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/web/packages/BRcal/index.html) and on Github (https://github.com/apguthrie/BRcal).

翻译：当概率预测过于保守而无法用于决策时，大胆度再校准能够在保持用户所需校准概率的前提下，实现负责任的预测强化。我们将大胆度再校准表述为在非线性校准约束条件下对大胆度进行的非线性优化问题。进一步证明，基于最大化线性对数几率似然函数的再校准方法，同样能最大化校准的后验概率。本文介绍BRcal——一个实现大胆度再校准及相关支持方法的R软件包，该方法体系为近期研究成果。BRcal包提供对校准-大胆度权衡关系的直接控制，并可视化展示不同校准水平如何改变个体预测结果。我们通过涉及住房止赎预测的新现实案例研究进行验证。BRcal包已在综合R档案网络（CRAN）（https://cran.r-project.org/web/packages/BRcal/index.html）和Github（https://github.com/apguthrie/BRcal）平台发布。