R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Firely.

A simple approach to configuring R projects with different
  parameter values. Configurations are specified using a reduced subset of base
  R and parsed accordingly.

An HTTP web server for R with a documented API to interface between R and the server. The documentation contains the Rook specification and details for building and running Rook applications. To get started, be sure and read the 'Rook' help file first.

Statistical distribution in OOP (Object Oriented Programming) way.
             This package proposes a R6 class interface to classic statistical
             distribution, and new distributions can be easily added with the
             class AbstractDist. A useful point is the generic fit() method for
             each class, which uses a maximum likelihood estimation to find the
             parameters of a dataset, see, e.g. Hastie, T. and al (2009)
             <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class
             gives a non-parametric fit, with the same accessors that for the
             classic distribution. Finally, three random generators useful to
             build synthetic data are given: a multivariate normal generator, an
             orthogonal matrix generator, and a symmetric positive definite
             matrix generator, see Mezzadri, F. (2007)
             <arXiv:math-ph/0609050>.

A set of tools to reconstruct ordered ontogenic trajectories from
    single cell RNAseq data.

Minirhizotrons are widely used to observe and explore roots and
    their growth. This package provides the means to stitch images and 
    divide them into depth layers.
    Please note that this R package was developed alongside the following 
    manuscript:
    Stitching root scans and extracting depth layer information -- a workflow 
    and practical examples, S. Kersting, L. Knüver, and M. Fischer.
    The manuscript is currently in preparation and should be citet as soon as 
    it is available.
    This project was supported by the project ArtIGROW, which is a part of the
    WIR!-Alliance ArtIFARM –  Artificial Intelligence in Farming funded by the
    German Federal Ministry of Research, Technology and Space (No. 03WIR4805).