Reverse Dependencies of lifelines

The following projects have a declared dependency on lifelines:

• auton-survival — no summary
• autoprognosis — A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
• biolearn — Machine learning for biomarkers computing
• bizkit — A package that streamlines business data analytics
• celldetective — description
• cinet — Scikit-Learn interface for CINET PyTorch siamese neural network
• crag — Competing Risk Analysis Genome-wide
• credoai-lens — Lens: comprehensive assessment framework for AI systems
• dcurves — A Python package for Decision Curve Analysis to evaluate prediction models, molecular markers, and diagnostic tests. For RELEASE NOTES, check RELEASE.md here: https://github.com/MSKCC-Epi-Bio/dcurves/RELEASE.md
• deepsurvk — Implementation of DeepSurv using Keras
• dsu — no summary
• ehrapy — Electronic Health Record Analysis with Python.
• ExhauFS — Exhaustive Feature Selection
• fathom-lib — Fathom lib
• fife — Finite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and multivariate time series forecasting
• fifeforspark — Finite-Interval Forecasting Engine for Spark: Machine learning models for discrete-time survival analysis and multivariate time series forecasting for Apache Spark
• fuse-med-ml — A python framework accelerating ML based discovery in the medical field by encouraging code reuse. Batteries included :)
• harmoniums — Harmoniums -- a.k.a. restricted Boltzmann machines -- with binary latent states for survival analysis.
• hypehd — This package aims to be a tool for real-world and practical data analysis, assisting in reaching a quicker understanding of various health related data.
• icgc-survival — A framework for survival prediction and analysis of ICGC datasets
• ifree — i love freedom, free my hand.
• ISLP — Library for ISLP labs
• jori-autoprognosis — Test
• kaplanmeier — Create survival curves using kaplanmeier, the log-rank test and making plots.
• labours — Python companion for github.com/src-d/hercules to visualize the results.
• liqa — A statistical tool to quantify isoform-specific expression using long-read RNA-seq
• lohrasb — This versatile tool streamlines hyperparameter optimization in machine learning workflows.It supports a wide range of search methods, from GridSearchCV and RandomizedSearchCVto advanced techniques like OptunaSearchCV, Ray Tune, and Scikit-Learn Tune.Designed to enhance model performance and efficiency, it's suitable for tasks of any scale.
• mlduct — A personal framework for Machine Learning Pipelines.
• ngboost — Library for probabilistic predictions via gradient boosting.
• ngboost-release — It is just a workaround to fix the current ngboost package issue. See: https://github.com/stanfordmlgroup/ngboost/issues/283
• njab — not Just Another Biomarker
• OmicsAnalysis — General omics data analysis tools
• omicverse — OmicVerse: A single pipeline for exploring the entire transcriptome universe
• Orange3-Survival-Analysis — Survival Analysis add-on for Orange data mining software package.
• organsync — Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
• PheTK — The Phenotype Toolkit
• prda — Prda contains packages for data processing, analysis and visualization. The ultimate goal is to fill the “last mile” between analysts and packages.
• pydts — Discrete time survival analysis with competing risks
• PyMSM — Multstate modeling in Python
• RAMSTK — The RAMS ToolKit (RAMSTK) is a suite of tools for performing and documenting reliability, availability, maintainability, and safety (RAMS) analyses
• random-survival-forest — A Random Survival Forest implementation inspired by Ishwaran et al.
• robi — ROBI: Robust and Optimized Biomarker Identifier
• rulexai — RuleXAI is a rule-based aproach to explain the output of any machine learning model. It is suitable for classification, regression and survival tasks.
• scanexitronlr — ScanExitronLR: a lightweight tool for the characterization and quantification of exitrons in long read RNA-seq data
• scikit-fibers — A Scikit Learn compatible implementation of FIBERS Algorithm
• scikit-rare — A Scikit Learn compatible implementation of RARE Algorithm
• skpro — A unified framework for probability distributions and probabilistic supervised regression
• skt — SKT package
• slideflow — Deep learning tools for digital histology
• SNAF — A Python package to predict, prioritize and visualize splicing derived neoantigens, including MHC-bound peptides (T cell antigen) and altered surface protein (B cell antigen)
• SurrealGAN — A python implementation of Surreal-GAN for semisupervised representation learning
• survival-analysis — Survival Analysis: Customer Churn and CLV Prediction
• survival-data-handler — no summary
• survivalvolume — Plotting tools for survival data
• survivors — no summary
• synthcity — Synthetic data generator and evaluator!
• synthetic-data-generation — Algorithms for generating synthetic data
• tapir-rna — Transcriptional Analysis in Python Imported from R
• teachpyx — Teaching material, algorithm, machine learning
• temporai — TemporAI: ML-centric Toolkit for Medical Time Series
• TiRank — A comprehensive analysis tool for transfering phenotype of bulk transcritomic data to single cell or spatial transcriptomic data.
• tno.mpc.protocols.kaplan-meier — Kaplan Meier using Paillier homomorphic encryption and a helper party
• TrialPathfinder — Python library for systematic evaluation of clinical trial eligibility criteria.
• unpast — A novel method for unsupervised patient stratification.
• xgbse — Improving XGBoost survival analysis with embeddings and debiased estimators