This package is intended to serve two purposes:

1. A packaged implementation in Python of the knockoffs framework introduced by

framework introduced by Dai and Barber 2016 (https://arxiv.org/abs/1602.03589) for variable selection problems with highly correlated features 2. It also has a variety of algorithms for adaptively selecting groupings to maximize power while maintaing FDR control.

This is currently under heavy development (it's in the early stages): docs/tests to come.

• To run all tests, run python3 -m pytest
• To run a specific label, run pytest -v -m {label}.
• To select all labels except a particular one, run pytest -v -m "not {label}" (with the quotes).
• To run a specific file, try pytest test/{file_name}.py. To run a specific test within the file, run pytest test/{file_name}.py::classname::test_method. You also don't have to specify the exact test_method, you get the idea.
• To run a test with profiling, try python3 -m pytest {path} --profile. This should generate a set of .prof files in prof/. Then you can run snakeviz filename.prof to visualize the output. There are also more flags/options for outputs in the command line command.
• However, this isn't reallyyy recommended - cprofilev is much better. To run cprofilev, copy and paste the test to proftest/* and then run python3 -m cprofilev proftest/test_name.py.

1. Gaussian knockoff generator should be class based
2. There should be an overarching "sample knockoffs" function where you can put the type of knockoffs you want to sample in as an input argument.
3. It would be cool if we moved the KS test code and used it as a method to validate the knockoffs.

1. Knockoff Filter + Debiased Lasso
2. Need to think about whether we'll actually shift X

1. Add hierarchical clustering to ASDP group-making

1. Gradient-based method can be sped up
2. Add value for rec_prop

1. DGP class? instead of returning like 6 things?