Interface to 'Phylocom'

Interface to 'Phylocom' (< https://phylodiversity.net/phylocom/>), a library for analysis of 'phylogenetic' community structure and character evolution. Includes low level methods for interacting with the three executables, as well as higher level interfaces for methods like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more.

Co-Data Learning for Bayesian Additive Regression Trees

Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) for details.

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.

Correcting Drift and Carry-over in Continuous Isotopic Measurements

A series of functions that allow an easy and fast correction for drift and carry-over in continuous isotopic measurements. This implementation provides queries allowing users to perform the implemented corrections according to their needs. These functions further enable the processing of large datasets and can provides apt visualizations of the corrections performed.

Generate 'CodeMeta' Metadata for R Packages

The 'Codemeta' Project defines a 'JSON-LD' format for describing software metadata, as detailed at < https://codemeta.github.io>. This package provides utilities to generate, parse, and modify 'codemeta.json' files automatically for R packages, as well as tools and examples for working with 'codemeta.json' 'JSON-LD' more generally.

Fused Partitioned Regression for Clinical and Omics Data

Fit (generalized) linear regression models in each leaf node of a tree. The tree is constructed using clinical variables only. The linear regression models are constructed using (high-dimensional) omics variables only. The leaf-node-specific regression models are estimated using the penalized likelihood including a standard ridge (L2) penalty and a fusion penalty that links the leaf-node-specific regression models to one another. The intercepts of the leaf nodes reflect the effects of the clinical variables and are left unpenalized. The tree, fitted with the clinical variables only, should be constructed outside of the package with the 'rpart' 'R' package. See Goedhart and others (2024) for details on the method.

RDF Library Bindings in R

Provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). RDF is described at < https://www.w3.org/TR/rdf-primer/>. This package supports RDF by implementing an R interface to the Redland RDF C library, described at < https://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.

Programming with Big Data -- Interface to 'ZeroMQ'

'ZeroMQ' is a well-known library for high-performance asynchronous messaging in scalable, distributed applications. This package provides high level R wrapper functions to easily utilize 'ZeroMQ'. We mainly focus on interactive client/server programming frameworks. For convenience, a minimal 'ZeroMQ' library (4.2.2) is shipped with 'pbdZMQ', which can be used if no system installation of 'ZeroMQ' is available. A few wrapper functions compatible with 'rzmq' are also provided.

R Bindings for Calling the 'Earth Engine' API

Earth Engine < https://earthengine.google.com/> client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See < https://r-spatial.github.io/rgee/> for further details.

Orchestrate Geospatial (Meta)Data Management Workflows and Manage FAIR Services

An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with 'FAIR' (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the 'DublinCore' standard (< https://dublincore.org/>), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with 'ISO/TC211' (< https://committee.iso.org/home/tc211>) and 'OGC' (< https://www.ogc.org/standards/>) geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. 'INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. 'PostGIS'), metadata catalogues (eg. 'GeoNetwork', 'CSW' servers), data servers (eg. 'GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of 'EML' metadata, and their management with 'DataOne' servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of 'CF' conventions, 'NetCDF' data format and 'OPeNDAP' access protocol, and their management with 'Thredds' servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', 'DataCite'), to facilitate the publication of data within (meta)data repositories such as 'Zenodo' (< https://zenodo.org>) or DataVerse (< https://dataverse.org/>). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference 'Zenodo' 'DOI' in 'GeoNetwork'/'GeoServer' metadata, or vice versa reference 'GeoNetwork'/'GeoServer' links in 'Zenodo' or 'EML' metadata). The use of standardized configuration files ('JSON' or 'YAML' formats) allow fully reproducible workflows to facilitate the work of data and information managers.