Function reference • partition

partition
`partition()`	Agglomerative partitioning
`super_partition()`	super_partition
`is_partition()`	Is this object a partition?
`simulate_block_data()`	Simulate correlated blocks of variables
Mapping key and scores
`partition_scores()` `fitted(<partition>)`	Return the reduced data from a partition
`mapping_key()` `unnest_mappings()` `mapping_groups()`	Return partition mapping key
`filter_reduced()` `unnest_reduced()`	Filter the reduced mappings
partitioners
`part_icc()`	Partitioner: distance, ICC, scaled means
`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 means
`part_stdmi()`	Partitioner: distance, mutual information, scaled means
`is_partitioner()`	Is this object a partitioner?
`direct_distance()` `direct_distance_pearson()` `direct_distance_spearman()`	Target based on minimum distance matrix
`direct_k_cluster()`	Target based on K-means clustering
`measure_icc()`	Measure the information loss of reduction using intraclass correlation coefficient
`measure_min_icc()`	Measure the information loss of reduction using the minimum intraclass correlation coefficient
`measure_min_r2()`	Measure the information loss of reduction using minimum R-squared
`measure_std_mutualinfo()`	Measure the information loss of reduction using standardized mutual information
`measure_variance_explained()`	Measure the information loss of reduction using the variance explained
`reduce_scaled_mean()`	Reduce selected variables to scaled means
`reduce_first_component()`	Reduce selected variables to first principal component
`reduce_kmeans()`	Reduce selected variables to scaled means
Permutation and mapping
`map_partition()`	Map a partition across a range of minimum information
`test_permutation()`	Permute partitions
`permute_df()`	Permute a data set
Data visualization
`plot_area_clusters()` `plot_stacked_area_clusters()` `plot_ncluster()` `plot_information()`	Plot partitions
`plot_permutation()`	Plot permutation tests
Statistical functions
`corr()`	Efficiently fit correlation coefficient for matrix or two vectors
`mutual_information()`	Calculate the standardized mutual information of a data set
`icc()`	Calculate the intraclass correlation coefficient
`scaled_mean()`	Average and scale rows in a `data.frame`
Extending partition
`as_director()`	Create a custom director
`as_measure()`	Create a custom metric
`as_partition_step()`	Create a partition object from a data frame
`as_partitioner()`	Create a partitioner
`as_reducer()`	Create a custom reducer
`replace_partitioner()`	Replace the director, metric, or reducer for a partitioner
`reduce_cluster()` `map_cluster()`	Reduce a target
`is_partition_step()`	Is this object a `partition_step`?
Data
`baxter_clinical` `baxter_otu` `baxter_family` `baxter_genus` `baxter_data_dictionary`	Microbiome data

Reference

partition

Mapping key and scores

partitioners

Permutation and mapping

Data visualization

Statistical functions

Extending partition

Data