Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
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Updated
May 1, 2024 - Jupyter Notebook
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
The MultipleTesting package offers common algorithms for p-value adjustment and combination and more…
A Shiny app for graphical multiplicity control
A FDR controlling procedure based on hidden Markov random field (Biometrics-15 paper)
Repository for R and Python packages and reproduction codes in Weighted Conformalized Selection paper
Fixed Sequence Multiple Testing Procedures
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
NYU DS-GA 1020 Final Project
POSSA: Power simulation for sequential analyses and multiple hypotheses.
Exploring the utility of surface approximation using non-radial basis functions.
R package MHTmult: Multiple Hypotheses Testing for Multiple Families Structure
Progressive permutation for a dynamic representation of the robustness of microbiome discoveries
This paper develops new methods to handle false positives in High-Throughput Screening experiments.
FDR-controlling multiple testing procedure with n screening stages for hypothesis with a family structure.
Python notebook on GtSt testing procedures on the LARS path. Code associated with the manuscript "Multiple Testing and Variable Selection along Least Angle Regression's path" (J.-M. Azaïs & Y. De Castro)
Conformal anomaly detection wrapper for uncertainty-quantified 'PyOD' anomaly detectors.
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