It allows running 'EViews' (<https://eviews.com>) program from R, R Markdown and Quarto documents. 'EViews' (Econometric Views) is a statistical software for Econometric analysis.  This package integrates 'EViews' and R and also serves as an 'EViews' Knit-Engine for 'knitr' package. Write all your 'EViews' commands in R, R Markdown or Quarto documents. For details,  please consult our peer-review article Mati S., Civcir I. and Abba S.I (2023) <doi:10.32614/RJ-2023-045>.

Checks to see whether a supplied set of dice (their face values) are transitive, returning pair-win and group-roll win probabilities. Expected returns (mean magnitude of win/loss) are presented as well.

Allows users to model and draw inferences from extreme value inflated count data, and to evaluate these models and compare to non extreme-value inflated counterparts. The package is built to be compatible with standard presentation tools such as 'broom', 'tidy', and 'modelsummary'.

Functions for extreme value theory, which may be 
  divided into the following groups; exploratory data analysis, 
  block maxima, peaks over thresholds (univariate and bivariate), 
  point processes, gev/gpd distributions.

Methods to deal with the free antiassociative algebra
      over the reals with an arbitrary number of indeterminates.
      Antiassociativity means that (xy)z = -x(yz).  Antiassociative
      algebras are nilpotent with nilindex four (Remm, 2022,
      <doi:10.48550/arXiv.2202.10812>) and this drives the design and
      philosophy of the package.  Methods are defined to create and
      manipulate arbitrary elements of the antiassociative algebra,
      and to extract and replace coefficients.  A vignette is
      provided.

Performs likelihood-based extreme value inferences with 
    adjustment for the presence of missing values based on Simpson and 
    Northrop (2026) <doi:10.1002/env.70075>. A Generalised Extreme Value
    distribution is fitted to block maxima using maximum likelihood estimation, 
    with the location and scale parameters reflecting the numbers of 
    non-missing raw values in each block. A Bayesian version is also provided. 
    For the purposes of comparison, there are options to make no adjustment for 
    missing values or to discard any block maximum for which greater than a 
    percentage of the underlying raw values are missing. Example datasets 
    containing missing values are provided.