Statistical Analysis With Outlier Detection

via “statistical-analysis-with-outlier-detection”

A local/remote high-performance Model Context Protocol (MCP) server for math-ing whilst vibing with LLMs. Built with Polars, Pandas, NumPy, SciPy, and SymPy for optimal calculation speed and comprehensive mathematical capabilities from basic arithmetic to advanced calculus and linear algebra ## Loc

Unique: Combines descriptive statistics (mean, median, quartiles) with automatic outlier detection using configurable methods (IQR or Z-score), returning both summary metrics and detailed outlier identification in a single call. Handles missing values transparently and provides distribution shape metadata.

vs others: More comprehensive than basic statistical functions by including outlier detection and distribution analysis; faster than manual outlier detection loops through vectorized NumPy/Pandas operations.

via “outlier detection in file changes”

Analyze GitHub repositories to uncover contributor impact, PR complexity, and author work patterns. Get recommendations on key contributors and visualize activity storylines across folders and files. Spot long-tail file outliers, coupling, and churn to guide reviews and planning.

Unique: Combines statistical analysis with Git history to provide a unique perspective on file change patterns, unlike typical file monitoring tools.

vs others: More focused on identifying potential issues through statistical outlier detection compared to basic file change logs.

via “outlier detection”

Load and profile tabular data to quickly understand structure, quality, and trends. Explore columns with statistics, correlations, value distributions, and outlier detection to surface insights. Clean, transform, and export datasets with flexible filtering, grouping, and column operations.

Unique: Combines multiple statistical methods for outlier detection within a single framework, allowing for flexible and comprehensive analysis.

vs others: More comprehensive than basic outlier detection tools by offering multiple statistical methods in one interface.

via “anomaly detection and outlier identification”

AI data processing, analysis, and visualization

Unique: Combines multiple anomaly detection algorithms with feature importance analysis to explain not just which records are anomalous, but which specific features caused the anomaly flag, enabling targeted investigation

vs others: More interpretable than black-box anomaly detection because it explains feature contributions, though less sophisticated than domain-specific fraud detection models

via “anomaly detection in time series”

via “data-anomaly-detection”