An automated graphical exploratory data analysis (EDA) tool that introduces: 
  a.) wideplot graphics for exploring the structure of a dataset through a grid of variables 
      and graphic types. 
  b.) longplot graphics, which present the entire catalog of available graphics for representing 
      a particular variable using a grid of graphic types and variations on these types.
  c.) plotup function, which presents a particular graphic for a specific variable of a dataset. 
      The plotup() function also makes it possible to obtain the code used to generate the graphic, 
      meaning that the user can adjust its properties as needed.
  d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships
      between all pairs of variables of a certain(s) type(s) in a multivariate data set.

Functions to handle basic input output, these functions
    always read and write UTF-8 (8-bit Unicode Transformation Format)
    files and provide more explicit control over line endings.

An interface to the 'Briq' API <https://briq.github.io>. 'Briq' is
    a tool that aims to promote employee engagement by helping employees
    recognize and reward each other. Employees can praise and thank one another
    (for achieving a company goal, for example) by giving virtual credits (known
    as 'briqs' or 'bqs') that can be redeemed for various rewards. The 'Briq'
    API lets you create, read, update and delete users, user groups,
    transactions and messages. This package provides functions that simplify
    getting the users, user groups and transactions of your organization into R.

Fits bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest neighbor Gaussian processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.

Quantitative methods for benefit-risk analysis help to condense
    complex decisions into a univariate metric describing the overall benefit
    relative to risk.  One approach is to use the multi-criteria decision
    analysis framework (MCDA), as in Mussen, Salek, and Walker
    (2007) <doi:10.1002/pds.1435>.  Bayesian benefit-risk
    analysis incorporates uncertainty through posterior distributions which are
    inputs to the benefit-risk framework.  The brisk package provides functions
    to assist with Bayesian benefit-risk analyses, such as MCDA.
    Users input posterior samples, utility functions, weights, and the package
    outputs quantitative benefit-risk scores.  The posterior of the benefit-risk
    scores for each group can be compared.  Some plotting capabilities are also
    included.