SchlossLab/mikropml: mikropml 1.6.0

New functions: bootstrap_performance() allows you to calculate confidence intervals for the model performance from a single train/test split by bootstrapping the test set (#329, @kelly-sovacool). calc_balanced_precision() allows you to calculate balanced precision and balanced area under the precision-recall curve (#333, @kelly-sovacool). Improved output from find_feature_importance() (#326, @kelly-sovacool). Renamed the column names to feat to represent each feature or group of correlated features. New column lower and upper to report the bounds of the empirical 95% confidence interval from the permutation test. See vignette('parallel') for an example of plotting feature importance with confidence intervals. Minor documentation improvements (#323, #332, @kelly-sovacool). Full Changelog: https://github.com/SchlossLab/mikropml/compare/v1.5.0...v1.6.