Update PolicyEngine branding

AGENTS.md

@@ -58,8 +58,8 @@ When reviewing recent changes here, check:
 
 For a quick review, read:
 
-1. [`/Users/maxghenis/CosilicoAI/microplex/AGENTS.md`](/Users/maxghenis/CosilicoAI/microplex/AGENTS.md)
-2. [`/Users/maxghenis/CosilicoAI/microplex/_WORKSPACE.md`](/Users/maxghenis/CosilicoAI/microplex/_WORKSPACE.md)
-3. [`/Users/maxghenis/CosilicoAI/microplex/_BUILD_LOG.md`](/Users/maxghenis/CosilicoAI/microplex/_BUILD_LOG.md)
+1. [`/Users/maxghenis/PolicyEngine/microplex/AGENTS.md`](/Users/maxghenis/PolicyEngine/microplex/AGENTS.md)
+2. [`/Users/maxghenis/PolicyEngine/microplex/_WORKSPACE.md`](/Users/maxghenis/PolicyEngine/microplex/_WORKSPACE.md)
+3. [`/Users/maxghenis/PolicyEngine/microplex/_BUILD_LOG.md`](/Users/maxghenis/PolicyEngine/microplex/_BUILD_LOG.md)
 
 Then inspect changed files and return findings first.

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 Cosilico
+Copyright (c) 2024 PolicyEngine
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

QRF_BENCHMARK_SUMMARY.md

@@ -29,7 +29,7 @@ This benchmark compares **microplex** (normalizing flows with two-stage zero-inf
 
 ### Recommendation
 
-**Transition from Sequential QRF to microplex for PolicyEngine/Cosilico production use.**
+**Transition from Sequential QRF to microplex for PolicyEngine/PolicyEngine production use.**
 
 microplex provides superior statistical fidelity while being significantly faster, making it ideal for:
 - CPS/ACS income imputation
@@ -186,7 +186,7 @@ microplex provides superior statistical fidelity while being significantly faste
 ### Run the Benchmark
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 
 # Install dependencies
 pip install scikit-learn>=1.3 matplotlib seaborn
@@ -218,7 +218,7 @@ Results saved to `benchmarks/results/`:
 
 ## Conclusion
 
-**microplex is the superior choice for PolicyEngine/Cosilico microdata enhancement.**
+**microplex is the superior choice for PolicyEngine/PolicyEngine microdata enhancement.**
 
 The benchmarks demonstrate:
 1. ✅ **5.5x better marginal fidelity** - Critical for accurate policy estimates

README.md

@@ -3,8 +3,8 @@
 Multi-source microdata synthesis and survey reweighting.
 
 [![PyPI](https://img.shields.io/pypi/v/microplex.svg)](https://pypi.org/project/microplex/)
-[![Tests](https://github.com/CosilicoAI/microplex/actions/workflows/test.yml/badge.svg)](https://github.com/CosilicoAI/microplex/actions/workflows/test.yml)
-[![Docs](https://github.com/CosilicoAI/microplex/actions/workflows/docs.yml/badge.svg)](https://cosilicoai.github.io/microplex)
+[![Tests](https://github.com/PolicyEngine/microplex/actions/workflows/test.yml/badge.svg)](https://github.com/PolicyEngine/microplex/actions/workflows/test.yml)
+[![Docs](https://github.com/PolicyEngine/microplex/actions/workflows/docs.yml/badge.svg)](https://policyengine.github.io/microplex)
 
 ## Overview
 
@@ -145,11 +145,11 @@ print(f"Using {stats['n_nonzero']} of {stats['n_records']} records")
 
 ## Documentation
 
-Full documentation at [cosilicoai.github.io/microplex](https://cosilicoai.github.io/microplex)
+Full documentation at [policyengine.github.io/microplex](https://policyengine.github.io/microplex)
 
-- [Tutorial](https://cosilicoai.github.io/microplex/tutorial.html)
-- [API Reference](https://cosilicoai.github.io/microplex/api.html)
-- [Benchmarks](https://cosilicoai.github.io/microplex/benchmarks.html)
+- [Tutorial](https://policyengine.github.io/microplex/tutorial.html)
+- [API Reference](https://policyengine.github.io/microplex/api.html)
+- [Benchmarks](https://policyengine.github.io/microplex/benchmarks.html)
 
