A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

ELKI Data Mining Toolkit

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🔗 Methods for Correlation Analysis

Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].

pca: A Python Package for Principal Component Analysis.

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Open-source framework to detect outliers in Elasticsearch events

Image Mosaicing or Panorama Creation

Deep Learning for Anomaly Deteection

RADseq Data Exploration, Manipulation and Visualization using R

Utility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs test.

2D Outlier Analysis using Shiny

Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters

Beyond Outlier Detection: LookOut for Pictorial Explanation

An implementation of Isolation forest

Imputation of Financial Time Series with Missing Values and/or Outliers

Genie: A Fast and Robust Hierarchical Clustering Algorithm (this R package has now been superseded by genieclust)

A MATLAB function for robust non-linear least squares

Mean and Covariance Matrix Estimation under Heavy Tails