This project contains software for selective inference, with emphasis on selective inference in regression.

Some key references:

• A significance test for the lasso: http://arxiv.org/abs/1301.7161
• Tests in adaptive regression via the Kac-Rice formula: http://arxiv.org/abs/1308.3020
• Post-selection adaptive inference for Least Angle Regression and the Lasso: http://arxiv.org/abs/1401.3889
• Exact post-selection inference with the lasso: http://arxiv.org/abs/1311.6238
• Exact Post Model Selection Inference for Marginal Screening: http://arxiv.org/abs/1402.5596

git submodule init # travis_tools and C-software
git submodule update
pip install -r requirements.txt
python setup.py install

• We can condition on "parts" of each draw of the sampler, in particular if we condition on the projection of the rejection sample - center onto direction then resampling on the ray can be sped up for some things like LASSO. Could be some cost in power.

• Learning a higher dimensional function can perhaps save some time -- proper conditioning has to be checked.