MARSS: Multivariate autoregressive state-space models for analyzing time-series data
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| Authors | Elizabeth E. Holmes, Eric John Ward, Kellie C. Wills |
| Journal/Conference Name | The R Journal |
| Paper Category | Other |
| Paper Abstract | MARSS is a package for fitting mul- tivariate autoregressive state-space models to time-series data. The MARSS package imple- ments state-space models in a maximum like- lihood framework. The core functionality of MARSS is based on likelihood maximization us- ing 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 avail- able 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 parame- ters). In addition to model-fitting, the package provides bootstrap routines for simulating data and generating confidence intervals, and multi- ple options for calculating model selection crite- ria (such as AIC). |
| Date of publication | 2012 |
| Code Programming Language | R |
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