Bayesian Hierarchical Modelling of Spatio-Temporal Health Data
Convert R-INLA Model Formulas into a GHRformulas Object
Add Covariates to All Combinations
Generate Interaction Terms Between Covariates
Create Covariate Combinations Across Groups
Create Non-Linear Effects for INLA
Build Univariable Covariate Sets
Create Spatially or Temporally Varying Effects for INLA
Create a Two-Dimensional INLA-compatible Cross-basis Matrix
Generate DLNM Predictions from GHRmodels Objects
Extract Covariate Names
Fit Multiple INLA Models
Retrieve Covariates from a GHRmodels Object as a List of Character V...
GHRmodel: Bayesian Hierarchical Modelling of Spatio-Temporal Health Da...
Generate lagged variables for one or more lags
Create a One-Dimensional Basis for INLA
Plot crosspred Objects: Overall, Slices, or Heatmap
Produce a Forest Plot of Linear Covariates from a GHRmodels Object
Plot Nonlinear Effects from a GHRmodels Object
Produce a Forest Plot for a Spatially or Temporally Varying Effects fr...
Plot Observed vs. Fitted Cases
Plot Models by Goodness-of-Fit
Plot Posterior Predictive Densities Versus Observed Data
Plot Random Effects
Rank Models by Goodness-of-Fit
Sample from the Posterior Predictive Distribution
Merge GHRmodels
Subset GHRmodels Objects
Generate INLA-compatible Model Formulas
Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the 'INLA' framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) <doi:10.1016/S2542-5196(20)30292-8>.
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