morie 1.1.2

Multi-domain scientific computing toolkit (Python + R) for observational inference and intervention analysis. Provides causal estimators (ATE, ATT, AIPW, DML, G-computation), survey sampling, propensity-score and doubly-robust methods, sensitivity analysis, spatial statistics, Hawkes point processes, and psychometrics, with a unified RichResult output.

pip install morie==1.1.2

MORIE 森

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.

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:

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).
(Optional — for the R package) install R from cran.r-project.org/bin/windows/base.
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.

Trust model — what the powerful features can do to your machine

morie is a developer/research toolkit with some intentionally powerful surfaces. They run local, user-supplied input by design — never remote or network-supplied code — but under an untrusted-input threat model each is high-impact, so every one defaults to the safe behaviour and gates the risky path behind a single, named, off-by-default environment knob:

Surface	What it can do	Safe default → opt-in
`MORIE_NO_EXEC`	master kill-switch for all dynamic execution	executes; set `MORIE_NO_EXEC=1` to disable REPL/exec/shell everywhere
`morie tui` / polyglot REPLs	run Python/R/shell/other code you type, like any REPL	run your input only; honour `MORIE_NO_EXEC=1`. Feed them only code you'd run at your own shell
`pt2gguf`	deserialize `.pt`/`.pkl` model files	`torch.load(weights_only=True)` + a restricted unpickler; the code-executing `weights_only=False` path needs `MORIE_TRUST_CHECKPOINT=1`. Use trusted checkpoints only
`bin/morie` installer — Ollama	remote `install.sh`	fetched to a temp file with its SHA256 printed; executes only under `MORIE_ALLOW_REMOTE_INSTALL=1`
`bin/morie` — `ESML_RC` config	shell config sourced on every run (persistence)	not sourced unless `MORIE_ALLOW_RC=1`
`bin/morie` — `morie cron`	writes your crontab (persistence)	`add`/`remove` refuse unless `MORIE_ALLOW_CRON=1`; `list` is read-only
`bin/morie` — `backup restore`	extracts a tar archive	archives with absolute or `..` paths are refused before extraction

morie doctor prints the current state of every knob so you can see the active trust posture at a glance. Only enable a knob for inputs you fully control.

Reporting issues / security

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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

morie-1.1.2.tar.gz (19.0 MB view details)

Uploaded Jul 11, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

morie-1.1.2-cp314-cp314-win_amd64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.14Windows x86-64

morie-1.1.2-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.14manylinux: glibc 2.24+ x86-64manylinux: glibc 2.28+ x86-64

morie-1.1.2-cp314-cp314-macosx_11_0_arm64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.14macOS 11.0+ ARM64

morie-1.1.2-cp313-cp313-win_amd64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.13Windows x86-64

morie-1.1.2-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.13manylinux: glibc 2.24+ x86-64manylinux: glibc 2.28+ x86-64

morie-1.1.2-cp313-cp313-macosx_11_0_arm64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.13macOS 11.0+ ARM64

morie-1.1.2-cp312-cp312-win_amd64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.12Windows x86-64

morie-1.1.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.12manylinux: glibc 2.24+ x86-64manylinux: glibc 2.28+ x86-64

morie-1.1.2-cp312-cp312-macosx_11_0_arm64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.12macOS 11.0+ ARM64

morie-1.1.2-cp311-cp311-win_amd64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.11Windows x86-64

morie-1.1.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.11manylinux: glibc 2.24+ x86-64manylinux: glibc 2.28+ x86-64

morie-1.1.2-cp311-cp311-macosx_11_0_arm64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.11macOS 11.0+ ARM64

morie-1.1.2-cp310-cp310-win_amd64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.10Windows x86-64

morie-1.1.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.10manylinux: glibc 2.24+ x86-64manylinux: glibc 2.28+ x86-64

morie-1.1.2-cp310-cp310-macosx_11_0_arm64.whl (7.0 MB view details)

Uploaded Jul 11, 2026 CPython 3.10macOS 11.0+ ARM64

File details

Details for the file morie-1.1.2.tar.gz.

File metadata

Download URL: morie-1.1.2.tar.gz
Upload date: Jul 11, 2026
Size: 19.0 MB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for morie-1.1.2.tar.gz
Algorithm	Hash digest
SHA256	`fffedec5319f495adb3cc30c75cb9ffef785f24a6b097bd33fdb1a9a98759a41`
MD5	`9735651eb6f4f5553571ed695abd0ff0`
BLAKE2b-256	`3365cce5c581c3ad2c7e0a2905829a259cf564572b3f2c38607a77231946f63c`

Algorithm	Hash digest
SHA256	`bb29919a170e6da239d8bf2f28edeae78961c58692549e7ec66918821046de3f`
MD5	`735705ce7ad7798474cdae72e014d42d`
BLAKE2b-256	`9be7871ac4ddcf6c4bad99ed93fc6e81e44ac2f214586a5380a843bd37639d33`

Algorithm	Hash digest
SHA256	`968d7e1c831c3d0ad9f9ad274b25d41f72eff68f4c61423521ef349d5abc638a`
MD5	`17891d036b8e18baeef8a39661606d08`
BLAKE2b-256	`31ecde2373cdb506300080c66b92da97f1c7d3224853b9e1e7668bd3e9b5ffc4`

