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fpROC

Provides optimized ‘C++’ code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) doi:10.1016/j.ecolmodel.2007.11.008. Parallelization via ‘OpenMP’ was implemented with assistance from the ‘DeepSeek’ Artificial Intelligence Assistant (https://www.deepseek.com/).

Installation

You can install the development version of fpROC from GitHub with:

# install.packages("pak")
pak::pak("luismurao/fpROC")

Examples

The package can work with numerical vectors and terra SpatRaster objects.

An example using numerical data

set.seed(999)
# With numeric vectors
test_data <- rnorm(100)
pred_data <- rnorm(100)
result <- fpROC::auc_metrics(test_prediction = test_data, prediction = pred_data)

An example using terra SpatRaster objects.

set.seed(999)
# With SpatRaster
library(terra)
#> terra 1.8.54
r <- terra::rast(ncol=10, nrow=10)
values(r) <- rnorm(terra::ncell(r))
result <- fpROC::auc_metrics(test_prediction = test_data, prediction = r)

Acknowledgments

CONACYT Ciencia de Frontera CF-2023-I-1156. Laboratorio Nacional de Biología del Cambio Climático, SECIHTI, México. To PAPIIT-UNAM IA202824 and PAPIIT-UNAM IA203922.RGC-D thanks the Dirección General de Asuntos del Personal Académico (DGAPA) from the UNAM, and the Secretaría de Ciencia, Humanidades, Tecnología e Innovación for her postdoctoral scholarship.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.