Function reference • cytominer

Common workflow operations
`aggregate()`	Aggregate data based on given grouping.
`transform()`	Transform observation variables.
`normalize()`	Normalize observation variables.
`variable_select()`	Select observation variables.
`variable_importance()`	Measure variable importance.
`drop_na_rows()`	Drop rows that are `NA` in all specified variables.
`mark_outlier_rows()`	Mark outlier rows.
Variable selection methods
`correlation_threshold()`	Remove redundant variables.
`drop_na_columns()`	Remove variables with NA values.
`variance_threshold()`	Remove variables with near-zero variance.
Aggregation methods
`covariance()`	Compute covariance matrix and vectorize.
Transformation methods
`generalized_log()`	Generalized log transform data.
`husk()`	Husk data.
`sparse_random_projection()`	Reduce the dimensionality of a population using sparse random projection.
`spherize()`	Spherize data.
Variable importance methods
`replicate_correlation()`	Measure replicate correlation of variables.
`svd_entropy()`	Feature importance based on data entropy.
Higher-order profiling methods
`extract_subpopulations()`	Extract subpopulations.
Meta operations
`stratify()`	Stratify operations.
Internal
`count_na_rows()`	Count the number of `NA`s per variable.
`generate_component_matrix()`	A sparse matrix for sparse random projection.
`find_significant_pcs()`	Find significant PC's given the eigenvalues.

Reference