modelsummary: Data and Model Summaries in R

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Vincent Arel‐Bundock
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引用次数: 11

Abstract

modelsummary is a package to summarize data and statistical models in R . It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. It makes it easy to execute common tasks such as computing robust standard errors, adding significance stars, and manipulating coefficient and model labels. Beyond model summaries, the package also includes a suite of tools to produce highly flexible data summary tables, such as dataset overviews, correlation matrices, (multi-level) cross-tabulations, and balance tables (also known as “Table 1”). The appearance of the tables produced by modelsummary can be customized using external packages such as kableExtra , gt , flextable , or huxtable ; the plots can be customized using ggplot2 . Tables can be exported to many output formats, including HTML, L A TEX, Text/Markdown, Microsoft Word, Powerpoint, Excel, RTF, PDF, and image files. Tables and plots can be embedded seamlessly in rmarkdown , knitr , or Sweave dynamic documents. The modelsummary package is designed to be simple, robust, modular, and extensible.
modelsummary: R中的数据和模型摘要
modelsummary是一个在R中汇总数据和统计模型的包。它支持一百多种现成的模型,并允许用户在表格或系数图中并排报告这些模型的结果。它使执行诸如计算健壮标准误差、添加显著性星型以及操纵系数和模型标签等常见任务变得容易。除了模型摘要之外,该软件包还包括一套工具,用于生成高度灵活的数据摘要表,例如数据集概述、相关矩阵、(多级)交叉表和平衡表(也称为“表1”)。modelsummary生成的表的外观可以使用外部包(如kableExtra、gt、flextable或huxtable)进行定制;可以使用ggplot2定制这些图。表格可以导出为多种输出格式,包括HTML、la TEX、Text/Markdown、Microsoft Word、Powerpoint、Excel、RTF、PDF和图像文件。表格和绘图可以无缝地嵌入到markdown、knitr或Sweave动态文档中。modelsummary包被设计成简单、健壮、模块化和可扩展的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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