| all_columns_reduced | Check if all variables reduced to a single composite |
| all_done | Mark the partition as complete to stop search |
| append_mappings | Append a new variable to mapping and filter out composite... |
| as_director | Create a custom director |
| as_measure | Create a custom metric |
| as_partition | Return a partition object |
| as_partitioner | Create a partitioner |
| as_partition_helpers | Process mapping key to return from 'partition()' |
| as_partition_step | Create a partition object from a data frame |
| as_reducer | Create a custom reducer |
| assign_partition | Process a dataset with a partitioner |
| baxter_data | Microbiome data |
| binary_k_search | Search for best 'k' using the binary search method |
| build_next_name | Create new variable name based on prefix and previous... |
| calculate_new_variable | Calculate or retrieve stored reduced variable |
| compare_k | Assess 'k' search |
| compare_metric | Compare metric to threshold |
| corr | Efficiently fit correlation coefficient for matrix or two... |
| direct_distance | Target based on minimum distance matrix |
| direct_k_cluster | Target based on K-means clustering |
| direct_measure_reduce | Apply a partitioner |
| filter_reduced | Filter the reduced mappings |
| find_min_distance_variables | Find the index of the pair with the smallest distance |
| fit_distance_matrix | Fit a distance matrix using correlation coefficients |
| guess_init_k | Guess initial 'k' based on threshold and 'p' |
| handle_missing | Process reduced variables when missing data |
| hits | Count and retrieve the number of metrics below threshold |
| icc | Calculate the intraclass correlation coefficient |
| icc_r | Calculate the intraclass correlation coefficient |
| is_partition | Is this object a partition? |
| is_partitioner | Is this object a partitioner? |
| is_partition_step | Is this object a 'partition_step'? |
| is_same_function | Are two functions the same? |
| k_exhausted | Have all values of 'k' been checked for metric? |
| kmeans_helpers | Which kmeans algorithm to use? |
| linear_k_search | Search for best 'k' using the linear search method |
| map_partition | Map a partition across a range of minimum information |
| mapping_helpers | Summarize and map partitions and permutations |
| mapping_key | Return partition mapping key |
| matrix_is_exhausted | Have all pairs of variables been checked for metric? |
| measure_icc | Measure the information loss of reduction using intraclass... |
| measure_min_icc | Measure the information loss of reduction using the minimum... |
| measure_min_r2 | Measure the information loss of reduction using minimum... |
| measure_std_mutualinfo | Measure the information loss of reduction using standardized... |
| measure_variance_explained | Measure the information loss of reduction using the variance... |
| mutual_information | Calculate the standardized mutual information of a data set |
| part_icc | Partitioner: distance, ICC, scaled means |
| partition | Agglomerative partitioning |
| partition_scores | Return the reduced data from a partition |
| part_kmeans | Partitioner: K-means, ICC, scaled means |
| part_minr2 | Partitioner: distance, minimum R-squared, scaled means |
| part_pc1 | Partitioner: distance, first principal component, scaled... |
| part_stdmi | Partitioner: distance, mutual information, scaled means |
| paste_partitioners | Lookup partitioner types to print in English |
| permute_df | Permute a data set |
| pipe | Pipe operator |
| plot_partitions | Plot partitions |
| plot_permutation | Plot permutation tests |
| print_color | Print to the console in color |
| print_helpers | Helper functions to print 'partition' summary |
| pull_mappings | Access mapping variables |
| reduce_first_component | Reduce selected variables to first principal component |
| reduce_kmeans | Reduce selected variables to scaled means |
| reduce_mappings | Create a mapping key out of a list of targets |
| reduce_scaled_mean | Reduce selected variables to scaled means |
| reduce_target | Reduce a target |
| replace_partitioner | Replace the director, metric, or reducer for a partitioner |
| return_if_single | Reduce targets if more than one variable, return otherwise |
| rewind_target | Set target to last value |
| scaled_mean | Average and scale rows in a 'data.frame' |
| search_k | Search for the best 'k' |
| simplify_names | Simplify reduced variable names |
| simulate_block_data | Simulate correlated blocks of variables |
| super_partition | super_partition |
| test_permutation | Permute partitions |
| update_dist | Only fit the distances for a new variable |
