You need the following Python libraries:
- numpy
- pandas
- matplotlib
- seaborn
- scipy
- lifelines
- sklearn
- fpdf
Make sure these are installed in your Python environment.
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Plotting functions
hist_plot(): Generates histograms.violin_plot(): Creates violin plots.waterfalls_plot(): Constructs waterfall plots.km_plot(): Plots Kaplan-Meier survival curves.forest_plot(): Creates forest plot.
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Statistical analysis functions
mann_whitney_p(): Computes the Mann-Whitney U test p-value.logranktest(): Performs the log-rank test for survival data.cph_univariable_analysis(): Conducts univariable Cox proportional hazards analysis.logistic_regression_loocv_auc(): Calculates the AUC from logistic regression with leave-one-out cross-validation.
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Comprehensive analysis function
analyze_feature(): This function conducts a thorough analysis of a specific feature, including generating plots, computing statistics, and compiling the results into a PDF report.
Refer to the example provided in the example_notebook. Use the analyze_feature() function, passing in your DataFrame, the specific feature you wish to analyze, and the directory path where you want the output (plots and pdf) to be saved.