The R Journal: article published in 2012, volume 4:1

MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data
Elizabeth E. Holmes, Eric J. Ward and Kellie Wills , The R Journal (2012) 4:1, pages 11-19.

Abstract MARSS is a package for fitting multivariate autoregressive state-space models to time-series data. The MARSS package implements state-space models in a maximum likelihood framework. The core functionality of MARSS is based on likelihood maximization using the Kalman filter/smoother, combined with an EM algorithm. To make comparisons with other packages available, parameter estimation is also permitted via direct search routines available in ’optim’. The MARSS package allows data to contain missing values and allows a wide variety of model structures and constraints to be specified (such as fixed or shared parameters). In addition to model-fitting, the package provides bootstrap routines for simulating data and generating confidence intervals, and multiple options for calculating model selection criteria (such as AIC).

CRAN packages: MARSS, sspir, dlm, dse, KFAS, FKF
CRAN Task Views implied by cited CRAN packages: TimeSeries, Finance, Bayesian, Environmetrics

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2012-002,
  author = {Elizabeth E. Holmes and Eric J. Ward and Kellie Wills},
  title = {{MARSS: Multivariate Autoregressive State-space Models for
          Analyzing Time-series Data}},
  year = {2012},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2012-002},
  url = {https://doi.org/10.32614/RJ-2012-002},
  pages = {11--19},
  volume = {4},
  number = {1}
}