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.

Two main functionalities are provided. One of them is predicting values with 
    k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm.
    These are carried out in parallel using multiple threads.

An interactive document on  the topic of K-nearest neighbour (KNN) using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/KNNShiny/>.