{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Phi_K computes a correlation coefficient that works consistently across categorical, ordinal, and interval variables, capturing non-linear dependencies while reverting to Pearson correlation for bivariate normal data.","skillfed_tags":["correlation-analysis","mixed-data-types","statistical-testing"],"use_cases":["Compute correlation matrices for datasets mixing categorical survey responses, ordinal ratings, and continuous measurements.","Detect non-linear dependencies between variables that Pearson correlation would miss.","Generate statistical significance tests for variable-pair dependencies in contingency tables.","Create correlation reports with visualizations for exploratory data analysis on mixed-type datasets.","Identify outliers and unusual patterns via normalized residuals of contingency tests."],"what_it_does":"Phi_K is a correlation coefficient designed to handle mixed data types\u2014categorical, ordinal, and interval variables\u2014in a single analysis. Unlike traditional correlation methods that require homogeneous data types, Phi_K applies a refinement of Pearson's hypothesis test of independence, interpreting the contingency test statistic as if from a rotated bivariate normal distribution. It captures non-linear relationships and reverts to standard Pearson correlation when the input is bivariate normal, making it useful for exploratory data analysis on heterogeneous datasets.\n\nThe package integrates with pandas DataFrames through methods like `phik_matrix()`, `significance_matrix()`, and `global_phik()`, and includes utilities for generating correlation reports as PDFs. It depends on numpy, scipy, pandas, matplotlib, and joblib, and is available as pre-built wheels for Python 3.9\u20133.14 across macOS, Linux, and Windows. The methodology is grounded in a peer-reviewed publication and includes example notebooks for basic and advanced usage.","worth_installing":"Yes, if you work with mixed-type data and need a single correlation method that handles categorical, ordinal, and interval variables together. The permissive MIT license and broad Python version support make adoption low-risk. However, the aging maintenance status (no release in 393 days) suggests checking whether active development continues before relying on it for production systems. No known vulnerabilities."},"id":"phik","links":{"html":"https://skillfed.io/packages/phik","md":"https://skillfed.io/packages/phik.md","pypi":"https://pypi.org/project/phik/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-07-17","license_spdx":null,"license_treatment":"permissive","name":"phik","python_support":"supports_current","summary":"Phi_K correlation analyzer library"},"popularity":{"monthly_downloads":1210795,"position":4213,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.12.5"}