survival-analysis

There are 515 repositories under survival-analysis topic.

  • lifelines

    CamDavidsonPilon/lifelines

    Survival analysis in Python

    Language:Python2.3k69954550

  • mlr-org/mlr

    Machine Learning in R

    Language:R1.6k1061.7k403

  • sebp/scikit-survival

    Survival analysis built on top of scikit-learn

    Language:Python1.1k23214207

  • havakv/pycox

    Survival analysis with PyTorch

    Language:Python77115134178

  • square/pysurvival

    Open source package for Survival Analysis modeling

    Language:HTML3321963105

  • MatthewReid854/reliability

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

    Language:Python316204071

  • loft-br/xgboost-survival-embeddings

    Improving XGBoost survival analysis with embeddings and debiased estimators

    Language:Python310844351

  • autonlab/auton-survival

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

    Language:Python30097373

  • robi56/Survival-Analysis-using-Deep-Learning

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

    20919068
  • Miscellaneous-R-Code

    m-clark/Miscellaneous-R-Code

    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 **Superseded by the models-by-example repo**.

    Language:R19322281

  • gpstuff-dev/gpstuff

    GPstuff - Gaussian process models for Bayesian analysis

    Language:MATLAB165232159

  • archd3sai/Customer-Survival-Analysis-and-Churn-Prediction

    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.

    Language:Jupyter Notebook1482158

  • rk2900/DRSA

    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.

    Language:Python13581557
  • mlr3proba

    mlr-org/mlr3proba

    Probabilistic Learning for mlr3

    Language:R1131120019

  • tylermorganwall/skpr

    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.

    Language:R106123615

  • liupei101/TFDeepSurv

    COX Proportional risk model and survival analysis implemented by tensorflow.

    Language:Python9631727
  • autoprognosis

    vanderschaarlab/autoprognosis

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

    Language:Python9123426
  • survex

    ModelOriented/survex

    Explainable Machine Learning in Survival Analysis

    Language:R8975410

  • kokikwbt/predictive-maintenance

    Datasets for Predictive Maintenance

    Language:Jupyter Notebook852124

  • MI2DataLab/survshap

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

    Language:Jupyter Notebook7461714

  • JuliaStats/Survival.jl

    Survival analysis in Julia

    Language:Julia7092422

  • huangzhii/SALMON

    SALMON: Survival Analysis Learning with Multi-Omics Neural Networks

    Language:Python663422

  • rk2900/DLF

    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.

    Language:Python646716

  • drizopoulos/JMbayes

    Joint Models for Longitudinal and Survival Data using MCMC

    Language:R55119124

  • julianspaeth/random-survival-forest

    A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.

    Language:Python55389

  • nt-williams/lmtp

    :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:

    Language:R5469015

  • ActuarialIntelligence/Base

    https://www.researchgate.net/profile/Rajah_Iyer

    Language:C#50114

  • sdw95927/pathology-images-analysis-using-CNN

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

    Language:Python493532

  • derrynknife/SurPyval

    A Python package for survival analysis. The most flexible survival analysis package available. SurPyval can work with arbitrary combinations of observed, censored, and truncated data. SurPyval can also fit distributions with 'offsets' with ease, for example the three parameter Weibull distribution.

    Language:Python454345

  • sebp/survival-cnn-estimator

    Tutorial on survival analysis using TensorFlow.

    Language:Jupyter Notebook454219
  • pammtools

    adibender/pammtools

    Piece-wise exponential Additive Mixed Modeling tools

    Language:R44412311

  • arturomoncadatorres/deepsurvk

    Implementation of DeepSurv using Keras

    Language:Python4121218

  • giabaio/survHE

    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.

    Language:R4183419

  • nanxstats/hdnom

    🔮 Benchmarking and visualization toolkit for penalized Cox models

    Language:R3981211

  • sauravmishra1710/Heart-Failure-Condition-And-Survival-Analysis

    Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

    Language:Jupyter Notebook371014

  • RyanWangZf/SurvTRACE

    SurvTRACE: Transformers for Survival Analysis with Competing Events

    Language:Python36388