A scripting and command-line front-end
 is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
 the GNU R language and environment for statistical computing and graphics.
 While R can be used in batch mode, the r binary adds full support for
 both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
 expression as the first line in scripts) as well as command-line use in
 standard Unix pipelines. In other words, r provides the R language without
 the environment.

Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Akdeniz and Kaciranlar (1995) \doi{10.1080/03610929508831585}
  ii.  Druilhet and Mom (2008) \doi{10.1016/j.jmva.2006.06.011}
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Liu (1993) \doi{10.1080/03610929308831027}
  v.   Liu (2001) \doi{10.1016/j.jspi.2010.05.030}.

Interpretability of complex machine learning models is a growing concern.
    This package helps to understand key factors that drive the 
    decision made by complicated predictive model (so called black box model). 
    This is achieved through local approximations that are either based on 
    additive regression like model or CART like model that allows for 
    higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
    More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

Creates shareable links for 'R' code in 'WebAssembly' (WASM)
    Read-Eval-Print Loop (REPL) environments like 'webR'
    <https://webr.r-wasm.org/> and for 'Shiny' applications using
    'Shinylive' <https://shinylive.io/>. Supports single scripts,
    multi-file projects, exercise and solution pairs, and batch
    processing. Includes encoding, decoding, and previewing of links
    for both 'R' and 'Python' environments.

Provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book <https://book-data-science-r.netlify.app>, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge.