Implements robust median-based Bayesian growth curve models
    that handle Missing Completely at Random (MCAR), Missing At Random (MAR),
    and Missing Not At Random (MNAR) missing-data mechanisms, and allow
    auxiliary variables. Models are fitted via 'rjags' (interface to 'JAGS')
    and summarized with 'coda'.

Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.

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.

Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.

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>.