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Utility functions for copula modeling and data preprocessing.
This module provides essential utility functions for working with copulas, including data transformation, rank computation, and dependence measures.
Functions§
- bootstrap_
sample - Bootstrap resample from a dataset.
- empirical_
cdf_ transform - Transform data using the empirical CDF.
- empirical_
copula_ cdf - Compute the empirical copula CDF at a given point.
- empirical_
ranks - Compute empirical ranks of data points.
- information_
criteria - Compute information criteria for model selection.
- kendall_
tau - Compute Kendall’s tau correlation coefficient.
- multivariate_
kendall_ tau - Compute the sample version of Kendall’s tau for multivariate data.
- multivariate_
spearman_ rho - Compute the sample version of Spearman’s rho for multivariate data.
- random_
correlation_ matrix - Generate a random correlation matrix.
- remove_
missing_ values - Remove observations with missing values (NaN).
- spearman_
rho - Compute Spearman’s rho correlation coefficient.
- to_
pseudo_ observations - Convert raw data to pseudo-observations (empirical copula).
- validate_
correlation_ matrix - Check if a matrix is a valid correlation matrix.
- validate_
pseudo_ observations - Validate that pseudo-observations are in the correct range.