Extending 'dendrogram' Functionality in R

Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.

Create Interactive 3D Visualizations of Molecular Data

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.

Density Estimation and Visualization of 2D Scatter Plots

The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) , and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 . Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) .

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.

Visualisations for Generalized Additive Models

Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.

Visualising Sets of Ontological Terms

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

Visualization of Functional Analysis Data

Implementation of multilayered visualizations for enhanced graphical representation of functional analysis data. It combines and integrates omics data derived from expression and functional annotation enrichment analyses. Its plotting functions have been developed with an hierarchical structure in mind: starting from a general overview to identify the most enriched categories (modified bar plot, bubble plot) to a more detailed one displaying different types of relevant information for the molecules in a given set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).

Visualizing Pedigrees with 'ggplot2' and 'plotly'

Provides plotting functions for visualizing pedigrees and family trees. The package complements a behavior genetics package 'BGmisc' [Garrison et al. (2024) ] by rendering pedigrees using the 'ggplot2' framework. Features include support for duplicated individuals, complex mating structures, integration with simulated pedigrees, and layout customization. Due to the impending deprecation of kinship2, version 1.0 incorporates the layout helper functions from kinship2. The pedigree alignment algorithms are adapted from 'kinship2' [Sinnwell et al. (2014) ]. We gratefully acknowledge the original authors: Jason Sinnwell, Terry Therneau, Daniel Schaid, and Elizabeth Atkinson for their foundational work.

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.