krl-model-zoo 1.0.0

Production-ready socioeconomic and econometric models for policy analysis, forecasting, and regional development

KR-Labs Model Zoo

Open Models. Trusted Intelligence. Shared Progress.

📚 Documentation • 🚀 Quick Start • 💡 Examples • 🤝 Contributing

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🌟 Highlights

• 🎯 100+ Production-Ready Models - ARIMA, GARCH, VAR, Kalman Filters, Random Forest, XGBoost, and more
• 📊 Built for Public Data - Native integration with Census, BLS, FRED, CDC, HUD via KRL Data Connectors
• 🔬 Research-Grade Quality - Peer-reviewed algorithms with comprehensive validation and testing
• 🚀 Production Ready - Battle-tested in real-world policy analysis and forecasting applications
• 📖 Fully Documented - Complete API reference, tutorials, and mathematical formulations
• 🤝 Open Source - Apache 2.0 License, community-driven development

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Overview

KRL Model Zoo™ is an open-source library of socioeconomic and econometric models designed for researchers, policymakers, analysts, and community organizations seeking to turn data into actionable intelligence.

It provides modular, production-grade tools for forecasting, regional analysis, anomaly detection, and policy evaluation — all engineered with transparency, reproducibility, and accessibility in mind.

Developed by KR-Labs, the Model Zoo bridges statistical rigor with public-interest purpose, transforming raw data into real-world insight.

Integrated Ecosystem

The Model Zoo is designed to work seamlessly with KRL Data Connectors, our companion library for accessing public datasets. Together, they provide an end-to-end workflow:

Data → Analysis → Insight

• KRL Data Connectors pulls data from Census, BLS, FRED, CDC, HUD, and 20+ other federal sources
• KRL Model Zoo applies statistical models and analytical frameworks
• Results drive evidence-based decisions and policy evaluation

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Mission

We believe data science should serve humanity.
The KRL Model Zoo exists to make responsible, interpretable, and replicable analytics accessible to everyone shaping economic, social, or community outcomes.

Our models support research, governance, and education by standardizing open methods for understanding change.

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Core Capabilities

• Unified Framework: Econometric, causal, ML, and Bayesian models under one architecture.
• Transparency by Design: Built-in provenance tracking, version control, and reproducibility.
• Seamless Data Integration: Native compatibility with KRL Data Connectors for federal datasets.
• Community-Driven: Collaborative ecosystem for model sharing, validation, and continuous improvement.
• Policy-Ready Tools: Designed for public datasets like Census, BLS, FRED, CDC, and HUD.
• Educational Utility: Ideal for universities, research institutes, and civic technology programs.

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Model Families

Domain	Example Models	Use Case
Time Series Analysis	ARIMA, SARIMA, GARCH, Kalman Filters	Forecasting unemployment, prices, or economic volatility
Econometrics	VAR, Cointegration, Structural Breaks	Studying policy impacts and long-term relationships
Regional Analysis	Location Quotient, Shift-Share	Understanding regional economic specialization
Anomaly Detection	STL Decomposition, Isolation Forest	Identifying shocks and unusual patterns in public data
Extensions	Causal Inference, Bayesian Hierarchies, Network Models	Enterprise Applications

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Practical Impact

The Model Zoo powers work that matters:

• Labor & Employment: Forecasting job trends, analyzing workforce shifts, and tracking equity gaps.
• Housing & Urban Development: Modeling affordability, detecting displacement, and identifying price volatility.
• Income & Inequality: Measuring economic disparity, mobility, and opportunity over time.
• Public Health: Linking health indicators with economic and environmental conditions.
• Regional Development: Assessing industrial strengths, resilience, and competitiveness.

Each model is field-tested, policy-relevant, and community-accessible.

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Quick Start

Installation

# Install Model Zoo
git clone https://github.com/KR-Labs/krl-model-zoo.git
cd krl-model-zoo
make install-dev

# Optional: Install Data Connectors for seamless data access
pip install krl-data-connectors

Complete Workflow Example

# Step 1: Fetch data using KRL Data Connectors
from krl_data_connectors import BLSConnector, CensusConnector

# Get unemployment data from BLS
bls = BLSConnector()
unemployment_data = bls.get_series('LNS14000000', start_year=2015, end_year=2024)

# Get regional employment from Census
census = CensusConnector()
regional_data = census.get_cbp_data(year=2023, geography='county', state='06')

# Step 2: Analyze with Model Zoo
from krl_models.econometric import SARIMAModel
from krl_models import LocationQuotientModel

# Forecast unemployment trends
model = SARIMAModel(
    data=unemployment_data,
    params={'order': (1, 1, 1), 'seasonal_order': (0, 0, 0, 0)},
    meta={'name': 'unemployment_forecast', 'version': '1.0.0'}
)
result = model.fit()
forecast = model.predict(steps=12)

# Analyze regional specialization
lq = LocationQuotientModel(
    data=regional_data,
    params={
        'region_col': 'county',
        'industry_col': 'naics',
        'employment_col': 'emp'
    },
    meta={'name': 'regional_lq', 'version': '1.0.0'}
)
lq_result = lq.fit()

