Reverse Dependencies of scikit-survival

The following projects have a declared dependency on scikit-survival:

• apd-crs — apd-crs is a Python package for survival analysis with cure rate, released by Aimpoint Digital, LP
• auton-survival — no summary
• bellatrex — A toolbox for Building Explanations through a LocaLly AccuraTe Rule EXtractor
• bnnsurv — TensorFlow 2.x Bayesian Neural Network for Survival Analysis
• casedi — Cancer ASE driver identification (CASEDI)
• cobsurv — Cobra Ensemble for Conditional Survival
• dnamite — Implementation of DNAMite, an interpretable model for survival analysis and more.
• ExhauFS — Exhaustive Feature Selection
• flexynesis — A deep-learning based multi-omics bulk sequencing data integration suite with a focus on (pre-)clinical endpoint prediction.
• fpboost — FPBoost: a gradient boosting model for survival analysis that builds hazard functions as a combination of fully parametric hazards.
• harmoniums — Harmoniums -- a.k.a. restricted Boltzmann machines -- with binary latent states for survival analysis.
• icare — ICARE models
• jarvais — jarvAIs: just a really versatile AI service
• osst — Implementation of Optimal Sparse Survival Trees
• proms — Protein Markers Selection
• prostate-nomograms — Prediction tools based on existing prostate cancer nomograms.
• pvOps — pvops is a python library for the analysis of field collected operational data for photovoltaic systems.
• pyhbr — Tools for bleeding/ischaemia risk estimation in PCI patients
• pystreed — Python Implementation of STreeD: Dynamic Programming Approach for Optimal Decision Trees with Separable objectives and Constraints
• ratio-t2e — Linear left barrier loss and data augmention for survival anlysis
• SCellBOW — SCellBOW is an unsupervised transfer learning algorithm for clustering scRNA-seq data and performing phenotype algebra analysis on the clusters
• skpro — A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
• survival-datasets — Data loader for common datasets in Survival Analysis.
• survivors — no summary
• survlimepy — A python package implementing SurvLIME algorithm
• survshap — Implementation of the SurvSHAP(t) explanation method for time-dependent explainability of machine learning survival models