R package MARSS: Multivariate Autoregressive State-Space Modeling. The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data. Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks. The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates. Type RShowDoc(”UserGuide”, package=”MARSS”) at the R command line to open the MARSS user guide. Online workshops (lectures and computer labs) at http://faculty.washington.edu/eeholmes/workshops.shtml See the NEWS file for update information.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Tucker S. McElroy, James A. Livsey: Ecce Signum: An R Package for Multivariate Signal Extraction and Time Series Analysis (2022) arXiv
- Jouni Helske: KFAS: Exponential Family State Space Models in R (2017) not zbMATH
- Chuliá, Helena; Guillén, Montserrat; Uribe, Jorge M.: Modeling longevity risk with generalized dynamic factor models and vine-copulae (2016)