grubbstest 2.0.0

Fast Grubbs' test implementation with C++ bindings.

Stat Grubb

High speed Grubbs Test used to calculate the the z-score data points in a data set. I created this to learn about c++ and python integration and to have an fast alternate to the major python libaries. You can view the package on pypi here.

How the Algorithm Works

run_Grubbs — Grubbs Test with Outlier Removal

This is an iterative algorithm that progressively removes outliers from the dataset before computing z-scores.

run_NoOutlier — Standard Z-Score (No Outlier Removal)

A single-pass calculation with no iterative removal:

1. Compute mean and standard deviation across the entire dataset.
2. Compute z = (x − mean) / sd for every data point.

Because no points are removed, outliers pull the mean and standard deviation toward themselves, which compresses z-scores across the rest of the dataset.

---

Implementation Specifics

• The dataset is assumed to follow a normal distribution.
• The Grubbs test is designed for detecting one outlier per iteration; the loop repeats to handle multiple outliers.
• Mean and standard deviation are computed with Welford's algorithm to avoid floating-point cancellation errors.

Performance

Benchmarked against an equivalent pure-Python/NumPy/SciPy implementation:

Dataset	run_Grubbs speedup	run_NoOutlier speedup
1k	43×	1.3×
10k	8×	1.2×
100k	2.5×	1.3×

The large gains in run_Grubbs come from O(1) outlier removal (swap-to-end) and incremental mean/variance updates (reverse Welford), avoiding repeated full-array passes.

Installing GrubbsTest

macOS only (Apple Silicon)

pip install grubbstest

Usage

import fastgrubbstest as g

Functions

• run_Grubbs(data, alpha=0.05)
  • Calculates z-scores with iterative outlier removal from mean and standard deviation calculation
  • Outliers still receive a z-score, computed relative to the cleaned distribution
• run_NoOutlier(data)
  • Calculates z-scores without removing outliers from mean and standard deviation

Inputs

• run_Grubbs

  • data: data in dict format
  • alpha: significance level for Student's t-distribution (default 0.05)

• run_NoOutlier

  • data: data in dict format

Input Format

• A dictionary where each key is the ID and the value is the number

  {"ab": 85, "cd": 4, ...}
  

Output Format

• A dictionary where each key is the ID and the value is a list of [original_value, z_score]

  {"ab": [85, 1.23], "cd": [4, -0.56], ...}
  

License

This project is licensed under the MIT License