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'Optimal cutpoints' for binary classification tasks are often established bytesting which cutpoint yields the best discrimination, for example the Youdenindex, in a specific sample. This results in 'optimal' cutpoints that arehighly variable and systematically overestimate the out-of-sample performance.To address these concerns, the cutpointr package offers robust methods forestimating optimal cutpoints and the out-of-sample performance. The robustmethods include bootstrapping and smoothing based on kernel estimation,generalized additive models, smoothing splines, and local regression. Thesemethods can be applied to a wide range of binary-classification and cost-basedmetrics. cutpointr also provides mechanisms to utilize user-defined metrics andestimation methods. The package has capabilities for parallelization of thebootstrapping, including reproducible random number generation. Furthermore, itis pipe-friendly, for example for compatibility with functions from tidyverse.Various functions for plotting receiver operating characteristic curves,precision recall graphs, bootstrap results and other representations of thedata are included. The package contains example data from a study onpsychological characteristics and suicide attempts suitable for applying binaryclassification algorithms.

cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R