moirais 0.1.0.post1

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.

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.

Installation

Python (PyPI)

pip install moirais

R (CRAN)

install.packages("moirais")

R (r-universe; nightly binary builds)

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

Quick start

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 (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 for machine-readable citation metadata.

License

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

Reporting issues / security

• General issues: GitHub Issues
• Security vulnerabilities: see SECURITY.md