0.2.0

[0.2.0]

• Non-Development version of 0.2.0.
• Minor internal code changes.
• Additional testthat tests to assess outputs.
• Removes explicit roxygen2 export of internal private functions.
• Documents the contr.dummy function from the kknn package since train.kknn requires this function to be in the
  current namespace to work.
• Allows additional arguments for multiple algorithms to be used.

[0.2.0.9000] [Development]

♻ Changed

• Refactored package internally to make code more reusable and maintainable. Version is in still in development but has
  passed previous testthat tests and all functions can be used.
• Some parameters for classCV and genFolds have been grouped together. For instance, split, n_folds, standardize,
  remove_obs, random_seed, stratified, etc are no longer separate input parameters. They are now apart of the new
  train_params parameter as elements (e.g train_params = list(split = 0.8, n_folds = 5, standardize = TRUE)). Additionally,
  model_params, save, and parallel_configs were also created to group similar parameters in the former classCV
  input parameters. For all functions, model_type is not models and for print, parameters is now configs.
• classCV output object is more organized and includes "configs" for user specified arguments and model-specific arguments,
  "class_summary" for information pertaining to classes such as the names of the classes, indices, proportions,
  "data_partitions" to include the indices, class proportions in each split/fold, and dataframes if requested, "imputation"
  for imputation information, "models" if models are requested, and "metrics" for metrics.
• Can request final model with having to specify n_folds or split.

🐛 Fixes

• The previous behavior were observations that are missing the target or excluded; however in addition to this,
  when imputation is requested, the target variable is excluded from being a predictor for imputation.

• Prior to imputation, regardless if standardization is requested, all numerical columns are standardized.

• Error when saving plots in RStudio.

• Metrics for latest GBM version should no longer produce NAs

• [RE-UPLOAD]: Version that allows final model to run without specifying split or n_folds,
  also includes additional tests. Still has the same version number - 0.2.0.9000. Re-upload also includes a fix for
  logistic regression, since model_params$threshold was called instead of model_params$logistic_threshold, resulting in
  the logistic threshold to be NULL and the prediction vector empty.