v0.1.0

Changed

• select_causal_rules() is now lasso_rules_filter()
• rules generation now accepts replace parameter to set replacement in bootstrapping
• rename parameter t with t_anom
• add parameter t_corr discard correlation threshold
• define discard_anomalous_rules() and discard_corre_rules() functions and
  and relative tests
• reorganize generate_rules_matrix() (separate standardization, and remove filtering)
• explicit prune_rules() function and add relative tests
• remove take1() function for random Rule Selection
• add effect modifiers filter for Rule Generation
• add generate_causal_rules() function and relative tests
• solve Undesired 'All' Decision Rule Issue
• solve No Causal Rule Selected Issue
• improve cre.summary() function
• min_nodes --> node_size (following the randomForest convention)
• estimate_cate include five methods for estimating the CATE values (poisson, DRLearner, bart-baggr, cf-means, linreg)
• cre added new arguments to (1) complement SuperLearner package (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) and to (2) select CATE method and (3) whether to filter CATE p-values (cate_method and filter_cate).
  Now returns an S3 object.
• estimate_ite_xyz conduct propensity score estimation using helper function with SuperLearner package
• generate_cre_dataset make number of covariates an argument of the function
• improve examples and update tests for all functions

Added

• print and summary generic functions.
• check_input function to isolate input checks.
• estimate_ite_aipw function for augmented inverse propensity weighting
• plot.cre generic function to plot CRE S3 object Results
• test-cre_functional.R tests the functionality of the package
• stability_selection function for causal rules selection

Removed

• estimate_ite_blp function