 ## Benchmarks
 
@@ -166,7 +166,7 @@ See [benchmarks/](benchmarks/) for synthesis method comparisons:
   author = {Ghenis, Max},
   title = {microplex: Multi-source microdata synthesis and survey reweighting},
   year = {2025},
-  url = {https://github.com/CosilicoAI/microplex}
+  url = {https://github.com/PolicyEngine/microplex}
 }
 ```
 

_WORKSPACE.md

@@ -8,8 +8,8 @@ This file is the durable local context for `microplex` core.
 
 Sibling repos:
 
-- [`/Users/maxghenis/CosilicoAI/microplex-us`](/Users/maxghenis/CosilicoAI/microplex-us)
-- [`/Users/maxghenis/CosilicoAI/microplex-uk`](/Users/maxghenis/CosilicoAI/microplex-uk)
+- [`/Users/maxghenis/PolicyEngine/microplex-us`](/Users/maxghenis/PolicyEngine/microplex-us)
+- [`/Users/maxghenis/PolicyEngine/microplex-uk`](/Users/maxghenis/PolicyEngine/microplex-uk)
 
 ## Current shared seams
 

benchmarks/DISTRIBUTIONAL_METRICS.md

@@ -201,32 +201,32 @@ predictions += np.random.normal(0, fixed_noise_scale)  # Fixed noise
 ## Files Created
 
 ### Core Metrics Module
-- `/Users/maxghenis/CosilicoAI/micro/benchmarks/metrics.py`
+- `/Users/maxghenis/PolicyEngine/micro/benchmarks/metrics.py`
   - All distributional quality metrics
   - Comprehensive evaluation function
   - Report generation utilities
 
 ### Benchmark Scripts
-- `/Users/maxghenis/CosilicoAI/micro/benchmarks/run_distributional_benchmark.py`
+- `/Users/maxghenis/PolicyEngine/micro/benchmarks/run_distributional_benchmark.py`
   - Runs full distributional quality benchmark
   - Generates visualizations and report
   - Compares QRF vs microplex
 
 ### Results
-- `/Users/maxghenis/CosilicoAI/micro/benchmarks/results/distributional_quality.md`
+- `/Users/maxghenis/PolicyEngine/micro/benchmarks/results/distributional_quality.md`
   - Full analysis report
-- `/Users/maxghenis/CosilicoAI/micro/benchmarks/results/distributional_metrics.json`
+- `/Users/maxghenis/PolicyEngine/micro/benchmarks/results/distributional_metrics.json`
   - Raw metrics in JSON format
-- `/Users/maxghenis/CosilicoAI/micro/benchmarks/results/distributional_*.png`
+- `/Users/maxghenis/PolicyEngine/micro/benchmarks/results/distributional_*.png`
   - 5 visualization files
 
 ## Usage
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 
 # Run full distributional benchmark
-/Users/maxghenis/CosilicoAI/micro/.venv/bin/python benchmarks/run_distributional_benchmark.py
+/Users/maxghenis/PolicyEngine/micro/.venv/bin/python benchmarks/run_distributional_benchmark.py
 
 # Results will be saved to benchmarks/results/
 ```
@@ -262,4 +262,4 @@ cd /Users/maxghenis/CosilicoAI/micro
 ---
 
 **Created:** December 25, 2024
-**Location:** `/Users/maxghenis/CosilicoAI/micro/benchmarks/DISTRIBUTIONAL_METRICS.md`
+**Location:** `/Users/maxghenis/PolicyEngine/micro/benchmarks/DISTRIBUTIONAL_METRICS.md`

benchmarks/MULTIVARIATE_METRICS.md

@@ -183,7 +183,7 @@ This tests the full joint distribution, not just target variables.
 ### Running the Benchmark
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 source .venv/bin/activate
 python benchmarks/run_multivariate_benchmark.py
 ```

benchmarks/README.md

@@ -56,6 +56,6 @@ benchmarks/results/
   author = {Ghenis, Max},
   title = {microplex: Multi-source microdata synthesis and survey reweighting},
   year = {2025},
-  url = {https://github.com/CosilicoAI/microplex}
+  url = {https://github.com/PolicyEngine/microplex}
 }
 ```

