Machine Learning in R

Survival analysis in Python

Survival analysis built on top of scikit-learn

Survival analysis with PyTorch

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms.

Open source package for Survival Analysis modeling

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

GPstuff - Gaussian process models for Bayesian analysis

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.

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.

Deep learning for flexible market price modeling (landscape forecasting) in real-time bidding advertising. An implementation of our KDD 2019 paper with some other (Python) implemented prediction models.

Probabilistic Supervised Learning for mlr3

Benchmarking and Visualization Toolkit for Penalized Cox Models

Joint Models for Longitudinal and Survival Data using MCMC

ICML 2018: "Adversarial Time-to-Event Modeling"

Scripts for https://www.nature.com/articles/s41598-018-27707-4, using Convolutional Neural Network to detect lung cancer tumor area

Survival analysis in Julia

Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.

Slides and notebooks for my tutorial at PyData London 2018

Piece-wise exponential Additive Mixed Modeling tools

Joint Models for Longitudinal & Survival Data under Maximum Likelihood

A Random Survival Forest implementation for python inspired by Ishwaran et al.

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

Targeted Learning for Survival Analysis

SALMON: Survival Analysis Learning with Multi-Omics Neural Networks

Tutorial on survival analysis using TensorFlow.

geneSurv: an interactive web-based tool for survival analysis in genomics research

An Artificial Life simulator for Investigating Noogenesis