制作有向无环图的开源软件比较。

IF 1.7 4区 医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Causal Inference Pub Date : 2024-01-01 Epub Date: 2024-01-10 DOI:10.1515/jci-2023-0031
Amy J Pitts, Charlotte R Fowler
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引用次数: 0

摘要

为了帮助研究人员绘制有向无环图(DAG),已经开发了许多软件包,每种软件都有独特的功能和可用性。我们研究了五种最常用的生成 DAG 的软件:TikZ、DAGitty、ggdag、dagR 和 igraph。我们对每个软件包的背景、分析和可视化能力以及用户友好性进行了总体描述。此外,为了对软件包进行比较,我们在每个软件中制作了两个 DAG,第一个 DAG 包含一个简单的混杂结构,第二个 DAG 包含一个包含三个混杂因素和一个中介因素的更复杂的结构。我们将根据用户的需求,为何时使用每种软件提供建议。
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Comparison of open-source software for producing directed acyclic graphs.

Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to generate DAGs: TikZ, DAGitty, ggdag, dagR, and igraph. For each package, we provide a general description of its background, analysis and visualization capabilities, and user-friendliness. Additionally in order to compare packages, we produce two DAGs in each software, the first featuring a simple confounding structure, while the second includes a more complex structure with three confounders and a mediator. We provide recommendations for when to use each software depending on the user's needs.

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来源期刊
Journal of Causal Inference
Journal of Causal Inference Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.90
自引率
14.30%
发文量
15
审稿时长
86 weeks
期刊介绍: Journal of Causal Inference (JCI) publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality.
期刊最新文献
Evaluating Boolean relationships in Configurational Comparative Methods Comparison of open-source software for producing directed acyclic graphs. LINGUISTIC FEATURES AND PRESENTATION OF MATERIALS ON ENGLISH TEXTBOOK “WHEN ENGLISH RINGS A BELL” BASED ON BSNP Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches Attributable fraction and related measures: Conceptual relations in the counterfactual framework
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