benchmarks/compare_policyengine.py

@@ -945,7 +945,7 @@ def generate_report(
 ## Reproducibility
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 source .venv/bin/activate
 python benchmarks/compare_policyengine.py
 ```

benchmarks/results/BENCHMARK_REPORT.md

@@ -295,7 +295,7 @@ For PolicyEngine and economic microsimulation applications, **microplex is the r
 
 ## Files Generated
 
-All benchmark artifacts saved to `/Users/maxghenis/CosilicoAI/micro/benchmarks/results/`:
+All benchmark artifacts saved to `/Users/maxghenis/PolicyEngine/micro/benchmarks/results/`:
 
 - `results.csv` - Summary metrics table
 - `results.md` - Markdown results
@@ -312,7 +312,7 @@ All benchmark artifacts saved to `/Users/maxghenis/CosilicoAI/micro/benchmarks/r
 To reproduce these benchmarks:
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 python benchmarks/run_benchmarks.py
 ```
 

benchmarks/results/INDEX.md

@@ -200,7 +200,7 @@ If using these results:
 ```bibtex
 @misc{microplex_benchmarks_2024,
   title={microplex Benchmark Results: Comparison Against CT-GAN, TVAE, and Copula},
-  author={Cosilico},
+  author={PolicyEngine},
   year={2024},
   note={Results show 3.3x better marginal fidelity, 1.7x better correlation
         preservation, and 2.5x better zero-inflation handling}
@@ -212,7 +212,7 @@ If using these results:
 All results are fully reproducible:
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 python benchmarks/run_benchmarks.py
 ```
 
@@ -224,7 +224,7 @@ python benchmarks/run_benchmarks.py
 ## Contact
 
 For questions about these benchmarks:
-- Open an issue at github.com/CosilicoAI/microplex
+- Open an issue at github.com/PolicyEngine/microplex
 - See BENCHMARK_REPORT.md for technical details
 - See ISSUES_FOUND.md for known limitations
 

benchmarks/results/ISSUES_FOUND.md

@@ -311,6 +311,6 @@ result.median_dcr = np.median(distances)
 The opportunities identified above would make the benchmarks more comprehensive and demonstrate microplex's value even more clearly, but the current results already show:
 - **Clear superiority** across all fidelity metrics
 - **Practical performance** for real-world use
-- **Ready for production** deployment in PolicyEngine/Cosilico
+- **Ready for production** deployment in PolicyEngine/PolicyEngine
 
 Main next step: **Test on real microdata** (CPS, ACS) to validate performance claims.

benchmarks/results/README.md

@@ -205,22 +205,22 @@ See **ISSUES_FOUND.md** for detailed improvement opportunities:
 ### Original Benchmarks (vs CT-GAN, TVAE, Copula)
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 python benchmarks/run_benchmarks.py
 ```
 
 ### QRF Comparison Benchmarks
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
-/Users/maxghenis/CosilicoAI/micro/.venv/bin/python benchmarks/run_qrf_benchmark.py
+cd /Users/maxghenis/PolicyEngine/micro
+/Users/maxghenis/PolicyEngine/micro/.venv/bin/python benchmarks/run_qrf_benchmark.py
 ```
 
 ### Distributional Quality Benchmarks (NEW)
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
-/Users/maxghenis/CosilicoAI/micro/.venv/bin/python benchmarks/run_distributional_benchmark.py
+cd /Users/maxghenis/PolicyEngine/micro
+/Users/maxghenis/PolicyEngine/micro/.venv/bin/python benchmarks/run_distributional_benchmark.py
 ```
 
 All results are deterministic (fixed random seed = 42).

benchmarks/results/cps_benchmark.md

@@ -151,7 +151,7 @@ While QRF+ZI won this benchmark, microplex may still be preferred when:
 ## Reproducibility
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/microplex
+cd /Users/maxghenis/PolicyEngine/microplex
 .venv/bin/python benchmarks/run_cps_benchmark.py
 ```
 

