mikropml 1.0.0

• mikropml now has a logo created by @NLesniak!
• Made documentation improvements (#238, #231 @kelly-sovacool; #256 @BTopcuoglu).
• New option in preprocess_data(): prefilter_threshold (#240, @kelly-sovacool, @courtneyarmour).
  • Remove any features that appear in N=prefilter_threshold or fewer rows in the data.
  • Created function remove_singleton_columns() called by preprocess_data() to carry this out.
• New option in get_feature_importance(): groups (#246, @kelly-sovacool).
  • Provide custom groups of features to permute together during permutation importance.
  • groups is NULL by default; in this case, correlated features above corr_thresh are grouped together.
• preprocess_data() now replaces spaces in the outcome column with underscores (#247, @kelly-sovacool, @JonnyTran).
• Clarify in the intro vignette that we do not support multi-label outcomes. (#254, @zenalapp)
• Optional progress bar for preprocess_data() and get_feature_importance() using the progressr package (#257, @kelly-sovacool, @JonnyTran, @FedericoComoglio).
• The mikropml paper is soon to be published in JOSS!

mikropml 0.0.1

This is the first release version of mikropml! 🎉

• Added a NEWS.md file to track changes to the package.
• Major new functions:
  • run_ml()
  • preprocess_data()
  • plot_model_performance()
  • plot_hp_performance()
• Support for ML methods in run_ml():
  • glmnet: logistic and linear regression
  • rf: random forest
  • rpart2: decision trees
  • svmRadial: support vector machines
  • xgbTree: gradient-boosted trees
• New vignettes:
  • Introduction
  • Preprocess data
  • Hyperparameter tuning
  • Parallel processing
  • The mikropml paper