Replicate analysis from: tinyVAST: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models

Aim Multivariate spatio-temporal models are widely applicable, but
specifying their structure is complicated and may inhibit wider
use.  We introduce the R package tinyVAST from two viewpoints:
the software user and the statistician.  Innovation From the user
viewpoint, tinyVAST adapts a widely used formula interface to specify
generalized additive models, and combines this with arguments to specify
spatial and spatio-temporal interactions among variables. These
interactions are specified using arrow notation (from structural equation
models), or an extended arrow-and-lag notation that allows simultaneous,
lagged, and recursive dependencies among variables over time. 
The user also specifies a spatial domain for areal (gridded), continuous
(point-count), or stream-network data. From the statistician's
viewpoint, tinyVAST constructs sparse precision matrices representing
multivariate spatio-temporal variation, and parameters are estimated by
specifying a generalized linear mixed model (GLMM).  This
expressive interface encompasses vector autoregressive, empirical
orthogonal functions, spatial factor analysis, and ARIMA models. 
Main conclusion To demonstrate, we fit the data from two survey platforms
sampling corals, sponges, rockfishes, and flatfishes in the Gulf of Alaska
and Aleutian Islands. We then compare eight alternative model structures
using different assumptions about habitat drivers and survey
detectability. Model selection suggests that towed-camera and bottom trawl
gears have spatial variation in detectability but sample the same
underlying density of flatfishes and rockfishes, and that rockfishes are
positively associated with sponges while flatfishes are negatively
associated with corals.  We conclude that tinyVAST can be used to
test complicated dependencies representing alternative structural
assumptions for research and real-world policy evaluation.