Provides functions that offer seamless 'D3Plus' integration. The examples provided here are taken from the official 'D3Plus' website <http://d3plus.org>.

Apache licensed alternative to 'Highcharter' which provides 
  functions for both fast and beautiful interactive visualization for 'Markdown'
  and 'Shiny'.

Provides a suite of functions to help ease the use of 'd3.js' in R.
              These helpers include 'htmltools::htmlDependency' functions, hierarchy
              builders, and conversion tools for 'partykit', 'igraph,' 'table',
              and 'data.frame' R objects into the 'JSON' that 'd3.js' expects.

Create and customize interactive collapsible 'D3' trees using the 'D3'
    JavaScript library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
    When in Shiny the tree layout is observed by the server and can be used as a reactive filter
    of structured data.

Provides an interface to 'D4Science' 'StorageHub' API (<https://dev.d4science.org/>). Allows to get user profile, and perform 
 actions over the 'StorageHub' (workspace) including creation of folders, files management (upload/update/deletion/sharing), and listing of 
 stored resources.

Tools, methods and processes for the management 
  of analysis workflows. These lightweight solutions facilitate 
  structuring R&D activities. These solutions were developed to comply with FAIR 
  principles as discussed by Jacobsen et al. (2017) <doi:10.1162/dint_r_00024>, 
  and with ALCOA+ principles as proposed by the U.S. FDA.

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.