The sampl.mcmc function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints.

Given a set of points around a knee curve,
    analyzes first and second derivatives to find knee points.

Provides the ability to create dynamic citations
    in which the bibliographic information is pulled from the web rather
    than having to be entered into a local database such as 'bibtex' ahead of
    time. The package is primarily aimed at authoring in the R 'markdown'
    format, and can provide outputs for web-based authoring such as linked
    text for inline citations.  Cite using a 'DOI', URL, or
    'bibtex' file key.  See the package URL for details.

Provides a general-purpose tool for dynamic report generation in R
    using Literate Programming techniques.

A framework to create Bootstrap <https://getbootstrap.com/> HTML reports from 'knitr'
    'rmarkdown'.

Implements a data language engine for incorporating data directly in 
    'rmarkdown' documents so that they can be made completely standalone.

Provides a progress bar similar to 'dplyr' that can write progress out to a 
    variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown',
    and you still want to see progress of calculations in the terminal.

Convert an R Markdown documents into an '.xlsx' spreadsheet reports
    with the knitxl() function, which works similarly to knit() from the 
    'knitr' package. The generated report can be opened in 'Excel' or similar
    software for further analysis and presentation.

Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix.
    The implementation is based on cover trees introduced by
    Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.

This is a 'C++' mutual information (MI) library based on the k-nearest 
    neighbor (KNN) algorithm. There are three functions provided for computing MI 
    for continuous values, mixed continuous and discrete values, and conditional MI 
    for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) <doi:10.1103/PhysRevE.69.066138>, 
    BC Ross (2014)<doi:10.1371/journal.pone.0087357>, 
    and A. Tsimpiris (2012) <doi:10.1016/j.eswa.2012.05.014>, respectively.