Blog

Notes and write-ups on the R packages and methods I build, mostly biostatistics, survival analysis, and machine learning for health data.

survalis is now on CRAN

A unified benchmarking, evaluation, and interpretability interface for survival machine learning in R, from first submission to release

R

survival-analysis

machine-learning

packages

survalis: An Interpretable Survival Machine Learning Framework in R

One fit/predict contract across 19 survival learners, with benchmarking, calibration, and model-agnostic interpretation built in

R

survival-analysis

machine-learning

packages

unsurv: Unsupervised Clustering of Individualized Survival Curves

PAM clustering on survival probability matrices with automatic K selection, monotonic enforcement, and plotting

R

survival-analysis

clustering

packages

survdnn: Deep Neural Networks for Survival Analysis in R

Cox, AFT, and CoxTime losses with a formula interface, cross-validation, and calibration, powered by torch

R

survival-analysis

deep-learning

packages

mimar: Compact Multiple Imputation, Assessment, and Reporting in R

Artificial amputation, chained-equations MI with ML imputers, diagnostic evaluation, and pooling, in one package

R

missing-data

packages

funcml: A Formula-First Framework for Machine Learning in R

One consistent API across 20+ learners, resampling, tuning, interpretation, and causal estimation

R

machine-learning

packages