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tinyVAST

Multivariate spatio-temporal models using dynamic structural equations

tinyVAST is an R package that fits multivariate spatio-temporal models while using Gaussian Markov random fields to represent nonseparable interactions among variables over time. See a preprint:

Thorson, J. T., Anderson, S. C., Goddard, P., & Rooper, C. N. (2024). tinyVAST: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models (arXiv:2401.10193). arXiv. http://arxiv.org/abs/2401.10193

Table of contents

Installation

tinyVAST can be installed from GitHub:

library(devtools)
install_github("vast-lib/tinyVAST", dependencies = TRUE)

Citation

To cite tinyVAST in publications use:

citation("tinyVAST")

Thorson, J. T., Anderson, S. C., Goddard, P., & Rooper, C. N. (2024). tinyVAST: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models (arXiv:2401.10193). arXiv. http://arxiv.org/abs/2401.10193

tinyVAST is builds upon many packages. This includes VAST R package:

Thorson, J.T. 2019. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fisheries Research 210: 143–161. https://doi.org/10.1016/j.fishres.2018.10.013.

and sdmTMB R package:

Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett. 2022. sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields. bioRxiv 2022.03.24.485545; doi: https://doi.org/10.1101/2022.03.24.485545

and the glmmTMB R package:

Brooks, M.E., Kristensen, K., van Benthem, K.J., Magnusson, A., Berg, C.W., Nielsen, A., Skaug, H.J., Maechler, M., and Bolker, B.M. 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal 9(2): 378–400. https://doi.org/10.32614/rj-2017-066.

INLA and inlabru can fit many of the same models as sdmTMB (and many more) in an approximate Bayesian inference framework.

mgcv can fit similar SPDE-based Gaussian random field models with code included in Miller et al. (2019).