Execute  'FOCAL' (<https://en.wikipedia.org/wiki/FOCAL_(programming_language)>) source code directly in 'R'. This is achieved by translating 'FOCAL' code into equivalent 'R' commands and controlling the sequence of execution.

Allows developers to work with many R folders inside a package. It offers functionalities to transfer R scripts (saved outside the R folder) into 
    the R folder while making additional checks.

The basic algorithm to perform the folding test of unimodality. 
  Given a dataset X (d dimensional, n samples), the test checks whether the 
  distribution of the data are rather unimodal or rather multimodal. This 
  package stems from the following research publication: 
  Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. 
  "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International 
  Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.
  <doi:10.1145/3219819.3219994>.

Contains all the code examples in the book "R for Dummies" (2nd
    edition) by Andrie de Vries and Joris Meys. You can view the table of 
    contents as well as the sample code for each chapter.

Provides fast implementations of Random Forests, 
    Gradient Boosting, and Linear Random Forests, with an emphasis on inference 
    and interpretability. Additionally contains methods for variable 
    importance, out-of-bag prediction, regression monotonicity, and
    several methods for missing data imputation.

The R Formatter formats R source code. It is very much based on
    formatR, but tries to improve it by heuristics. For example, spaces can be
    forced around the division operator "/".

Estimate significance of importance metrics
    for a Random Forest model by permuting the response
    variable. Produces null distribution of importance
    metrics for each predictor variable and p-value of
    observed. Provides summary and visualization functions for 'randomForest' 
    results.

A machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.