Formula-Driven Table Generation

Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.

Report Functions to Create HTML and PDF Files

Create and combine HTML and PDF reports from within R. Possibility to design tables and listings for reporting and also include R plots.

Combined Slider and Numeric Input for 'Shiny'

Provides a combined slider and numeric input for usage in a 'Shiny' app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).

Improved Text Rendering Support for 'Grid' Graphics

Provides support for rendering of formatted text using 'grid' graphics. Text can be formatted via a minimal subset of 'Markdown', 'HTML', and inline 'CSS' directives, and it can be rendered both with and without word wrap.

Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready

Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents. Details can be found in Arel-Bundock (2022) .

Extending 'gt' for Beautiful HTML Tables

Provides additional functions for creating beautiful tables with 'gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions.

Turn HTML 'Shiny'

Contains functions for converting existing HTML/JavaScript source into equivalent 'shiny' functions. Bootstraps the process of making new 'shiny' functions by allowing us to turn HTML snippets directly into R functions.

moDel Agnostic Language for Exploration and eXplanation

Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) < https://jmlr.org/papers/v19/18-416.html>.

HTML Writer - Outputs R Objects in HTML Format

Easy-to-use and versatile functions to output R objects in HTML format.

Tools to Quickly and Neatly Summarize Data

Data frame summaries, cross-tabulations, weight-enabled frequency tables and common descriptive (univariate) statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.