b1-method 0.2.0

Domain-independent convergent derivation of canonical basis vectors from heterogeneous observations

b1-method

Domain-independent convergent derivation of canonical basis vectors from K independent sources.

Install

pip install b1-method

Quick Start

from b1_method import B1Analysis

alignment = {
    "Extraversion":      ["Y", "Y", "Y", "Y", "Y", "Y"],
    "Agreeableness":     ["Y", "Y", "Y*", "Y", "Y", "Y"],
    "Conscientiousness": ["Y", "Y", "Y", "Y", "Y", "Y"],
    "Neuroticism":       ["Y", "Y", "Y*", "N*", "Y", "Y"],
    "Openness":          ["Y", "Y", "Y", "N*", "Y", "Y"],
    "Honesty-Humility":  ["N", "N", "Y", "Y*", "N", "N"],
}

result = B1Analysis(alignment, domain="Personality").run()
B1Analysis.print_report(result)

CLI

b1-method run alignment.csv --sources sources.csv --domain Personality
b1-method temporal alignment.csv --sources sources.csv --domain Personality
b1-method version

How It Works

Given K independent source assessments proposing competing dimensional structures for the same domain, B1 produces a tier-classified, independence-verified basis:

• Tier 1 (count >= ceil(2K/3)): Strong convergence — confirmed basis vectors
• Tier 2 (count >= ceil(K/3)): Partial convergence — contested candidates
• Tier 3 (count < ceil(K/3)): Weak/non-convergent — insufficient support

The number of Tier 1 candidates is a lower bound on the domain's dimensionality.

License

MIT