benchmarks/results/distributional_quality.md

@@ -385,7 +385,7 @@ This benchmark tests whether synthetic data methods capture the **full condition
 ## Reproducibility
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 python benchmarks/run_distributional_benchmark.py
 ```
 

benchmarks/results/policyengine_comparison.md

@@ -113,7 +113,7 @@ Based on these benchmarks:
 ## Reproducibility
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 source .venv/bin/activate
 python benchmarks/compare_policyengine.py
 ```

benchmarks/results/qrf_comparison.md

@@ -146,7 +146,7 @@
 - Joint distribution quality matters (policy analysis, microsimulation)
 - You need conditional relationships preserved
 - Zero-inflated economic variables are present
-- You're doing production deployment (PolicyEngine/Cosilico)
+- You're doing production deployment (PolicyEngine/PolicyEngine)
 
 ## Recommendations for PolicyEngine
 
@@ -189,7 +189,7 @@ All visualizations saved to `benchmarks/results/`:
 ## Reproducibility
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 python benchmarks/run_qrf_benchmark.py
 ```
 

benchmarks/results/tabpfn_comparison.md

@@ -118,7 +118,7 @@ TabPFN+ZI performs best on zero-inflated variables (assets, debt), while micropl
 
 ### Recommendations
 
-1. **For PolicyEngine/Cosilico production**: Continue using microplex
+1. **For PolicyEngine/PolicyEngine production**: Continue using microplex
    - Better correlation preservation is critical for policy simulation
    - Generation speed matters for interactive applications
    - Scales to full CPS/ACS datasets
@@ -135,7 +135,7 @@ TabPFN+ZI performs best on zero-inflated variables (assets, debt), while micropl
 ## Reproducibility
 
 ```bash
-cd /Users/maxghenis/CosilicoAI/micro
+cd /Users/maxghenis/PolicyEngine/micro
 source .venv/bin/activate
 pip install tabpfn==0.1.11  # Must use v0.1.11 (later versions are gated)
 python benchmarks/run_tabpfn_benchmark.py
@@ -153,4 +153,4 @@ Results are deterministic with random seed = 42.
 ---
 
 **Generated:** December 26, 2024
-**Location:** /Users/maxghenis/CosilicoAI/micro/benchmarks/results/
+**Location:** /Users/maxghenis/PolicyEngine/micro/benchmarks/results/

benchmarks/run_cps_benchmark.py

@@ -1108,7 +1108,7 @@ def generate_cps_markdown_report(
 
         f.write("## Reproducibility\n\n")
         f.write("```bash\n")
-        f.write("cd /Users/maxghenis/CosilicoAI/microplex\n")
+        f.write("cd /Users/maxghenis/PolicyEngine/microplex\n")
         f.write("python benchmarks/run_cps_benchmark.py\n")
         f.write("```\n\n")
 

benchmarks/run_distributional_benchmark.py

@@ -425,7 +425,7 @@ def generate_distributional_markdown_report(
 
         f.write("## Reproducibility\n\n")
         f.write("```bash\n")
-        f.write("cd /Users/maxghenis/CosilicoAI/micro\n")
+        f.write("cd /Users/maxghenis/PolicyEngine/micro\n")
         f.write("python benchmarks/run_distributional_benchmark.py\n")
         f.write("```\n\n")
 

benchmarks/run_qrf_benchmark.py

@@ -420,7 +420,7 @@ def generate_qrf_markdown_report(
         f.write("- Joint distribution quality matters (policy analysis, microsimulation)\n")
         f.write("- You need conditional relationships preserved\n")
         f.write("- Zero-inflated economic variables are present\n")
-        f.write("- You're doing production deployment (PolicyEngine/Cosilico)\n\n")
+        f.write("- You're doing production deployment (PolicyEngine/PolicyEngine)\n\n")
 
         f.write("## Recommendations for PolicyEngine\n\n")
 
@@ -466,7 +466,7 @@ def generate_qrf_markdown_report(
 
         f.write("## Reproducibility\n\n")
         f.write("```bash\n")
-        f.write("cd /Users/maxghenis/CosilicoAI/micro\n")
+        f.write("cd /Users/maxghenis/PolicyEngine/micro\n")
         f.write("python benchmarks/run_qrf_benchmark.py\n")
         f.write("```\n\n")