Explainable Machine Learning in Survival Analysis
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Updated
Jun 15, 2024 - R
Explainable Machine Learning in Survival Analysis
snpnet - Efficient Lasso Solver for Large-scale genetic variant data
Survival analysis utility functions using functional programming principals
psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets
Survival functions for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.
Survival modelling using Cox proportional hazard regression model
R script for calculating cox proportional hazard models for the association between polyphenols and cancer risk in EPIC cohort
Survival learners for the `mlexperiments` R 📦
Multiresponse time-to-event Cox proportional hazards model - CPU
Biomarker selection in penalized regression models
Predicting customers' churn with survival analysis, including time-dependent variables
Extended diagnostic and visualization tools for Cox proportional hazard models in R
Research Major Osteoporotic Fracture: risk calculator
R package. Functions for data analysis and reporting in RMarkdown. Fast and flexible.
Survival Analysis through the integration of expression data, phenotype data, and clinical data
This project was in collaboration with University Hospital Birmingham
KM plots and Cox Proportional Hazards model for feature selection
Survival Analysis through the integration of expression data, phenotype data, and clinical data
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