论文标题
ConformalInference.Multi和ConformalInference.fd：共形预测的双包装
conformalInference.multi and conformalInference.fd: Twin Packages for Conformal Prediction
论文作者
论文摘要
在回归模型的基础上，共形预测方法产生了无分布的预测集，仅需要I.I.D.数据。尽管已经开发了为单变量响应框架实施此类方法的R软件包，但多元和功能响应并非如此。 conformalinference.multi和ConformalInference.fd通过扩展古典和更先进的保形预测方法（如完全保形，拆分保形，Jackknife+ and Jackknife+和多裂料）来处理多变量和功能案例，以解决此空隙。包裹的结构完全包含了共形预测的极端灵活性，该软件包的结构不需要任何特定的回归模型，使用户可以将任何回归函数作为输入传递，同时使用基本的回归模型作为参考。最后，通过提供嵌入式绘图功能以可视化预测区域来解决可视化问题。
Building on top of a regression model, Conformal Prediction methods produce distribution free prediction sets, requiring only i.i.d. data. While R packages implementing such methods for the univariate response framework have been developed, this is not the case with multivariate and functional responses. conformalInference.multi and conformalInference.fd address this void, by extending classical and more advanced conformal prediction methods like full conformal, split conformal, jackknife+ and multi split conformal to deal with the multivariate and functional case. The extreme flexibility of conformal prediction, fully embraced by the structure of the package, which does not require any specific regression model, enables users to pass in any regression function as input while using basic regression models as reference. Finally, the issue of visualisation is addressed by providing embedded plotting functions to visualize prediction regions.
