Machine Learning in R

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.

Probabilistic Learning for mlr3

Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

Explainable Machine Learning in Survival Analysis

📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮

Joint Models for Longitudinal and Survival Data using MCMC

Piece-wise exponential Additive Mixed Modeling tools

🔮 Benchmarking and visualization toolkit for penalized Cox models

Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.

Joint Models for Longitudinal & Survival Data under Maximum Likelihood

Repository for the familiar R-package. Familiar implements an end-to-end pipeline for interpretable machine learning of tabular data.

Solutions and code examples from An Introduction to Statistical Learning (Second Edition) by James, Witten, Hastie, and Tibshirani.

Targeted Learning for Survival Analysis

A small wrapper package that enables full survival curve estimation using xgboost

R package for fitting joint models to time-to-event and longitudinal data

snpnet - Efficient Lasso Solver for Large-scale genetic variant data

Analysis of Multivariate Event Times https://kkholst.github.io/mets/

ClinicoPath jamovi Module