A Gallery of Animations in Statistics and Utilities to Create Animations

Provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, non-parametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted into 'Sweave' / 'knitr' easily.

Fetching Tabular Data "Onload" in Compiled R Markdown HTML Documents

Dynamically retrieve data from the web to render HTML tables on inspection in R Markdown HTML documents.

Create an HTML Vertical Timeline from a Data Frame in 'rmarkdown' and 'shiny'

Creates an HTML vertical timeline from a data frame as an input for 'rmarkdown' documents and 'shiny' applications.

Produce R Functions to Create HTML Forms Based on SQL Meta Data

Offers meta programming style tools to generate configurable R functions that produce HTML forms based on table input and SQL meta data. Also generates functions for collecting the parameters of those HTML forms after they are submitted. Useful for quickly generating HTML forms based on existing SQL tables. To use the resultant functions, the output files containing those functions must be read into the R environment (perhaps using base::source()).

Create Satisficing 'Excel', 'HTML', 'LaTeX', and 'RTF' Tables using a Simple Formula

Create "good enough" tables with a single formula. 'tablespan' tables can be exported to 'Excel', 'HTML', 'LaTeX', and 'RTF' by leveraging the packages 'openxlsx' and 'gt'. See < https://jhorzek.github.io/tablespan/> for an introduction.

Create Simple Yet Powerful HTML Documents with Light Weight CSS Frameworks

Create minimal, responsive, and style-agnostic HTML documents with the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'. Powerful features include table of contents floating as a sidebar, folding codes and results, and more.

High Performance CommonMark and Github Markdown Rendering in R

The CommonMark specification < https://github.github.com/gfm/> defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text.

Simple and Configurable Tables in 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', and 'Typst' Formats

Create highly customized tables with this simple and dependency-free package. Data frames can be converted to 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', or 'Typst' tables. The user interface is minimalist and easy to learn. The syntax is concise. 'HTML' tables can be customized using the flexible 'Bootstrap' framework, and 'LaTeX' code with the 'tabularray' package.

Tufte's Styles for R Markdown Documents

Provides R Markdown output formats to use Tufte styles for PDF and HTML output.

Visualizations of Distributions and Uncertainty

Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) < https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) , quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) , complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) , and fit curves with multiple uncertainty ribbons.