Algorithm	Hash digest
SHA256	`4a6aacdf7f702b42a20bb43aa62910a2d17fbb59229fc463b8cc0ba963693ce0`
MD5	`2ecfe6f13a31cef91f2793feb77bf715`
BLAKE2b-256	`831f342e48a207ad3ceeb48d111ff9d31777105b720172c8fba451e4e8f44c9a`

Algorithm	Hash digest
SHA256	`c864fbbb6de4ab610aaaad1ae4a49e32461c1be28b8b26c0ce8118730b3706c4`
MD5	`d5fa6449a31f04907a4f3693fd1adb97`
BLAKE2b-256	`bd87e623dea8f7801ea82722ca7fd17f2d4a78198b688906ca47f7b8a1249824`

Algorithm	Hash digest
SHA256	`8fc7eacfc0b87e19c807a2e49905d67a69c8b59dd75619cf48510c3015962da7`
MD5	`f6a95cc181d8fdc8e3c0ee8e8611a4e9`
BLAKE2b-256	`3bbfb7e1a9c9cb9e3594232a247fc8dc859ff41abd3ff4dcb6e292b9f870e63c`

Algorithm	Hash digest
SHA256	`e508a91b0b9e6aa320744bc9eb147d4bcc18ee77b1ed5d0e3a30d4cb466f5d6f`
MD5	`fdbc7ddd98d8d5667ff96774cb096f0e`
BLAKE2b-256	`605aede199913f83a226aedaa5254fc5d09a95dee3f825af5516f6fd6203cab1`

Algorithm	Hash digest
SHA256	`ae00f47a4d3b708d59069870e6ff27abe96d5244ba8e758911e4ecae5ab4d6c9`
MD5	`465cac468c2a0368a73d93eee50fb464`
BLAKE2b-256	`6ccf7bced5e13c7cde7331c9e6a94411cfe443a6044ceb5b86a1194b41bea2ec`

Algorithm	Hash digest
SHA256	`662cb742835e529ff0ac765d0c49a413b2b84a25d55b9cb0c6f897155d99058d`
MD5	`dea732b2e527b369cea404b93eaa6870`
BLAKE2b-256	`5480ca671e25347810d703994099d4020781f902ec1ba966e516f23b82dbfa21`

Algorithm	Hash digest
SHA256	`abd21520405182bef196287662f2a92f710ceb8a276afd3fd78e001e22db399b`
MD5	`3f2b75e132f9ca9be2ea29c1fbb034b5`
BLAKE2b-256	`001b8c8dfc111c94088263524d6b9bc5aea258ea6778db5217b5a0617215e61e`

Algorithm	Hash digest
SHA256	`1ca64cabd241f3e2944495073c2c59354dbc3490c9558cc519c8151026379323`
MD5	`bdfe464a7fc2ab02633e68f3b6eafd4a`
BLAKE2b-256	`2784242b6144b9ccd964d69a15f0a7095ed54bc1984995a05896cbd38054af77`

Algorithm	Hash digest
SHA256	`4bc126786ea3fcd4b38103cfb0908fa484354cac981492923813e3553e645e79`
MD5	`bc22f4f9c1e9c65c1e2ed7b60ce3d018`
BLAKE2b-256	`07a8a70eac4ed3eb5b2728c1f9e2afea3d423991103864cd3eb67d2556e08da1`

Algorithm	Hash digest
SHA256	`53e8c22ab6f8c30b31b255f59ae044c2e808d4b8ea14b061576fd24540ae3ddd`
MD5	`dab3ddf376a114bf0d90440f58a2b8cd`
BLAKE2b-256	`95fb83b455a75b5dbbae76192679c01ae4fe57056965150f238161fc439922fc`

Algorithm	Hash digest
SHA256	`4121d2b1ad649b66110dd223bb7ea985e9d93207f803dd3b69fbe755d5fc9d09`
MD5	`0b125b326de50e678cf5e05a48e63c36`
BLAKE2b-256	`be6efe29075e4df680e47fb7c75d980cd8fc4b5cd4f873e742c40d466854142b`

Algorithm	Hash digest
SHA256	`dce70e748a11ce20c2945190cc8f7b19c92f9db65ef624e4d93b08bbff10ab57`
MD5	`b489299a52f57e17188f0846159fcdff`
BLAKE2b-256	`a1b4ebb9348b16bb2ae020764ee15d12ae6ec65f61ba6e4b40c3e55a6823c8c0`

Algorithm	Hash digest
SHA256	`4e3b1336ea5b8caa565079775eaeb9bb99fcc578fa848b79c3d26f355e431046`
MD5	`5782d99a50b0fef40d4062b123ad42ff`
BLAKE2b-256	`8a83ebcba628127b1c221a21fba20d12c6e062cdf5a4c15b72fa91687e7f4629`

morie 1.1.2

MORIE 森

Installation

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

Recommended — Windows

Python — Homebrew (macOS / Linuxbrew)

Python — PyPI (manual; requires pip already installed)

Python — Docker (no local dependencies)

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

Quick start

What's new

Documentation

Citation

Acknowledgments

AI assistance

Funding and infrastructure

Data acknowledgments

License

Trust model — what the powerful features can do to your machine

Reporting issues / security

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License expression

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Python — PyPI (manual; requires `pip` already installed)