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RiskMap

Overview

RiskMap provides functions for geo-statistical analysis of both continuous and count data using maximum likelihood methods. The models implemented in the package use stationary Gaussian processes with Matern correlation function to carry out spatial prediction in a geographical area of interest. The underpinning theory of the methods implemented in the package are found in Diggle and Giorgi (2019).

Installation

To install the stable version, use:

install.packages("RiskMap")

The development version can be installed using devtools:

# install.packages("devtools") # if not already installed
devtools::install_github("claudiofronterre/RiskMap")

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Version

Install

install.packages('RiskMap')

Monthly Downloads

188

Version

1.0.0

License

MIT + file LICENSE

Maintainer

Emanuele Giorgi

Last Published

October 9th, 2025

Functions in RiskMap (1.0.0)

abund_sma

Female Culex pipiens abundance (collections) in the Sacramento Metropolitan Area
dast

Fitting of decay-adjusted spatio-temporal (DAST) model
infect_sma

West Nile virus pool tests for female *Culex pipiens* in the Sacramento Metropolitan Area
glgpm

Estimation of Generalized Linear Gaussian Process Models
liberia

River-blindness in Liberia
gp

Gaussian Process Model Specification
dist_summaries

Summaries of the distances
glgpm_sim

Simulation from Generalized Linear Gaussian Process Models
loaloa

Loa loa prevalence data from 197 village surveys
galicia

Heavy metal biomonitoring in Galicia
matern_cor

Matern Correlation Function
matern.hessian.phi

Second Derivative with Respect to \(\phi\)
matern.grad.phi

First Derivative with Respect to \(\phi\)
plot.RiskMap_pred_target_shp

Plot Method for RiskMap_pred_target_shp Objects
maxim.integrand

Maximization of the Integrand for Generalized Linear Gaussian Process Models
plot_mda

Plot the estimated MDA impact function
italy_sim

Simulated data-set on the Italian peninsula
malkenya

Malaria Transmission in the Western Kenyan Highlands
plot.RiskMap_pred_target_grid

Plot Method for RiskMap_pred_target_grid Objects
plot_AnPIT

Plot Calibration Curves (AnPIT / PIT) from Spatial Cross-Validation
print.summary.RiskMap

Print Summary of RiskMap Model
plot_sim_surf

Plot simulated surface data for a given simulation
print.summary.RiskMap.sim.res

Print Simulation Results
malnutrition

Malnutrition in Ghana
pred_over_grid

Prediction of the random effects components and covariates effects over a spatial grid using a fitted generalized linear Gaussian process model
print.summary.RiskMap.spatial.cv

Print Summary of RiskMap Spatial Cross-Validation Scores
propose_utm

EPSG of the UTM Zone
summary.RiskMap.sim.res

Summarize Simulation Results
summary.RiskMap.spatial.cv

Summarize Cross-Validation Scores for Spatial RiskMap Models
set_control_sim

Set Control Parameters for Simulation
pred_target_shp

Predictive Targets over a Shapefile (grid-aggregated)
summary.RiskMap

Summarize Model Fits
pred_target_grid

Predictive Target Over a Regular Spatial Grid
tz_malaria

Malaria Dataset from Tanzania Demographic Health Surveys 2015
s_variogram

Empirical variogram
re

Random Effect Model Specification
plot_s_variogram

Plotting the empirical variogram
plot_score

Plot Spatial Scores for a Specific Model and Metric
tz_covariates

Covariates Dataset for Malaria Prediction in Tanzania
update_predictors

Update Predictors for a RiskMap Prediction Object
to_table

Generate LaTeX Tables from RiskMap Model Fits and Validation
surf_sim

Simulate surface data based on a spatial model
check_mcmc

Check MCMC Convergence for Spatial Random Effects
compute_ID_coords

Compute Unique Coordinate Identifiers
coef.RiskMap

Extract Parameter Estimates from a "RiskMap" Model Fit
anopheles

Anopheles mosquitoes in Southern Cameroon
convex_hull_sf

Convex Hull of an sf Object
assess_pp

Assess Predictive Performance via Spatial Cross-Validation
create_grid

Create Grid of Points Within Shapefile
assess_sim

Assess Simulations
Laplace_sampling_MCMC

Laplace-sampling MCMC for Generalized Linear Gaussian Process Models