PyPI recent updates for KnockoffOrigins https://pypi.org/project/knockofforigins/ Recent updates to the Python Package Index for KnockoffOrigins en 0.1.4 https://pypi.org/project/knockofforigins/0.1.4/ This repository is dedicated to implementing the methodologies from the 2015 paper "False Discovery Rate via Knockoffs". It provides code for generating knockoff features and applying selection procedures. The aim is to help users understand and apply the knockoff method for feature selection. Please refer to the original paper for a complete understanding. j.razi@outlook.com Sun, 12 May 2024 16:06:26 GMT 0.1.3 https://pypi.org/project/knockofforigins/0.1.3/ This repository is dedicated to implementing the methodologies from the 2015 paper "False Discovery Rate via Knockoffs". It provides code for generating knockoff features and applying selection procedures. The aim is to help users understand and apply the knockoff method for feature selection. Please refer to the original paper for a complete understanding. j.razi@outlook.com Fri, 26 Apr 2024 23:59:37 GMT 0.1.2 https://pypi.org/project/knockofforigins/0.1.2/ This repository is dedicated to implementing the methodologies from the 2015 paper "False Discovery Rate via Knockoffs". It provides code for generating knockoff features and applying selection procedures. The aim is to help users understand and apply the knockoff method for feature selection. Please refer to the original paper for a complete understanding. j.razi@outlook.com Fri, 26 Apr 2024 23:43:27 GMT 0.1.1 https://pypi.org/project/knockofforigins/0.1.1/ This repository is dedicated to implementing the methodologies from the 2015 paper "False Discovery Rate via Knockoffs". It provides code for generating knockoff features and applying selection procedures. The aim is to help users understand and apply the knockoff method for feature selection. Please refer to the original paper for a complete understanding. j.razi@outlook.com Fri, 26 Apr 2024 22:07:18 GMT 0.1.0 https://pypi.org/project/knockofforigins/0.1.0/ This repository is dedicated to implementing the methodologies from the 2015 paper "False Discovery Rate via Knockoffs". It provides code for generating knockoff features and applying selection procedures. The aim is to help users understand and apply the knockoff method for feature selection. Please refer to the original paper for a complete understanding. j.razi@outlook.com Fri, 26 Apr 2024 21:53:43 GMT