# Step 3: Export results for reporting or visualization
forecast.to_csv('unemployment_forecast.csv')
lq_result.to_excel('regional_analysis.xlsx')

Standalone Usage (Without Data Connectors)

from krl_models.econometric import SARIMAModel
from krl_models import STLAnomalyModel
import pandas as pd

# Use your own data
data = pd.read_csv('your_data.csv')

# Forecast with SARIMA
model = SARIMAModel(
    data=data,
    params={'order': (1, 1, 1), 'seasonal_order': (1, 1, 1, 12)},
    meta={'name': 'my_forecast', 'version': '1.0.0'}
)
result = model.fit()
forecast = model.predict(steps=12)

# Detect anomalies
anomaly_model = STLAnomalyModel(
    data=data,
    params={
        'time_col': 'date',
        'value_col': 'metric',
        'seasonal_period': 12,
        'threshold': 3.0
    },
    meta={'name': 'anomaly_detection', 'version': '1.0.0'}
)
anomalies = anomaly_model.fit()

Explore Example Notebooks

Visit /examples/notebooks/ for guided walkthroughs:

• End-to-End Workflows: Fetching data with Connectors + analyzing with Model Zoo
• Forecasting labor market trends with BLS data
• Regional specialization analysis using Census CBP data
• Housing market volatility with HUD Fair Market Rent data
• Anomaly detection in CDC health indicators
• Multi-source integration: combining FRED, BLS, and Census data

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Community & Collaboration

KR-Labs thrives on open collaboration and shared intelligence.

You can:

• Contribute new models, tutorials, or datasets
• Report issues or propose features
• Share research Mapplications and case studies
• Join our growing community of open-data practitioners

See our Contributing Guide for details.
Join the discussion at GitHub Discussions.

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Governance & Roadmap

The Model Zoo evolves through the KR-Labs Gate Framework, ensuring transparent, high-quality development:

Gate	Description	Status
Gate 1 – Foundation	Time series & econometric core	Open-Source
Gate 2 – Domain Models	Regional & anomaly detection	Open-Source
Gate 3 – Ensembles	Meta-models and hybrid systems	Enterprise App
Gate 4 – Advanced Extensions	Causal inference, Bayesian, network models	Enterprise App

Each Gate reflects a maturity milestone balancing innovation, stability, and community feedback.

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KR-Labs Ecosystem

The Model Zoo is part of a broader open-source intelligence platform:

Repository	Purpose	Status
krl-model-zoo	Statistical models & analytical frameworks	Production
krl-data-connectors	Unified API for 20+ federal data sources	Production

Why This Matters

Traditional Approach:

1. Manually download CSVs from multiple government websites
2. Write custom parsers for each data format
3. Clean and normalize data structures
4. Finally begin analysis

KR-Labs Approach:

1. pip install krl-data-connectors krl-model-zoo
2. Write analysis code immediately
3. Results in minutes, not days

Key Benefits:

• Single API for Census, BLS, FRED, CDC, HUD, and more
• Pre-validated data compatible with Model Zoo methods
• Reproducible workflows from data fetch to final analysis
• Version control for both data retrieval and model parameters
• Community support for common use cases and patterns

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Documentation

📚 Full Documentation on ReadTheDocs (Coming Soon)

Quick Links

• Quickstart Guide – Get started in 5 minutes
• User Guide – Comprehensive usage documentation
• API Reference – Complete API documentation
• Contributing Guide – Join our open-source community
• Development Guide – Architecture and development practices
• Testing Guide – Quality assurance and test framework

Example Code

• Python Examples – Standalone Python scripts demonstrating model usage
• Jupyter Notebooks – Interactive tutorials with real datasets

Related Documentation

• KRL Data Connectors – Integrate with 20+ federal data sources

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Licensing

• Software: Apache 2.0 License – Free for academic and commercial use
• Documentation: CC-BY-SA-4.0

KR-Labs™ and KRL Model Zoo™ are trademarks of Quipu Research Labs, LLC, a subsidiary of Sundiata Giddasira, Inc.

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Citation

If you use the KRL Model Zoo in research or analysis, please cite:

@software{krl-model-zoo,
  author = {Deloatch, Brandon C.},
  title = {KRL Model Zoo: Open-Source Socioeconomic Modeling Framework},
  Year = {2025},
  publisher = {KR-Labs},
  version = {1.0.0},
  url = {https://github.com/KR-Labs/krl-model-zoo}
}

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Contact & Support

• Website: krlabs.dev
• Email: [e-mail]
• GitHub Discussions: Join the conversation

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KR-Labs™ | Building Open Intelligence for the Public Good

Version 1.0.0 • Apache 2.0 License

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Legal & Trademark Notice

© 2025 KR-Labs. All rights reserved.

KR-Labs™ and KRL Model Zoo™ are trademarks of Quipu Research Labs, LLC, a subsidiary of Sudiata Giddasira, Inc.

Software License: Apache 2.0 – Free for commercial and academic use
Documentation License: CC-BY-SA-4.0

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