R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, data mining surveys and studies of scholarly literature databases show substantial increases in popularity in recent years. As of November 2019, R ranks 16th in the TIOBE index, a measure of popularity of programming languages. (Wikipedia)

All algorithms can be found in the DIRECTORY.md file ordered by folder.

Please ensure to follow the points stated below if you would like to contribute:

-Ensure that your code is functional and well-structured before submitting a pull request.

-The code should run without errors in an R environment and produce the expected output.

-Follow best practices for efficiency, readability, and maintainability.

-Verify that the algorithm is not already implemented in the repository.

-Include a brief explanation of the algorithm in the file as comments.

-Add an example showcasing its usage (it can be commented within the script).

-If making improvements, clearly document the changes in your pull request description.

-Ensure that your modifications do not break existing functionality.

-If applicable, update or add test cases to validate your changes.

-Use consistent and meaningful variable names. Follow one of these naming conventions:

-Use . or _ to separate words (e.g., results.df for a data frame).

-Ensure that filenames follow the existing directory structure and naming patterns.

-Provide clear and concise documentation in the form of comments within the code.

-Add a brief docstring at the beginning of the script explaining:

-What the algorithm does

-The expected input and output

-Any dependencies required

-Before submitting a pull request, verify that your code:

-Runs correctly with different test cases

-Does not produce unnecessary warnings or errors

-If applicable, add a test file demonstrating the algorithm’s correctness.