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Signal Processing
A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions.
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
Visualizing Pedigrees with 'ggplot2' and 'plotly'
Provides plotting functions for visualizing pedigrees in behavior genetics and kinship research. The package complements 'BGmisc' [Garrison et al. (2024)
Accelerating 'ggplot2'
The aim of 'ggplot2' is to aid in visual data investigations. This focus has led to a lack of facilities for composing specialised plots. 'ggforce' aims to be a collection of mainly new stats and geoms that fills this gap. All additional functionality is aimed to come through the official extension system so using 'ggforce' should be a stable experience.
Network Dynamic Temporal Visualizations
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.
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.
Self Calibrating Quantile-Quantile Plots for Visual Testing
Provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution.
Visualisation of Sequential Probability Distributions Using Fan Charts
Visualise sequential distributions using a range of plotting
styles. Sequential distribution data can be input as either simulations or
values corresponding to percentiles over time. Plots are added to
existing graphic devices using the fan function. Users can choose from four
different styles, including fan chart type plots, where a set of coloured
polygon, with shadings corresponding to the percentile values are layered
to represent different uncertainty levels. Full details in R Journal article; Abel (2015)
Perceptual Analysis, Visualization and Organization of Spectral Colour Data
A cohesive framework for the spectral and spatial analysis of
colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013)