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Mehrad
@mehrad.mahmoudian

I work on various OpenSource projects in different languages, but mostly R and C++

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Description

I work on various OpenSource projects in different languages, but the code I'm mostly involved in are in R, C++, Bash, Python. The main projects that I'm evolved in are the following, but for the full list, you should go to every forge I am in and check the repos out:

  • varhandle: R package which contains some QoL (quality of life) functions to deal with variables easier in R

  • sivs: R package for robust feature selection on high-dimensional data

  • Flameshot: Cross-platform screenshot tool [C++]

  • r-mgr: A CLI tool to manage various R versions on the same Linux machine [Bash]

  • highlighter: A CLI tool to highlight text on the fly and through pipe chain based on regex [Rust]

I also have my own Emacs config which I actively try to make it as a good entry point for new Emacs users, and it has quite an extensive documentation. This has somewhat organically grown while I myself am learning Emacs. :)

I'm also in the process of learning Guile Scheme to also have an easy setup for R projects to be 100% reproducible using Guix containers. This is a project that is practically done and I'm using it in daily basis, and needs some more documentation and some scheme code to handle some rough edges.

More about me on my website: mehrad.ai

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Repositories

R_helper_scripts Stars 0 Updated 1 month ago

This repository contains some scripts to help to work with R easier. Enjoy them and create pull requests if you manage to improve them. Migrated to https://codeberg.org/mehrad/r-mgr

coding solutions for OEIS Stars 0 Updated 5 years ago

This repo contains the code and perhaps the results of those codes for the integer series in OEIS (Online Encyclopedia of Integer Sequences). This repo is not affiliated with the OEIS.org and does not claim to have correct codes.

sivs Stars 4 Updated 4 months ago

An iterative feature selection method that internally utilizes varius Machine Learning methods that have embeded feature reduction in order to shrink down the feature space into a small and yet robust set.

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History

mehrad.mahmoudian joined 6 years ago.

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