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.

Wrapper for the Gnu Scientific Library

An R wrapper for some of the functionality of the Gnu Scientific Library.

Create and Investigate Magic Squares

A collection of functions for the manipulation and analysis of arbitrarily dimensioned arrays. The original motivation for the package was the development of efficient, vectorized algorithms for the creation and investigation of magic squares and high-dimensional magic hypercubes.

Manipulate and Map Origin-Destination Data

The aim of 'od' is to provide tools and example datasets for working with origin-destination ('OD') datasets of the type used to describe aggregate urban mobility patterns (Carey et al. 1981) . The package builds on functions for working with 'OD' data in the package 'stplanr', (Lovelace and Ellison 2018) with a focus on computational efficiency and support for the 'sf' class system (Pebesma 2018) . With few dependencies and a simple class system based on data frames, the package is intended to facilitate efficient analysis of 'OD' datasets and to provide a place for developing new functions. The package enables the creation and analysis of geographic entities representing large scale mobility patterns, from daily travel between zones in cities to migration between countries.

Additive Partitions of Integers

Additive partitions of integers. Enumerates the partitions, unequal partitions, and restricted partitions of an integer; the three corresponding partition functions are also given. Set partitions and now compositions and riffle shuffles are included.

Sustainable Transport Planning

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) , but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) . The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as < https://cyclestreets.net/> (Desjardins et al. 2021) ; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) and the 'OD jittering' method described in Lovelace et al. (2022) . Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) , and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) .

The Gauss Hypergeometric Function

The Gaussian hypergeometric function for complex numbers.

Propensity to Cycle Tool

Functions and example data to teach and increase the reproducibility of the methods and code underlying the Propensity to Cycle Tool (PCT), a research project and web application hosted at < https://www.pct.bike/>. For an academic paper on the methods, see Lovelace et al (2017) .

Miscellaneous Useful Functions for Statistics

A series of functions in some way considered useful to the author. These include methods for subsetting tables and generating indices for arrays, conditioning and intervening in probability distributions, generating combinations, fast transformations, and more...

Very Large Numbers in R

Very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. Functionality for complex numbers is also provided. The package includes a vignette that gives a step-by-step introduction to using S4 methods.