Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
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Updated
May 15, 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…
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
This repository contains a collection of functions to evaluate investment strategies regarding multiple testing concerns.
Repository for R and Python packages and reproduction codes in Weighted Conformalized Selection paper
A Shiny app for graphical multiplicity control
A FDR controlling procedure based on hidden Markov random field (Biometrics-15 paper)
Fixed Sequence Multiple Testing Procedures
Conformal Anomaly Detection
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
NYU DS-GA 1020 Final Project
POSSA: Power simulation for sequential analyses and multiple hypotheses.
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.
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
Extensive collection of resources on multiple hypothesis testing.
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