There are no reviews yet. Be the first to send feedback to the community and the maintainers!
innsight
Interpretability methods to analyze the behavior and individual predictions of modern neural networks in R.neuralnet
Training of Neural Networksarfpy
Python implementation of adversarial random forests for density estimation and generative modellingpvm
A collection of signal detection methods used in the field of pharmacovigilancecpi
CPI: Conditional Predictive ImpactAPTS_Causal_Inference
Practicals for the APTS Module "Causal Inference"blockForest
Random Forests for Blocks of Clinical and Omics Covariate Dataarf
Adversarial Random Forestsmethods_week
ZeSOB Methods Weeks in Statistics on Interpretable Machine Learningsrsim
An R package for simulating spontaneous reportspvmcomparison
R package for comparing various methods used in the field of pharmacovigilance to detect associations between drugs and adverse events in spontaneous reporting dataDataTrainCausalLearning
Practicals for the Data Train Course "Causal learning" 2021 (V Didelez)datatrain_workshop_ml
Workshop / hands-on component to the Data Train ML workshopiml_bremen_2023
Exercises for the Lecture in Interpretable Machine Learning taught at the University of Bremen in 2023generative_rf
Generative Random Forestssurvnet
Artificial neural networks for survival analysisarf_paper
Code and materials to reproduce adversarial RF papermicd
Multiple Imputation in Causal Graph Discoveryrgp
Identification of Risk Groups in Pharmacovigilance Using Penalized Regression (RGP)tpc
tPC - Causal discovery with temporal backgroundiml_exercise
Interpretable Machine Learning Lecture WS2021 Uni Bremen: Exercise SheetscountARFactuals
This repository contains the code for the countARFactuals paper.Love Open Source and this site? Check out how you can help us