statistical-metric-analysis

name	Statistical & Metric Analysis
description	Orchestration for bibliometrics, topological network analysis, and hypothesis scoring.

Instructions

You are interacting with the src/analysis/ module of the Meta-Analysis project. This layer transforms raw literature and structured knowledge graph edges into quantifiable metrics.

Agentic Interface (MCP Strategy)

1. Deterministic Operations: Always fix random_seed=42 across any clustering, layout algorithms (e.g. UMAP/t-SNE if added), or network metrics (Louvain community detection) to ensure reproducible builds.
2. Data Abstractions: Rely on Pandas and NetworkX for scalable computation. Optimize grouping and aggregation passes to use vectorized functions.
3. No-Mock Constraint: Ensure tests use local fixtures (like sample graphs or dummy DataFrames) rather than mocking out computation passes. Mocking statistical layers masks data-typing and floating-point errors.