MOTIVATION The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc, fill these gaps. IMPLEMENTATION micd and tpc packages are R packages. GENERAL FEATURES The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors. AVAILABILITY The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd; https://github.com/bips-hb/tpc).

中文翻译：

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软件应用程序配置文件：tpc 和 micd-R 软件包，用于使用不完整的队列数据进行因果发现。


动机 Peter Clark (PC) 算法是一种流行的因果发现方法，用于以数据驱动的方式学习因果图。直到最近，R 中现有的 PC 算法实现在缺失值、时间结构或混合测量尺度（分类/连续）方面存在重要局限性，这些都是队列数据的常见特征。这里介绍的新 R 包、micd 和 tpc 填补了这些空白。实现micd 和tpc 包是R 包。一般功能 micd 包提供了用于处理现有 pcalg R 包缺失值的附加功能，包括依赖于随机缺失假设的多重插补方法。此外，micd 允许假设条件高斯性的混合测量尺度。 tpc 包有效地利用时间信息，从而产生信息更丰富且不易出现统计错误的输出。可用性 tpc 和micd 软件包可在综合R 存档网络(CRAN) 上免费获取。它们的源代码也可以在 GitHub 上找到（https://github.com/bips-hb/micd；https://github.com/bips-hb/tpc）。