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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)
Visualize Data on Spirals
It visualizes data along an Archimedean spiral < https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.
Visualizing Panel Data
Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see
A Collection of Database, Data Structure, Visualization, and Utility Functions for R
The caroline R library contains dozens of functions useful for: database migration (dbWriteTable2), database style joins & aggregation (nerge, groupBy, & bestBy), data structure conversion (nv, tab2df), legend table making (sstable & leghead), automatic legend positioning for scatter and box plots (), plot annotation (labsegs & mvlabs), data visualization (pies, sparge, confound.grid & raPlot), character string manipulation (m & pad), file I/O (write.delim), batch scripting, data exploration, and more. The package's greatest contributions lie in the database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions.
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)
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.
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.
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.
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.
Visualising Sets of Ontological Terms
Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017