MORIE 森 morie hex logo

Multi-domain Open Research and Inferential Estimation.

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.

CI CodeQL License: AGPL-3.0-or-later PyPI version rmorie on r-universe Python 3.10+

Installation

Full step-by-step install guide with platform-specific notes (PEP 668 on Debian, python 3.13 segfault on Raspberry Pi OS, etc.) is at INSTALLATION.md.

morie is a Python (and R) package — once Python is present it is pip install morie. If you are starting with nothing installed, INSTALLATION.md opens with Step 1 — install the prerequisites: every tool you might need (Python, curl, bash/WSL, Git Bash, winget, Homebrew, Docker, R) with its official download. The short version:

  • Windows — install Python from python.org (on the first screen tick Add python.exe to PATH), then pip install morie. Full walkthrough: Windows below. Windows has no curl/bash, so the one-liner does not apply there.
  • macOS / Linux — the one-liner below sets up everything. It needs curl and bash, which macOS has built in and most Linux ships.
  • Already have Python ≥3.10 — just pip install morie.

For terminal users — one-liner (Linux / macOS / WSL)

The simplest path if you have a terminal with curl and bash — both are built into macOS and preinstalled on most Linux (Windows has no bash, so use the installer above instead). It then bootstraps everything else for you: Python via uv, a managed venv, and the morie wheel. No pre-existing Python or pip needed.

curl -fsSL https://rootcoder007.github.io/morie/install.sh | bash

Or, with R alongside Python:

curl -fsSL https://rootcoder007.github.io/morie/install.sh | bash -s -- --auto

After install, ~/.local/bin/morie is a thin shim into the managed venv at ~/.venvs/morie. Full install instructions, channel comparison, and platform-specific notes are at rootcoder007.github.io/morie/#quick-start.

On minimal Linux containers (Alpine, slim Debian) that ship without curl, install it first: apt-get install -y curl or apk add curl. macOS already has curl built in.

Recommended — Windows

Windows doesn't ship curl, bash, python, or R, so the Linux/macOS one-liner above won't run there. The path that works on any Windows with no prerequisites:

  1. Install Python from python.org/downloads — on the first installer screen, tick "Add python.exe to PATH" (skipping this is the No. 1 cause of python being "not recognized" in the terminal).
  2. (Optional — for the R package) install R from cran.r-project.org/bin/windows/base.
  3. Open PowerShell and install morie:
python -m pip install --upgrade pip
python -m pip install morie
python -c "import morie; print(morie.__version__)"

For the R package, install rmorie (the R distribution of morie): Rscript -e "install.packages('rmorie', repos=c('https://rootcoder007.r-universe.dev','https://cloud.r-project.org'))"

Prefer a package manager? If winget --version works on your machine, winget install -e --id Python.Python.3.12 (and RProject.R) installs the prerequisites in one line each — but winget is absent from many Windows installs, so the installer steps above are the reliable default. The full Windows walkthrough, including fixes for common errors (python opening the Microsoft Store, PowerShell execution policy, long-path), is in INSTALLATION.md.

Python — Homebrew (macOS / Linuxbrew)

If you don't have Homebrew yet, install it first (macOS ships curl and bash, so this works out of the box):

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Then:

brew tap rootcoder007/morie
brew install morie

The tap repo is rootcoder007/homebrew-morie. It pulls morie's source distribution from PyPI and bundles a self-contained python@3.12 venv — no system Python required.

Python — PyPI (manual; requires pip already installed)

pip install morie

Heads-up: modern Debian / Ubuntu / Raspberry Pi OS forbid pip outside virtual environments (PEP 668), and the system python3 on Raspberry Pi OS 13 segfaults on importing the SciPy stack. If pip install morie errors or import morie segfaults, use the one-liner above instead — it handles both cases automatically.

Python — Docker (no local dependencies)

# Latest stable
docker run --rm ghcr.io/rootcoder007/morie:latest morie --help

# Pin to a specific version (recommended for reproducibility)
docker run --rm ghcr.io/rootcoder007/morie:latest morie --help

Multi-arch image published on every release with both versioned and :latest tags. Requires only Docker — no Python, no pip.

R package: rmorie — r-universe (CRAN when available)

The R distribution of morie is the rmorie package.

# Stable from CRAN (when listing is live)
install.packages("rmorie")

# Nightly binary builds (recommended while CRAN listing is rolling out)
install.packages(
  "rmorie",
  repos = c(
    rootcoder007 = "https://rootcoder007.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)

What's new

Per-release user-facing changes are now in WHATS_NEW.md. For the R-package changelog see r-package/morie/NEWS.md. For the planned roadmap see ROADMAP.md.

Documentation

Full documentation is at rootcoder007.github.io/morie.

Citation

If you use morie in your research, please cite the software:

Ruhela, V. S. (2026). morie: Multi-domain Open Research and Inferential Estimation. https://github.com/rootcoder007/morie

BibTeX:

@Manual{ruhela_morie_2026,
  title  = {morie: Multi-domain Open Research and Inferential Estimation},
  author = {Vansh Singh Ruhela},
  year   = {2026},
  url    = {https://github.com/rootcoder007/morie},
}

The single citation above covers both the R (r-package/morie/) and Python (src/morie/) implementations, which ship under the same version.

See CITATION.cff for machine-readable citation metadata (BibTeX, etc.) — that file is what GitHub's "Cite this repository" button consumes.

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.

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), 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 licensed under the GNU Affero General Public License, version 3.0 or later (AGPL-3.0-or-later), on both the Python and R sides. The AGPL is a strong copyleft license: anyone who distributes a modified morie — or offers a modified morie to users over a network — must publish their source. Modifications and improvements cannot be kept secret or taken closed-source.

  • Python and R packages (src/morie/, r-package/morie/) — AGPL-3.0-or-later. See LICENSE.
  • Optional Linux kernel adjuncts (kernel-module/morie.c, daemon/morie_lsm.py) — GPL-2.0-only (the Linux kernel ABI requires GPL for loaded modules). These are NOT part of the R / Python distribution; they are separately-licensed, independently-distributed adjuncts. See kernel-module/LICENSE-GPL2.
  • Papers, data and documentation — CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike) unless explicitly marked otherwise.

Full detail in LICENSING.md.

Reporting issues / security

Project links

Data verified by PyPI on Jul 10, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.1.7)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for rootcoder007 from gravatar.com rootcoder007

Credits

Author: Vansh Singh Ruhela

GitHub Statistics

Data verified by PyPI on Jul 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

AGPL-3.0-or-later
View SPDX License List

Requires

Python >=3.10

Provides Extra

test ai sim interactive ml callbacks carbon forensics bigquery docs

Tags

MRM MRM 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 MORIE

Classifiers

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