Package index • PRIDEC

All functions

calc_inla_vi(): Calculate variable importance in INLA via simulation

calc_pi_nb_analytic(): Estimate glm.nb prediction interval analytically

create_counterfactual_inla(): Create counterfactual data of variables from INLA model

create_demo_preds(): Simulate a data.frame prediction interval to use in tests

create_pridec_quarto(): Function to create quarto doc from model outputs

demo_malaria: Demo malaria datasets

demo_polygon: Demo malaria dataset polygon

ensemble_forecast(): Fit an ensemble forecast

eval_performance(): Evaluate performance on a cv_set

fill_seasonal(): Fill in missing data via seasonally decomposed imputation

fit_arima(): Fit an ARIMAX model to a cv_set

fit_arima_OneOrgUnit(): Fit an ARIMAX model to one orgUnit

fit_glm_nb(): Fit a negative binomial GLM to a cv_set

fit_inla(): Fit an INLA model to one cv_set and get prediction intervals

fit_naive(): Fit a naive model to a cv_set

fit_ranger(): Fit a ranger Random Forest model to a cv_set

get_arima_pi(): Internal function for estimating prediction intervals from ARIMAX model

get_cv_subsets(): Internal function to format cv_set for analysis

get_inla_pi(): Function to calculate and format posterior prediction distributions from INLA using random error trick

get_inla_pi_sample(): Function to calculate and format posterior prediction distributions from INLA via posterior sampling

get_wis(): Calculate Weighted Interval Score

inv_variables_arima(): Investigate variables from ARIMAX model

inv_variables_glm_nb(): Estimate variable importance and counter-factual plots of a glm.nb model

inv_variables_inla(): Estimate variable importance and partial dependence plots of a INLA model

inv_variables_ranger(): Estimate variable importance and partial dependence plots of a ranger model

plot_counterfactual(): Plot multiple counterfactual plots from the list of cf_data using ggplot

plot_counterfactual_one(): Plot one counterfactual plot from models using ggplot

plot_predictions(): Plot predictions and PI ribbon with ggplot2

prep_data(): Prepare case data and predictor variables for forecast models

retry_arima(): Internal function to iteratively fit an ARIMAX model

run_inla_config(): Configuration for an INLA model An internal function to run a configured INLA model

sample_fixed_posteriors(): Sample from fixed-effect posteriors

sample_preds(): Utility function to get weighted means of predictions

split_cv_forecast(): Create data to use for forecast

split_cv_rolling(): Create cv folds within a dataset using rolling origin method

split_stratified(): Split the dataset into a training and testing set

train_models(): Run full train_model workflow using multiple modeling approaches

tune_ranger(): Tune a ranger random forest model