GithubHelp home page GithubHelp logo

mutual-ai / marss Goto Github PK

View Code? Open in Web Editor NEW

This project forked from atsa-es/marss

0.0 2.0 0.0 5.13 MB

Multivariate Autoregressive State-Space Modeling with R

Home Page: https://nwfsc-timeseries.github.io/MARSS

R 99.31% HTML 0.69%

marss's Introduction

MARSS

cran version rstudio mirror downloads

MARSS stands for Multivariate Auto-Regressive(1) State-Space. The MARSS package is an R package for estimating 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 to multivariate time-series data via maximum-likelihood using primarily an EM algorithm.

Collaborate

Issues? https://github.com/nwfsc-timeseries/MARSS/issues

Wiki https://github.com/nwfsc-timeseries/MARSS/wiki

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:

install.packages("devtools")
library(devtools)
install_github("nwfsc-timeseries/MARSS@*release")
library(MARSS)

The master branch on GitHub has work leading up to a GitHub release. The code here may be broken though usually prelim work is done on a development branch before merging. To install the master branch:

install_github("nwfsc-timeseries/MARSS")

DOCUMENTATION and TUTORIALS

There is an extensive user manual included in the package: here.

Many applications are also covered in our Applied Time Series Analysis book: here.

We have lectures on our course website: here.

CITING:

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 as you would other R packages:

Elizabeth Holmes, Eric Ward, Mark Scheuerell, and Kellie Wills (2018). MARSS: Multivariate Autoregressive State-Space Modeling. R package version 3.10.4.

Update the version number and year if you use a more recent version on GitHub.

PUBLICATIONS

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

marss's People

Contributors

eeholmes avatar eric-ward avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.