Tools for Managing Classes on GitHub

Interface for the GitHub API that enables efficient management of courses on GitHub. It has a functionality for managing organizations, teams, repositories, and users on GitHub and helps automate most of the tedious and repetitive tasks around creating and distributing assignments.

Interface to Geometry Engine - Open Source ('GEOS')

Interface to Geometry Engine - Open Source ('GEOS') using the C 'API' for topology operations on geometries. Please note that 'rgeos' will be retired during October 2023, plan transition to 'sf' or 'terra' functions using 'GEOS', or the 'geos' package, at your earliest convenience (see < https://r-spatial.org/r/2023/05/15/evolution4.html> and earlier blogs for guidance). The 'GEOS' library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on 'CRAN'. ('rgeos' >= 0.5-1): Up to and including 'GEOS' 3.7.1, topological operations succeeded with some invalid geometries for which the same operations fail from and including 'GEOS' 3.7.2. The 'checkValidity=' argument defaults and structure have been changed, from default FALSE to integer default '0L' for 'GEOS' < 3.7.2 (no check), '1L' 'GEOS' >= 3.7.2 (check and warn). A value of '2L' is also provided that may be used, assigned globally using 'set_RGEOS_CheckValidity(2L)', or locally using the 'checkValidity=2L' argument, to attempt zero-width buffer repair if invalid geometries are found. The previous default (FALSE, now '0L') is fastest and used for 'GEOS' < 3.7.2, but will not warn users of possible problems before the failure of topological operations that previously succeeded. From 'GEOS' 3.8.0, repair of geometries may also be attempted using 'gMakeValid()', which may, however, return a collection of geometries of different types.

Statistical Inference on Lineup Fairness

Computes statistical measures for laboratory and police lineups in forensic psychology, including lineup bias, functional and effective size, diagnosticity, confidence-accuracy curves, signal detection models, and simulation tools. Optional face-similarity functions use numeric face representations computed by Python's 'deepface' library; optional lineup-memory model fitting uses a separately installed, pinned 'pyWitness' engine.

A 'Neo4J' Driver

A Modern and Flexible 'Neo4J' Driver, allowing you to query data on a 'Neo4J' server and handle the results in R. It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the way it returns the results, by trying to stay as close as possible to the way 'Neo4J' returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the "heavy lifting" of data wrangling is not left to the user.

Functions Related to ICES Advice

A collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics.

Pain Assessment at Withdrawal Speeds (PAWS)

Automated pain scoring from paw withdrawal tracking data. Based on Jones et al. (2020) "A machine-vision approach for automated pain measurement at millisecond timescales" .

String Distance Calculation with Tidy Data Principles

Calculation of string distance following the tidy data principles. Built on top of the 'stringdist' package.

Gillespie's Stochastic Simulation Algorithm for Impatient People

A fast, scalable, and versatile framework for simulating large systems with Gillespie's Stochastic Simulation Algorithm ('SSA'). This package is the spiritual successor to the 'GillespieSSA' package originally written by Mario Pineda-Krch. Benefits of this package include major speed improvements (>100x), easier to understand documentation, and many unit tests that try to ensure the package works as intended. Cannoodt and Saelens et al. (2021) .

Generate Fake Datasets for Prototyping and Teaching

Create fake datasets that can be used for prototyping and teaching. This package provides a set of functions to generate fake data for a variety of data types, such as dates, addresses, and names. It can be used for prototyping (notably in 'shiny') or as a tool to teach data manipulation and data visualization.

Lorem-Ipsum-Like Helpers for Fast Shiny Prototyping

Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers.