Pure C Implementation of the Chatarjee – Xi Correlation Coefficient

Hacker News

The xi (ξ) correlation coefficient proposed by Chatterjee, quantifies the degree of dependence between two variables X, Y. It is 0 if and only if the variables are independent and 1 if and only if one is a measurable function of the other. I played a bit today but couldn't find a pure C implementation, so I hacked one. Previously discussed in HN [0] Beautiful visualization [1] [0] https://news.ycombinator.com/item?id=29687613 [1] https://x.com/adad8m/status/1474754752193830912?s=21

Visit product View on Hacker NewsDec 27, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, visual · Missing: mac, agents, macos

17%17% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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