Developer Tools●●Solid
VarLiNGAM-rs / Causal discovery in Rust, 50x faster than Python
14-50x faster Rust port of lingam, but causal discovery is a niche research domain.
WizardryNiche Gem
edyos
113mo ago
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GitHub Repository
Python toolkit for competing risks: forest (RSF) today; Fine-Gray + Aalen-Johansen + Gray's test + cause-specific Cox in v0.4. Scales to n=10⁶ in ~1 min, 10–22× faster than randomForestSRC on real EHR data, sklearn-compatible.
5 starsPython
Crforest – Competing-risks RSF in Python, 6× faster than R's rfSRC
by sunnyadn·Apr 29, 2026·1 point·0 comments
AI Analysis
●●●BangerNiche GemSolve My Problem
Native Python competing-risks RSF that's 6x faster than R's randomForestSrc.
Strengths
- •Bit-identical reproducibility mode enables direct R migration without recalculating any baseline results.
- •Scales to one million rows in minutes where R's randomForestSRC currently fails.
- •Scikit-learn compatible API reduces friction for existing standard Python machine learning workflows.
Weaknesses
- •Pre-alpha status means API instability until the promised stable version 1.0 release.
- •Highly niche domain limits adoption outside specific epidemiology and reliability engineering fields.
Category
Target Audience
Data scientists, epidemiologists, and researchers doing survival analysis in Python
Similar To
randomForestSRC · scikit-survival · lifelines
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