R: Machine Learning and Mapping for Spatial Epidemiology

# Machine Learning and Mapping for Spatial Epidemiology

## Documentation for package ‘mlspatial’ version 0.1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

africa_shp | Africa shapefile data
africa_shps | Africa shapefile data 2
compute_spatial_autocorr | Compute Moran's I & LISA, classify clusters
eval_model | Get RMSE/MAE/R² metrics on training data
global_variables_eval | Declare known global variables to suppress R CMD check NOTE Global variables used in evaluation functions
join_data | Join spatial and incidence datasets
load_incidence_data | Load incidence data from Excel
load_shapefile | Load shapefile as sf + optionally convert to sp
model_evaluation_examples | Examples for model evaluation functions
pancre_mort | Pancreatic Cancer Mortality Data
panc_incidence | Pancreatic Cancer Incidence Data
panc_prevalence | Pancreatic Cancer Prevalence Data
plot_map_grid | Arrange Multiple tmap Plots in a Grid
plot_obs_vs_pred | Plot observed vs predicted values with correlation
plot_single_map | Build a tmap for a single variable
train_rf | Train Random Forest model
train_svr | Train Support Vector Regression (SVR) model
train_xgb | Train XGBoost model