Metadata-Version: 2.4
Name: moirais
Version: 0.1.0.post1
Summary: Methods for Observational Inference and Robust Analysis of Interventions in Scientific Experimentation. Multi-domain scientific computing toolkit hosting the DLRM framework for Canadian carceral, police, and oversight data, with general-purpose causal inference, signal processing, cryptography, spatial statistics, statistical physics, and psychometrics modules.
Author-email: Vansh Singh Ruhela <hadesllm@proton.me>
License-Expression: GPL-2.0-only
Project-URL: Homepage, https://github.com/hadesllm/moirais
Project-URL: Repository, https://github.com/hadesllm/moirais
Project-URL: Issues, https://github.com/hadesllm/moirais/issues
Project-URL: Documentation, https://github.com/hadesllm/moirais#readme
Project-URL: Changelog, https://github.com/hadesllm/moirais/releases
Keywords: DLRM,RF modules,causal inference,observational inference,criminology,Mandela Rules,OTIS,Special Investigations Unit,Structured Intervention Unit,Toronto Police Service,Crime Severity Index,Hawkes process,spatial statistics,signal processing,psychometrics,double machine learning,MOIRAIS
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Legal Industry
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: R
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Topic :: Security :: Cryptography
Classifier: Topic :: Sociology
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=2.0
Requires-Dist: numpy>=1.24
Requires-Dist: scipy>=1.10
Requires-Dist: scikit-learn>=1.3
Requires-Dist: statsmodels>=0.14
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Provides-Extra: test
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Provides-Extra: docs
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Requires-Dist: myst-parser; extra == "docs"
Dynamic: license-file

# MOIRAIS

**Methods for Observational Inference and Robust Analysis of Interventions in Scientific Experimentation**

A multi-domain scientific computing toolkit (Python and R) for observational inference, with sociolegal, signal-processing, cryptographic, spatial-statistics, statistical-physics, and psychometrics modules. Hosts the DLRM framework as a primary application for Canadian carceral, police, and oversight data analysis.

[![License: GPL v2](https://img.shields.io/badge/License-GPL_v2-blue.svg)](LICENSE)
[![PyPI version](https://img.shields.io/pypi/v/moirais.svg)](https://pypi.org/project/moirais/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.20096350-blue)](https://doi.org/10.5281/zenodo.20096350)

## Installation

### Python (PyPI)

```bash
pip install moirais
```

### R (CRAN)

```r
install.packages("moirais")
```

### R (r-universe; nightly binary builds)

```r
install.packages(
  "moirais",
  repos = c(
    hadesllm = "https://hadesllm.r-universe.dev",
    CRAN     = "https://cloud.r-project.org"
  )
)
```

## Quick start

```python
import moirais

# Load a built-in dataset
df = moirais.load_dataset("otis-2025")

# Run a Ruhela formulation (DLRM primary)
from moirais.otis_all_analyze import analyze_a01_ruhela_formulations
result = analyze_a01_ruhela_formulations(df)
print(result)
```

## Documentation

Full documentation is at [hadesllm.github.io/moirais](https://hadesllm.github.io/moirais/) (auto-built from `docs/source/`).

## Citation

If you use MOIRAIS in your research, please cite the package paper, the DLRM framework paper, and (where applicable to your work) the Hawkes-process methodology paper:

```
Ruhela, V. S. (2026). MOIRAIS: A Multi-Domain Scientific Computing
Toolkit for Observational Inference, with Sociolegal, Signal-Processing,
Cryptographic, and Spatial-Statistics Modules. Zenodo.
https://doi.org/10.5281/zenodo.20096350

Ruhela, V. S. (2026). The DLRM Framework: A Multi-Source Mathematical
Foundation for Canadian Carceral, Police, and Oversight Data, Implemented
as RF Modules in MOIRAIS. Zenodo.
https://doi.org/10.5281/zenodo.20096075

Ruhela, V. S. (2026). Criminological Hawkes Process via MOIRAIS:
Markovian and Non-Markovian Self-Exciting Point Processes for Toronto
Crime. Zenodo.
https://doi.org/10.5281/zenodo.20102198
```

See [`CITATION.cff`](CITATION.cff) for machine-readable citation metadata.

## License

MOIRAIS is released under the GNU General Public License v2 (`GPL-2.0-only`); see [`LICENSE`](LICENSE). The licensing matrix for individual components is documented in [`LICENSING.md`](LICENSING.md).

## Reporting issues / security

- General issues: [GitHub Issues](https://github.com/hadesllm/moirais/issues)
- Security vulnerabilities: see [`SECURITY.md`](.github/SECURITY.md)
