MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data

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).

Elizabeth E. Holmes , Eric J. Ward , Kellie Wills
2012-6-01

CRAN packages used

MARSS, sspir, dlm, dse, KFAS, FKF

CRAN Task Views implied by cited packages

TimeSeries, Finance, Bayesian, Environmetrics

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Citation

For attribution, please cite this work as

Holmes, et al., "MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data", The R Journal, 2012

BibTeX citation

@article{RJ-2012-002,
  author = {Holmes, Elizabeth E. and Ward, Eric J. and Wills, Kellie},
  title = {MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data},
  journal = {The R Journal},
  year = {2012},
  note = {https://doi.org/10.32614/RJ-2012-002},
  doi = {10.32614/RJ-2012-002},
  volume = {4},
  issue = {1},
  issn = {2073-4859},
  pages = {11-19}
}