missoutlier 0.1.0

Outlier detection using the MISS (MAD-IQR-SD Simultaneous) method

🎯 missoutlier

Outlier Detection Using the MISS Method

A weighted composite of MAD, IQR, and SD for robust univariate outlier detection

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Overview

missoutlier implements the MISS (MAD–IQR–SD Simultaneous) method, a new approach for univariate outlier detection that combines three classical techniques into a single robust threshold:

Method	Bounds	Weight
MAD (Median Absolute Deviation)	median ± 1.5 × MAD	87.8%
IQR (Interquartile Range)	Q25/Q75 ± 2 × IQR	1.2%
SD (Standard Deviation)	mean ± 5 × SD	11.0%

The composite threshold is computed as:

$$\text{MISS} = 0.878 \times \text{MAD} + 0.012 \times \text{IQR} + 0.11 \times \text{SD}$$

By heavily weighting the robust MAD while retaining sensitivity from IQR and SD, MISS offers a balanced approach that handles skewed and heavy-tailed distributions better than any single method alone.

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Installation

# Install from GitHub
pip install git+https://github.com/GuillaumePech/missOutlierPy.git

Dependencies: numpy >= 1.20, scipy >= 1.7

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Quick Start

import numpy as np
from missoutlier import detect_outliers_miss

# Generate data with outliers
x = np.concatenate([np.random.randn(100), [50, -40]])

# Default: replace outliers with NaN
x_clean = detect_outliers_miss(x)
# Detected 2 outliers (1.96% of data) using MISS method.

# Drop outliers entirely
x_dropped = detect_outliers_miss(x, drop=True)
# Detected 2 outliers (1.96% of data) using MISS method.

# Handle existing NaNs
x_na = np.concatenate([np.random.randn(100), [np.nan, 50]])
x_clean = detect_outliers_miss(x_na, na_rm=True)

# Silent mode (no messages)
x_clean = detect_outliers_miss(x, silent=True)

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Parameters

Parameter	Type	Default	Description
data	array-like	—	Input data (must be one-dimensional)
drop	bool	False	If True, removes outliers. If False, replaces them with NaN
na_rm	bool	False	If True, ignores NaN values when computing thresholds
silent	bool	False	If True, suppresses the detection message

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How It Works

                ┌──────────────┐
                │  Input Data  │
                └──────┬───────┘
                       │
          ┌────────────┼────────────┐
          ▼            ▼            ▼
     ┌─────────┐ ┌─────────┐ ┌─────────┐
     │   MAD   │ │   IQR   │ │   SD    │
     │  ×0.878 │ │  ×0.012 │ │  ×0.11  │
     └────┬────┘ └────┬────┘ └────┬────┘
          │            │            │
          └────────────┼────────────┘
                       ▼
              ┌────────────────┐
              │ MISS Threshold │
              └────────┬───────┘
                       ▼
              ┌────────────────┐
              │ Flag Outliers  │
              └────────────────┘

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Also available in R

devtools::install_github("GuillaumePech/missOutlierR")

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Citation

If you use this package in your research, please cite:

Pech, G., Vaccaro, N., Caspar, E. A., Amerio, P., Cleeremans, A., Leys, C., & Ley, C. (2026). How not to MISS an outlier: comparing three classic univariate methods and introducing a new one, the MAD–IQR–SD Simultaneous (MISS). PsyArXiv. https://doi.org/10.31234/osf.io/2r9yw_v2

@article{pech2026miss,
  title={How not to {MISS} an outlier: comparing three classic univariate methods and introducing a new one, the {MAD--IQR--SD} Simultaneous ({MISS})},
  author={Pech, Guillaume and Vaccaro, Niccol{\`o} and Caspar, Emilie A. and Amerio, Pietro and Cleeremans, Axel and Leys, Christophe and Ley, Christophe},
  year={2026},
  journal={PsyArXiv},
  doi={10.31234/osf.io/2r9yw_v2}
}

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License

MIT © Guillaume Pech