Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included.

Grammar of Graphics and Plot Helpers for Time Series Visualization

Extends the capabilities of 'ggplot2' by providing grammatical elements and plot helpers designed for visualizing temporal patterns. The package implements a grammar of temporal graphics, which leverages calendar structures to highlight changes over time. The package also provides plot helper functions to quickly produce commonly used time series graphics, including time plots, season plots, and seasonal sub-series plots.

Analysis and Visualization of Macroevolutionary Dynamics on Phylogenetic Trees

Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more.

Create Maps and Visualize Data in 2D and 3D

Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format.

Tools to Visualize, Manipulate, and Summarize MCMC Output

Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with Stan, NIMBLE, JAGS, and other software.

Document Conversion to 'PDF', Thumbnails and Visual Testing

Provides the ability to generate images from documents of different types. Three main features are provided: generating document thumbnails, performing visual tests of documents, and updating fields and tables of contents of a 'Microsoft Word' or 'RTF' document. 'Microsoft Word' and/or 'LibreOffice' must be installed on the machine. If 'Microsoft Word' is available, it can produce PDF documents or images identical to the originals; otherwise 'LibreOffice' is used and the rendering may sometimes differ from the original documents.

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.

Performance Assessment of Binary Classifier with Visualization

Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.

Some Useful Functions for Statistics and Visualization

Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.

Visualization via Beanplots (Like Boxplot/Stripchart/Violin Plot)

Plots univariate comparison graphs, an alternative to boxplot/stripchart/violin plot.