Abstract

When probability predictions are too cautious for decision making, boldness-recalibration enables responsible emboldening while maintaining the probability of calibration required by the user. We formulate boldness-recalibration as a nonlinear optimization of boldness with a nonlinear inequality constraint on calibration. We further show that recalibration based on the maximized linear log odds likelihood also maximizes the posterior probability of calibration. We introduce BRcal, an R package implementing boldness-recalibration and supporting methodology as recently proposed. The BRcal package provides direct control of the calibration-boldness tradeoff and visualizes how different calibration levels change individual predictions. We present a new real world case study involving housing foreclosure predictions. The BRcal package is available on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=BRcal) and on Github (https://github.com/apguthrie/BRcal).