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.

Wrapper for the Gnu Scientific Library

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

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.

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) .

Create and Explore Geographic Zoning Systems

Functions, documentation and example data to help divide geographic space into discrete polygons (zones). The functions are motivated by research into the merits of different zoning systems . A flexible 'ClockBoard' zoning system is provided, which breaks-up space by concentric rings and radial lines emanating from a central point. By default, the diameter of the rings grow according the triangular number sequence with the first 4 'doughnuts' (or 'annuli') measuring 1, 3, 6, and 10 km wide. These annuli are subdivided into equal segments (12 by default), creating the visual impression of a dartboard. Zones are labelled according to distance to the centre and angular distance from North, creating a simple geographic zoning and labelling system useful for visualising geographic phenomena with a clearly demarcated central location such as cities.

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.

Flexible Framework for Developing Spatial Interaction Models

Develop spatial interaction models (SIMs). SIMs predict the amount of interaction, for example number of trips per day, between geographic entities representing trip origins and destinations. Contains functions for creating origin-destination datasets from geographic input datasets and calculating movement between origin-destination pairs with constrained, production-constrained, and attraction-constrained models (Wilson 1979) .

Create, Read, Write, and Work with 'iCalander' Files, Calendars and Scheduling Data

Provides function to create, read, write, and work with 'iCalander' files (which typically have '.ics' or '.ical' extensions), and the scheduling data, calendars and timelines of people, organisations and other entities that they represent. 'iCalendar' is an open standard for exchanging calendar and scheduling information between users and computers, described at < https://icalendar.org/>.

Bayesian Emulation of Computer Programs

Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently.