{ "info": { "author": "fixed_lambda developers", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Scientific/Engineering" ], "description": "\nThe selection project\n=====================\n\nThis project contains software for selective inference, with emphasis on\nselective inference in regression.\n\nSome key references\n-------------------\n\n- ``A significance test for the lasso``: http://arxiv.org/abs/1301.7161\n- ``Tests in adaptive regression via the Kac-Rice formula``:\n http://arxiv.org/abs/1308.3020\n- ``Post-selection adaptive inference for Least Angle Regression and the Lasso``:\n http://arxiv.org/abs/1401.3889\n- ``Exact post-selection inference with the lasso``:\n http://arxiv.org/abs/1311.6238\n- ``Exact Post Model Selection Inference for Marginal Screening``:\n http://arxiv.org/abs/1402.5596\n\nInstall\n-------\n\n.. code:: python\n\n git submodule init # travis_tools and C-software\n git submodule update\n pip install -r requirements.txt\n python setup.py install\n\nPotential speedups\n------------------\n\n- We can condition on \u201cparts\u201d of each draw of the sampler, in\n particular if we condition on the projection of the rejection\n ``sample - center`` onto direction then resampling on the ray can be\n sped up for some things like LASSO. 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