Explainable Machine Learning in Survival Analysis
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Updated
Apr 16, 2024 - R
Explainable Machine Learning in Survival Analysis
📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮
📦 🎲 R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling
Reproduction of the work by Hong, Y., Meeker, W. Q., & McCalley, J. D. (2009). Prediction of remaining life of power transformers based on left truncated and right censored lifetime data. Annals of Applied Statistics, 3(2), 857-879.
Imputation of zeros, nondetects and missing data in compositional data sets
Random or Extremely Random Forest for censored quantile regression.
Kendall's Tau for Two-Sample Inference Problems
tcensReg is a package written to obtain maximum likelihood estimates from a truncated normal distribution with censoring.
The work had been done with Prof. Biswabrata Pradhan, Indian Statistical Institute, Kolkata. A tree-based method for censored survival data is discussed, based on maximizing the difference in survival between groups of patients by the Log-Rank statistic value based criterion.
❗ This is forked from the read-only mirror of the CRAN R package repository. imputeYn — Imputing the Last Largest Censored Observation(s) Under Weighted Least Squares
Tools for working with NPDES data during permit development.
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