BreakoutDetection <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">
An R package for detecting statistically significant breakpoints in time series using robust energy statistics.
What is BreakoutDetection <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">?
BreakoutDetection is an R package that detects statistically significant breakouts—changes in mean or distribution—in time series data. It uses the E-Divisive with Medians (EDM) algorithm, a non-parametric method based on energy statistics, to identify one or more breakpoints without assuming normal distributions. This solves the problem of accurately pinpointing shifts in data trends across various domains.
Data scientists, statisticians, and researchers working with time series data in fields like A/B testing, behavioral analysis, econometrics, financial engineering, and social sciences.
Developers choose BreakoutDetection for its robust, non-parametric approach that handles real-world data complexities, supports multiple breakout detection, and provides statistical validation through permutation tests, all within a simple R package interface.
Overview
Breakout Detection via Robust E-Statistics
Use Cases
Best For
- Detecting engagement changes after A/B tests in user analytics
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