Survival analysis in Python
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
Python Structural Reliability Analysis
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.
Uncertainty Quantification in Julia
Code for the paper "Deep Cox Mixtures for Survival Regression", Machine Learning for Healthcare Conference 2021
ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification
Statistical methods and visualizations often used in reliability engineering including the well-known weibull analysis and Monte Carlo simulations
A Python3 library of test functions from the uncertainty quantification community with a common interface for validation and benchmarking purposes.
PARANSYS (Python pArametric Reliability Analysis on ANSYS) is a module that can connect the ANSYS software to Python scripts using APDL scripts. It has one connection class and two other classes for reliability analysis, using explicit or implicit limit states.
Course repository for learning R for Cornell course SYSEN 5300: Systems Engineering and Six Sigma for the Design and Operation of Reliable Systems
Internet reliability check - CLI tool
Framework to perform content-based diversity analysis and reliability analysis
Simulation and generation of synthetic reliability-related data.
Программа для анализа надежности электронных средств.
Weibull series system estimation from data with censored lifetimes and masked component cause of failure.
L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions
This Project is a study of the patient’s survival rate due to heart failure condition caused by cardiovascular diseases. Various factors causing the disease were analyzed with the use of reliability analysis and software to model and predict patients’ survival.
Codes for estimates of intensity functions parameters of multivariate counting processes and density estimation for latent variable.
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