Multivariate Spatio-Temporal Models using Structural Equations
Add predictions to data-list
Calculate conditional AIC
Classify variables path
Calculate deviance explained
Additional families
Integration for target variable
Extract the (marginal) log-likelihood of a tinyVAST model
Make a RAM (Reticular Action Model)
Make a RAM (Reticular Action Model)
Make a RAM (Reticular Action Model) from a SEM (structural equation mo...
Parse path
Predict using vector autoregressive spatio-temporal model
print summary of tinyVAST model
Objects exported from other packages
Reload a previously fitted model
Calculate deviance or response residuals for tinyVAST
Multivariate Normal Random Deviates using Sparse Precision
Rotate factors to match Principal-Components Analysis
Sample from predictive distribution of a variable
Construct projection matrix for stream network
Make mesh for stream network
Simulate GMRF for stream network
Simulate new data from a fitted model
summarize tinyVAST
Fit vector autoregressive spatio-temporal model
Control parameters for tinyVAST
Extract Variance-Covariance Matrix
Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2024) <doi:10.48550/arXiv.2401.10193> for more details.