VISROC 2.0: Updated Software for the Visualization of the significance of Receiver Operating Characteristics based on confidence ellipses

The Receiver Operating Characteristics (ROC) method is used to evaluate the diagnostic accuracy of binary quantitative tests in a broad spectrum of disciplines, including medicine, physics of complex systems, geophysics, meteorology, etc. The estimation of the significance of the examined prediction method is of high importance and it's usually approximated by Monte Carlo calculations. To simplify this problem, a FORTRAN code called VISROC was submitted to the CPC Program Library in 2014. VISROC evaluates the significance of binary diagnostic and prognostic tools for a family of k-ellipses which are based on confidence ellipses and cover the whole ROC space. Since that time, the code has been significantly improved and several new capabilities have been added. Most importantly, a Graphical User Interface (GUI) has been implemented, which can be invoked using either the R shiny web application or the Python application available for Windows, Mac, and Linux operating systems, both of which are described here.

The previous version of this program (AERY_v1_0) may be found here https://doi.org/10.1016/j.cpc.2013.12.009.

受试者工作特征（Receiver Operating Characteristics, ROC）方法被广泛应用于多个学科领域，用以评估二元定量检测的诊断准确性，涵盖医学、复杂系统物理学、地球物理学、气象学等诸多方向。对所研究的预测方法的显著性进行评估具有重要意义，这类评估通常通过蒙特卡洛（Monte Carlo）计算进行近似。为简化该问题，一款名为VISROC的FORTRAN程序代码于2014年被提交至《计算机物理学报》（Computer Physics Communications, CPC）程序库。VISROC可针对基于置信椭圆构建、覆盖整个ROC空间的k椭圆族，评估二元诊断与预测工具的显著性。自该版本发布以来，该程序代码得到了大幅优化，并新增了多项功能。其中最为关键的是，现已实现图形用户界面（Graphical User Interface, GUI），可通过R Shiny网页应用或适配Windows、Mac及Linux操作系统的Python应用程序调用，本文将对这两种调用方式均进行详细说明。
该程序的旧版本（AERY_v1_0）可通过以下链接获取：https://doi.org/10.1016/j.cpc.2013.12.009。