Explainable Machine Learning in Survival Analysis

📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

📦 🎲 R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling

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Reproduction of the work by Hong, Y., Meeker, W. Q., & McCalley, J. D. (2009). Prediction of remaining life of power transformers based on left truncated and right censored lifetime data. Annals of Applied Statistics, 3(2), 857-879.

Imputation of zeros, nondetects and missing data in compositional data sets

Simple Mixed Effect Models and Censoring

Enhance content integrity using CustomCensorify: a JavaScript module. Efficiently replace sensitive words for respectful communication. Code and example included.

Kendall's Tau for Two-Sample Inference Problems

This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

Random or Extremely Random Forest for censored quantile regression.

Predicting the inhibitory response of drugs using Graph Convolutional Networks trained on censored data.

tcensReg is a package written to obtain maximum likelihood estimates from a truncated normal distribution with censoring.

The "rcens" package provides functions to generate censored samples of type I, II and III, from any random sample generator. It also provides the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the testing phase. With two options of fixed length intervals and random lengths.

FPBoost: a gradient boosting model for survival analysis that builds hazard functions as a combination of fully parametric hazards.

COMPASS: an open-source, general-purpose software toolkit for computational psychiatry

R package for `Dynamic Regression with Recurrent Events'

R library for creating means of censored data

💬 Talk on causal inference and variable importance with stochastic interventions under two-phase sampling