Reproducible Data Science Environments with 'Nix'

Simplifies the creation of reproducible data science environments using the 'Nix' package manager, as described in Dolstra (2006) . The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using 'Nix'. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools. Additional helpers make it easy to run R code in 'Nix' software environments for testing and production.

Interactive, Complex Heatmaps

Make complex, interactive heatmaps. 'iheatmapr' includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.

Control 'BEAST2'

'BEAST2' (< https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is commonly accompanied by 'BEAUti 2', 'Tracer' and 'DensiTree'. 'babette' provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from 'R'.

'IUCN' Red List Client

'IUCN' Red List (< https://api.iucnredlist.org/>) client. The 'IUCN' Red List is a global list of threatened and endangered species. Functions cover all of the Red List 'API' routes. An 'API' key is required.

Tools for Spell Checking in R

Spell checking common document formats including latex, markdown, manual pages, and description files. Includes utilities to automate checking of documentation and vignettes as a unit test during 'R CMD check'. Both British and American English are supported out of the box and other languages can be added. In addition, packages may define a 'wordlist' to allow custom terminology without having to abuse punctuation.

Generate Raster Images from QuadKey-Identified Datasets

A set of functions of increasing complexity allows users to (1) convert QuadKey-identified datasets, based on 'Microsoft's Bing Maps Tile System', into Simple Features data frames, (2) transform Simple Features data frames into rasters, and (3) process multiple 'Meta' ('Facebook') QuadKey-identified human mobility files directly into raster files. For more details, see D’Andrea et al. (2024) .

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) .

Cast '(R)Markdown' Files to 'XML' and Back Again

Parsing '(R)Markdown' files with numerous regular expressions can be fraught with peril, but it does not have to be this way. Converting '(R)Markdown' files to 'XML' using the 'commonmark' package allows in-memory editing via of 'markdown' elements via 'XPath' through the extensible 'R6' class called 'yarn'. These modified 'XML' representations can be written to '(R)Markdown' documents via an 'xslt' stylesheet which implements an extended version of 'GitHub'-flavoured 'markdown' so that you can tinker to your hearts content.

Querying and Managing Large Biodiversity Occurrence Datasets

Facilitates the gathering of biodiversity occurrence data from disparate sources. Metadata is managed throughout the process to facilitate reporting and enhanced ability to repeat analyses.

'NOAA' Weather Data from R

Client for many 'NOAA' data sources including the 'NCDC' climate 'API' at < https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for each of the 'API' 'endpoints': data, data categories, data sets, data types, locations, location categories, and stations. In addition, we have an interface for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS', tornado data via the 'NOAA' storm prediction center, and more.