morie 0.1.14

Multi-domain Open Research and Inferential Estimation. Multi-domain scientific computing toolkit hosting the MRM framework for Canadian carceral, police, and oversight data, with general-purpose causal inference, signal processing, cryptography, spatial statistics, statistical physics, and psychometrics modules. (Renamed from MOIRAIS in v0.1.3; the moirais module remains as a deprecated alias.)

MORIE

Multi-domain Open Research and Inferential Estimation

Renamed from MOIRAIS in v0.1.3. The moirais Python module and the moirais R package remain available as deprecated aliases — import morie / library(morie) is the canonical entry point going forward.

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 MRM framework as a primary application for Canadian carceral, police, and oversight data analysis.

Installation

Python (PyPI)

pip install morie

R (CRAN)

install.packages("morie")

R (r-universe; nightly binary builds)

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

Quick start

import morie

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

# Run an MRM module on OTIS data
from morie.otis_all_analyze import analyze_a01_mrm
result = analyze_a01_mrm(df)
print(result)

Documentation

Full documentation is at hadesllm.github.io/morie.

Citation

If you use morie in your research, please cite both software papers (R and Python) and, where applicable, the MRM framework paper and the Hawkes methodology paper.

# Software paper — R (also the R package source on Zenodo)
Ruhela, V. S. (2026). morie: Multi-domain Open Research and Inferential
Estimation in R (v0.1.14). Zenodo.
https://doi.org/10.5281/zenodo.20111233

# Software paper — Python (also the Python package source on Zenodo)
Ruhela, V. S. (2026). morie: Multi-domain Open Research and Inferential
Estimation in Python (v0.1.14). Zenodo.
https://doi.org/10.5281/zenodo.20096350

# MRM framework paper (theoretical foundations)
Ruhela, V. S. (2026). MRM Framework: Multi-Source Statistical Foundation
for Canadian Carceral, Police, and Oversight Data (v1). Zenodo.
https://doi.org/10.5281/zenodo.20096075

# Hawkes-process methodology paper
Ruhela, V. S. (2026). Criminological Hawkes Process via MORIE: Markovian
and Non-Markovian Self-Exciting Point Processes for Toronto Crime (v1).
Zenodo. https://doi.org/10.5281/zenodo.20102198

See CITATION.cff for machine-readable citation metadata.

Acknowledgments

AI assistance

MORIE was developed with substantial assistance from frontier AI assistants. The author retains full responsibility for the code, the methods, and the scientific claims; AI assistance accelerated implementation but does not change the attribution of the work.

• Claude — Anthropic. Anthropic's Claude family (Opus, Sonnet, and Haiku across the 4.x generation) was used extensively throughout development for code generation, refactoring, documentation, code review, and design discussions. Use was supported by Anthropic research-credit programs.

• Gemini and Vertex AI — Google. Google's Gemini 2.5 models (Pro and Flash) on the Vertex AI platform were used extensively for additional code generation, cross-checking Claude-generated code, multi-modal data analysis, and prototype evaluation. Use was supported by Google research-credit programs.

Funding and infrastructure

• Anthropic — Claude API research credits.

• Google — Gemini / Vertex AI research credits.

• The author thanks Glenn McNamara — a 35-year career with the Ontario Government — for his methodological mentorship. He brings distribution theory, applied-statistics intuition for administrative data, and the judgment that grounds much of this framework. Glenn is the M in MRM (McNamara-Ruhela-Medina) (catalyst).

• The author thanks Prof. Angela Zorro Medina, Centre for Criminology and Sociolegal Studies, University of Toronto, who is the author's supervisor, methodological instructor, the domain-expert reviewer of the preliminary methodological approach, and a knowledge user of the framework. The methodological lineage MRM follows is established in her work on anti-gang legislation (Zorro Medina, 2023, The Effect of Anti-Gang Laws on Crime and Social Control) — staggered two-way-fixed-effects identification, formal leads-and-lags Granger-causality diagnostics for parallel trends, multi-source data-integration over five jurisdictional sources, deterrence / routine-activities / certainty mechanism categorisation, and the inequality-effects-of-criminal-law framing — all of which directly shape MRM's empirical-statistical spine. Prof. Medina is the M in MRM (supervisor & reviewer).

Data acknowledgments

Several MRM analyses use Statistics Canada and Health Canada Public Use Microdata Files (PUMFs) — including the Canadian Cannabis Survey (CCS), the Canadian Student Alcohol and Drugs Survey (CSADS), the Canadian Substance Use Survey (CSUS), the Canadian Alcohol and Drugs Survey (CADS, 2019; doi.org/10.25318/132500052021001-eng), and the Canadian Postsecondary Education Alcohol and Drug Use Survey (CPADS) — along with Public Health Agency of Canada (PHAC) and Canadian Institute for Health Information (CIHI) aggregates. Although the analyses use Statistics Canada and Health Canada data, the analyses, interpretations, and conclusions are those of the author and do not represent the views of Statistics Canada or Health Canada. Ontario open data (OTIS, A01-RCDD release; via data.ontario.ca) and Toronto Police Service open data are used under the same standard disclaimer.

License

MORIE 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