Agglomerative Partitioning Framework for Dimension Reduction

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 |

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 |

test_permutation | Permute partitions |

update_dist | Only fit the distances for a new variable |

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