- Caret - Aims to be a unified interface for hyper-parameter tuning, classification, and predictive modelling,
can substantially reduce startup time for using a new technique by providing consistent model specification.
I'm uncertain when it's advantageous not to use Caret.
- Kernlab - A rich assortment of kernel based methods for machine learning. I can't find a good into
page for this, so the official page will have to stand-in
- RandomForest - The canonical implementation of Breiman's RandomForest algorithms, ported from the original
fortran. There are many updates/extensions to the algorithm but this one is a natural choice.
- Xgboost - Do some extreme gradient boosting
Writing short code bits is pretty straight-forward in R, but serious development is a different ball game
- Devtools - The essentially must have set of developer tools for R. Allows installation from github and
much, much more.
- Testthat - Nice code testing framework to make sure your code does what you think it does
- Argparse - Graceful argument handling for your scripts. R port of python's argparse package