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Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, and recoding variables.

Geographic Data Analysis and Modeling

Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported. There is a also support for vector data operations such as intersections. See the manual and tutorials on < https://rspatial.org/> to get started.

Display and Analyze ROC Curves

Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

Export Tables to LaTeX or HTML

Coerce data to LaTeX and HTML tables.

Polynomial Spline Routines

Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.

Color Palettes for EPL, MLB, NBA, NHL, and NFL Teams

Color palettes for EPL, MLB, NBA, NHL, and NFL teams.

Trust Region Optimization

Does local optimization using two derivatives and trust regions. Guaranteed to converge to local minimum of objective function.

Routines for Logspline Density Estimation

Contains routines for logspline density estimation.
The function oldlogspline() uses the same algorithm as the logspline package
version 1.0.x; i.e. the Kooperberg and Stone (1992)
algorithm (with an improved interface). The recommended routine logspline()
uses an algorithm from Stone et al (1997)

Making Visual Exploratory Data Analysis with Nested Data Easier

Functions for making visual exploratory data analysis with nested data easier.

Manage Massive Matrices with Shared Memory and Memory-Mapped Files

Create, store, access, and manipulate massive matrices. Matrices are allocated to shared memory and may use memory-mapped files. Packages 'biganalytics', 'bigtabulate', 'synchronicity', and 'bigalgebra' provide advanced functionality.