as_GHRformulas: This function converts a character vector of suitable R-INLA formulas into a structured GHRformulas object.
cov_add: This function appends one or more covariate names to all elements (i.
cov_interact: This function generates interaction terms between covariates specified in the pattern or name arguments.
cov_multi: This function generates all possible combinations of covariates by selecting one variable from each user-defined group.
cov_nl: This function transforms selected covariates identified by pattern or name into non-linear terms using INLA’s f() syntax.
cov_uni: This function returns a list where each element contains a single covariate, based on covariates specified in the pattern or name arguments.
cov_varying: This function transforms covariates identified by pattern or name into varying effect terms of the form: f(unit, covariate, model = ‘iid’) , which allows covariates to have varying slopes across spatial or temporal units.
crossbasis_inla: This function is a wrapper around dlnm::crossbasis to generate cross-basis matrices that capture nonlinear effects of a predictor across both exposure and lag dimensions.
crosspred_inla: This function takes an object of class GHRmodels , extracts the relevant coefficients and variance-covariance matrix, and then calls dlnm::crosspred to compute predictions over a range of covariate values (or at specified points).
dengue_MS: The dengue_MS example data set contains monthly counts of notified dengue cases by microregion, along with a range of spatial and spatiotemporal covariates (e.
dengue_SP: The dengue_SP example data set reports the weekly number of notified dengue cases in the municipality of São Paulo together with climatic covariates.
extract_names: This function allows the user to select variables from a data set by prefix (using the pattern argument) or by exact name matching.
fit_models: This function fits a set of INLA model formulas, provided in a GHRformulas object, to a specified dataset.
get_covariates: Extracts covariates from a GHRmodels object and returns them as a list of character vectors.
lag_cov: This function creates lagged versions of one or more numeric or categorical variables in an equally spaced time-series data set.
map_MS: A simple feature ( sf ) multipolygon object representing a map of Mato Grosso do Sul , Brazil, including 11 municipalities.
onebasis_inla: This function is a wrapper around onebasis to create a one-dimensional basis for spline modeling.
plot_coef_crosspred: Generate plots from a “crosspred” object.
plot_coef_lin: This function extracts fixed-effect coefficients from a specified model in models , filters them by name or interaction pattern, and produces a forest plot (point estimates with error bars).
plot_coef_nl: Generates plots of nonlinear effects from one or more fitted models contained within a GHRmodels object.
plot_coef_varying: Generates a forest plot for a specified spatially or temporally varying coefficient (i.
plot_fit: This function creates a time-series plot comparing observed cases with fitted values from one or more models in a GHRmodels object.
plot_gof: Provides visualization of model performance using selected goodness-of-fit (GoF) metrics for one or more models.
plot_ppd: This function draws kernel-density curves for posterior-predictive samples and observed data using ggplot2::geom_line() .
plot_re: Generates plots of random effects from one or more fitted models contained within a GHRmodels object.
rank_models: This function ranks fitted models in a GHRmodels object by a chosen metric (e.
sample_ppd: This function refits a specified model from a GHRmodels object and generates samples from its posterior predictive distribution.
stack_models: This function stack together two or more objects GHRmodels object, returning one GHRmodels object that contains all the input models.
subset_models: This function subsets selected models from a GHRmodels object into a new reduced GHRmodels object.
write_inla_formulas: This function streamlines the creation of INLA-compatible model formulas by automatically structuring fixed effects, random effects, and interactions.
