This page provides overview descriptions of my R packages and those I have contributed to. The package logos link to the GitHub repositories. Documentation buttons take you to the pkgdown sites with full documentation and rendered vignettes.
For a complete list of all packages with full descriptions, see the GitHub packages.md page in my profile.
Multivariate Linear Models
Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions).
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Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an ‘mlm’.
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Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model, and methods to visualize those diagnostics including better tabular presentation and a “collinearity biplot”.
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Introduces generalizations of the standard univariate ridge trace plot used in ridge regression. These graphical methods show both bias (shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients.
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Computes regression deletion diagnostics for multivariate linear models and provides associated diagnostic plots. Measures include hat-values (leverages), generalized Cook’s distance, and generalized squared studentized residuals.
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A collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. Functions are designed for tutorial purposes with clear demonstrations of algorithms.
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A ‘ggplot2’ based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance.
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Represents generalized geometric ellipsoids with the “(U,D)” representation. It allows degenerate and/or unbounded ellipsoids, together with methods for linear and duality transformations.
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Carries out analyses of two-way tables with one observation per cell, together with graphical displays for an additive fit and a diagnostic plot for removable ‘non-additivity’.
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Categorical Data Analysis
Provides additional data sets, methods and documentation to complement the ‘vcd’ package for Visualizing Categorical Data. This package is a support package for the book, “Discrete Data Analysis with R” by Michael Friendly and David Meyer.
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Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response. Nested dichotomies are statistically independent and provide an additive decomposition of tests.
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Data Packages
A collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal is to make these available for both instructional use and historical research.
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Maps of France in 1830, multivariate datasets from A.-M. Guerry and others. Facilitates exploration and development of statistical and graphic methods for multivariate data in a geospatial context.
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Generates random quotations from a database of quotes on topics in statistics, data visualization and science. Includes functions for searching quotes by tags or authors and creating word clouds.
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Provides the tables from the ‘Sean Lahman Baseball Database’ as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2024.
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Collects several classical word pools used most often to provide lists of words in psychological studies of learning and memory. It provides a simple function, ‘pickList’ for selecting random samples of words within given ranges.
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Implements stylistic elements (fonts, hachure patterns, color palettes) used by ‘Emile Cheysson’ in the ‘Albums de Statistique Graphique’, sometimes called the pinnacle of the Golden Age of Statistical Graphics.
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For a complete list of all packages with full descriptions, see the GitHub packages.md file in my profile.