Data Sets from the History of Statistics and Data Visualization

The 'HistData' package provides a collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R.

Explore and Visualize Your Data Interactively

A 'shiny' gadget to create 'ggplot2' figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.

3D Brain Visualization

A fast, interactive cross-platform, and easy to share 'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or 'AFNI/SUMA' surfaces. The viewer widget can be either standalone or embedded into 'R-shiny' applications. The standalone version only require a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 'Safari'), and can be inserted into any websites. The 'R-shiny' support allows the 3D viewer to be dynamically generated from reactive user inputs. This feature has been fully adopted by 'RAVE' < https://openwetware.org/wiki/RAVE>, an interactive toolbox to analyze 'iEEG' data. Documentation about 'threeBrain' is provided by < https://dipterix.org/threeBrain/> and several vignettes included in this package. To cite the package, please check our 'NeuroImage' paper by Magnotti, Wang, and Beauchamp (2020, ), or see 'citation("threeBrain")' for details.

Atmospheric Thermodynamics and Visualization

Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.

Various Functions to Facilitate Visualization of Data and Analysis

When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.

Statistical Rank Aggregation: Inference, Evaluation, and Visualization

A set of methods to implement Generalized Method of Moments and Maximal Likelihood methods for Random Utility Models. These methods are meant to provide inference on rank comparison data. These methods accept full, partial, and pairwise rankings, and provides methods to break down full or partial rankings into their pairwise components. Please see Generalized Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of some of our methods.

Credible Visualization for Two-Dimensional Projections of Data

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in .

Ridgeline Plots in 'ggplot2'

Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.

Interactive Visualization of Topic Models

Tools to create an interactive web-based visualization of a topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). Given the estimated parameters of the topic model, it computes various summary statistics as input to an interactive visualization built with D3.js that is accessed via a browser. The goal is to help users interpret the topics in their LDA topic model.

Visualising Sets of Ontological Terms

Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 .