Search Results for "statistical"

...The central idea of {ggstatsplot} is simple: combine these two phases into one in the form of graphics with statistical details, which makes data exploration simpler and faster. Summary of statistical tests and effect sizes.

gtsummary is an R package for creating elegant, customizable, publication-ready summary tables of datasets and statistical models. It provides concise code to produce demographic tables (tbl_summary()), regression result tables, and more, with flexible styling options for reporting.

ggpubr is an R package that provides easy-to-use wrapper functions around ggplot2 to create publication-ready visualizations with minimal code. It streamlines plot creation for researchers and analysts, allowing features such as statistical annotation, theme customization, and plot arrangement with fewer lines of code.

ggforce is an extension package for ggplot2 that introduces specialized statistical transforms, geoms, and layout utilities to enhance and complement the built-in ggplot2 offerings. It enables more advanced visualization techniques such as faceting enhancements, hulls, annotation marks, and novel layouts for network data and marked regions.

see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It includes themes, scales, geoms for ggplot2, and custom color palettes to make visual summaries more informative and attractive.

...It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.

...The repository is designed as a shared space for code examples, datasets, and instructional materials, helping learners follow along with lectures and assignments. It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow.