Survival analysis in Python

Machine Learning in R

Survival analysis built on top of scikit-learn

Survival analysis with PyTorch

Open source package for Survival Analysis modeling

Improving XGBoost survival analysis with embeddings and debiased estimators

Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.

GPstuff - Gaussian process models for Bayesian analysis

In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling. An implementation of our AAAI 2019 paper and a benchmark for several (Python) implemented survival analysis methods.

Probabilistic Learning for mlr3

Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

COX Proportional risk model and survival analysis implemented by tensorflow.

A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.

Explainable Machine Learning in Survival Analysis

Datasets for Predictive Maintenance

SurvSHAP(t): Time-dependent explanations of machine learning survival models