2)
  • chenhappy (1)
  • mariajesus1980 (1)
  • Carherg (1)
  • anon94041 (1)
  • camrynblawas (1)
  • AleBitetto (1)
  • george-butler (1)
  • goephs (1)
  • ericward-noaa (1)
  • deveArt (1)
Pull Request Authors
  • eeholmes (21)
  • k-doering-NOAA (1)
Top Labels
Issue Labels
enhancement (29) bug (20) documentation (17) question (14) testing (4) wontfix (1) invalid (1) forecasting (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 933 last-month
  • Total docker downloads: 43,390
  • Total dependent packages: 4
  • Total dependent repositories: 4
  • Total versions: 26
  • Total maintainers: 1
cran.r-project.org: MARSS

Multivariate Autoregressive State-Space Modeling

  • Versions: 26
  • Dependent Packages: 4
  • Dependent Repositories: 4
  • Downloads: 933 Last month
  • Docker Downloads: 43,390
Rankings
Forks count: 2.9%
Stargazers count: 7.3%
Average: 10.1%
Dependent packages count: 10.7%
Dependent repos count: 14.8%
Downloads: 14.9%
Maintainers (1)
Last synced: 10 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
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  • lattice * suggests
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.github/workflows/pkgdown.glmmTMByaml actions
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  • JamesIves/github-pages-deploy-action v4.4.1 composite
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  • r-lib/actions/setup-pandoc v2 composite
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