Many modern C/C++ development tools in the 'clang' toolchain,
  such as 'clang-tidy' or 'clangd', rely on the presence of a compilation database
  in JSON format <https://clang.llvm.org/docs/JSONCompilationDatabase.html>.
  This package temporarily injects additional build flags into the R build
  process to generate such a compilation database.

Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user.  Also fits the user specified distribution to a given data set.  More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017)  CompDist: Multisection composite distributions.

Compute ranking and rating based on competition
    results. Methods of different nature are implemented: with fixed
    Head-to-Head structure, with variable Head-to-Head structure and with
    iterative nature. All algorithms are taken from the book 'Who’s #1?:
    The science of rating and ranking' by Amy N. Langville and Carl D.
    Meyer (2012, ISBN:978-0-691-15422-0).

Tools for storing and managing competition results.
    Competition is understood as a set of games in which players gain some
    abstract scores.  There are two ways for storing results: in long (one
    row per game-player) and wide (one row per game with fixed amount of
    players) formats. This package provides functions for creation and
    conversion between them. Also there are functions for computing their
    summary and Head-to-Head values for players. They leverage grammar of
    data manipulation from 'dplyr'.

A graphical user interface for simulating the effects of mergers, tariffs, and quotas under an
             assortment of different economic models. The interface is powered by the 'Shiny' web application framework from 
             'RStudio'.

In computer experiments space-filling designs are having great impact. Most popularly used space-filling designs are Uniform designs (UDs), Latin hypercube designs (LHDs) etc. For further references one can see Mckay (1979) <DOI:10.1080/00401706.1979.10489755> and Fang (1980) <https://cir.nii.ac.jp/crid/1570291225616774784>. In this package, we have provided algorithms for generate efficient LHDs and UDs. Here, generated LHDs are efficient as they possess lower value of Maxpro measure, Phi_p value and Maximum Absolute Correlation (MAC) value based on the weightage given to each criterion. On the other hand, the produced UDs are having good space-filling property as they always attain the lower bound of Discrete Discrepancy measure. Further, some useful functions added in this package for adding more value to this package.