EpistasisLab / scikit-mdr / 29

Builds Branch Commit Type Ran Committer Via Coverage
29 master Use absolute value for CMDR scoring function The t-statistic can be negative, and we don’t want to treat a large negative t-value as worse than a small positive t-value. Thus we use the absolute t-value because the sign does not matter in this case. push rhiever travis-ci pending completion   set done
28 master Add docs for continuous MDR push rhiever travis-ci pending completion   set done
27 master Add unit tests for continuous MDR push rhiever travis-ci pending completion   set done
26 master Break out ContinuousMDR into a separate file push rhiever travis-ci pending completion   set done
25 master Initial implementation of continuous MDR for continuous outcomes push rhiever travis-ci pending completion   set done
24 master Update README.md push web-flow travis-ci pending completion   set done
23 master Add feature map to MDREnsemble push rhiever travis-ci pending completion   set done
22 master Remove pandas dependency push rhiever travis-ci pending completion   set done
21 master Replace our custom accuracy function with sklearn's accuracy function push rhiever travis-ci pending completion   set done
20 master Add MDREnsemble Just adds a BaggingClassifier around a MDR push rhiever travis-ci pending completion   set done
19 master Allow MDR to become a classifier as well Added predict and fit_predict methods, as well as the set_param method so it can be used on scikit-learn ensemble methods. push rhiever travis-ci pending completion   set done
18 master Add PyPi release version badge push web-flow travis-ci pending completion   set done
17 master Update version for release push rhiever travis-ci pending completion   set done
16 master Update README.md push web-flow travis-ci pending completion   set done
15 master Change repo over to EpistasisLab push web-flow travis-ci pending completion   set done