A project template to support the data science workflow.

The main function archetypes implements a
    framework for archetypal analysis supporting arbitrary
    problem solving mechanisms for the different conceptual
    parts of the algorithm.

Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, <DOI:10.1007/s11104-009-0005-2>), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, <DOI:10.1104/pp.114.253625>). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, <DOI:10.1007/s11104-015-2673-4>.

Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025).

Bindings to 'libarchive' <http://www.libarchive.org> the
    Multi-format archive and compression library. Offers R connections and
    direct extraction for many archive formats including 'tar', 'ZIP',
    '7-zip', 'RAR', 'CAB' and compression formats including 'gzip',
    'bzip2', 'compress', 'lzma' and 'xz'.

Scraping content from archived web pages stored in
    the 'Internet Archive' (<https://archive.org>) using a systematic
    workflow.  Get an overview of the mementos available from the
    respective homepage, retrieve the Urls and links of the page and
    finally scrape the content. The final output is stored in tibbles,
    which can be then easily used for further analysis.

Data exploration and modelling is a process in which a lot of data
    artifacts are produced. Artifacts like: subsets, data aggregates, plots,
    statistical models, different versions of data sets and different versions
    of results. The more projects we work with the more artifacts are produced
    and the harder it is to manage these artifacts. Archivist helps to store
    and manage artifacts created in R. Archivist allows you to store selected
    artifacts as a binary files together with their metadata and relations.
    Archivist allows to share artifacts with others, either through shared
    folder or github. Archivist allows to look for already created artifacts by
    using it's class, name, date of the creation or other properties. Makes it
    easy to restore such artifacts. Archivist allows to check if new artifact
    is the exact copy that was produced some time ago. That might be useful
    either for testing or caching.