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mlspatial
View on CRAN: Click
here
Download and install mlspatial package within the R console
Install from CRAN:
install.packages("mlspatial")
Install from Github:
library("remotes")
install_github("cran/mlspatial") Install by package version:
library("remotes")
install_version("mlspatial", "0.1.1") Attach the package and use:
library("mlspatial")
Maintained by
Adeboye Azeez
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-08-26
Latest Update: 2025-08-26
Description:
Provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). 'Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model' <doi:10.5281/zenodo.16529986> and <doi:10.5281/zenodo.16529016>. It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. 'mlspatial' enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, 'XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions.
How to cite:
Adeboye Azeez (2025). mlspatial: Machine Learning and Mapping for Spatial Epidemiology. R package version 0.1.1, https://cran.r-project.org/web/packages/mlspatial. Accessed 14 Mar. 2026.
Previous versions and publish date:
0.1.0 (2025-08-26 21:40)
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