mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

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