Department of Epidemiology, Boston University, Boston, MA, USA.
Department of Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
Int J Epidemiol. 2024 Aug 14;53(5). doi: 10.1093/ije/dyae113.
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.
micd and tpc packages are R packages.
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.
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).
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 算法实现都存在重要的局限性，例如缺失值、时间结构或混合测量尺度（分类/连续），这些都是队列数据的常见特征。这里介绍的新的 R 包 micd 和 tpc 弥补了这些空白。
micd 和 tpc 包是 R 包。
micd 包为现有的 pcalg R 包提供了处理缺失值的附加功能，包括基于随机缺失假设的多重插补方法。此外，micd 允许在假设条件高斯性的情况下使用混合测量尺度。tpc 包以一种有效利用时间信息的方式，提供了更具信息量的输出，从而减少了统计误差的可能性。
tpc 和 micd 包可在 Comprehensive R Archive Network (CRAN) 上免费获得。它们的源代码也可在 GitHub 上获得（https://github.com/bips-hb/micd；https://github.com/bips-hb/tpc）。
