论文标题
DirePack：用于最先进统计维度减少方法的Python 3包装
direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods
论文作者
论文摘要
DirePack软件包旨在将一组现代统计维度缩小技术建立在Python宇宙中，以单个一致的软件包。减少尺寸的方法包括分为三类：基于投影追击的尺寸降低，足够的尺寸降低以及降低尺寸的强大m估计器。作为推论，还提供了基于这些缩小尺寸空间的正则回归估计器，从经典的主成分回归到稀疏的部分稳健M回归。该软件包还包含一组经典和强大的预处理实用程序，包括广义空间标志，以及专用的绘图功能和交叉验证公用事业。最后，DirePack的编写与Scikit-Learn API一致，因此可以将估计器完美地包含在该框架中的（统计和/或机器）学习管道中。
The direpack package aims to establish a set of modern statistical dimension reduction techniques into the Python universe as a single, consistent package. The dimension reduction methods included resort into three categories: projection pursuit based dimension reduction, sufficient dimension reduction, and robust M estimators for dimension reduction. As a corollary, regularized regression estimators based on these reduced dimension spaces are provided as well, ranging from classical principal component regression up to sparse partial robust M regression. The package also contains a set of classical and robust pre-processing utilities, including generalized spatial signs, as well as dedicated plotting functionality and cross-validation utilities. Finally, direpack has been written consistent with the scikit-learn API, such that the estimators can flawlessly be included into (statistical and/or machine) learning pipelines in that framework.
