MORIE 森

Multi-domain Open Research and Inferential Estimation.

License: AGPL-3.0-or-later Python 3.10+ R 4.3+ PyPI version r-universe

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

Quick start

Pick any one channel — each installs the current morie release:

# 1. One-line installer (Linux / macOS / WSL) — detects pip + R, installs both
curl -fsSL https://rootcoder007.github.io/morie/install.sh | bash

# 2. PyPI (any platform with Python ≥3.10)
pip install morie                  # 71 catalogued datasets, 15,222 morie.fn callables
pip install "morie[interactive]"   # + Terminal IDE (TUI)
morie interactive install          # the REPL, exec, agent and TUI modules, once per user
#    Debian / Ubuntu / Raspberry Pi OS refuse system-wide pip: use a venv
python3 -m venv ~/.venvs/morie && source ~/.venvs/morie/bin/activate && pip install -U morie

# 3. Homebrew (macOS / Linuxbrew)
brew tap rootcoder007/morie
brew install morie
morie interactive install

# 4. Docker (zero local dependencies)
docker run --rm ghcr.io/rootcoder007/morie:latest morie --help

# 5. R package: rmorie and its companions rmoriebricklayer and
#    rmoriedata, all from r-universe (CRAN's companions are older)
install.packages("pak", repos = "https://cloud.r-project.org")
pak::repo_add(rootcoder007 = "https://rootcoder007.r-universe.dev")
pak::pkg_install(c("rmoriebricklayer", "rmoriedata", "rmorie"))

Note

Heads-up for Raspberry Pi OS / Debian Trixie users: the system /usr/bin/python3 is python 3.13.5, which segfaults on import for several scientific wheels (a Debian-packaging bug, not a morie bug). Work around it with uv:

curl -LsSf https://astral.sh/uv/install.sh | sh
uv python install 3.12
uv venv ~/.venvs/morie --python 3.12
uv pip install --python ~/.venvs/morie/bin/python morie
~/.venvs/morie/bin/morie --version

Run your first analysis in seconds:

# Launch the Terminal IDE (multi-pane IDE)
morie tui

# Self-diagnostics — checks LLM providers, datasets, R, Docker
morie doctor

# List all 71 catalogued datasets
morie list-datasets

# List all 23 analysis modules
morie list-modules

# Run a single module against built-in data
morie run-module power-design --output-dir /tmp/morie-outputs

# Run the full pipeline (with enlighten progress bars)
morie pipeline --all -y

# Chat with the local model (no API key needed)
morie chat

From R:

library(rmorie)

# Load a built-in dataset by key (CSV corpus from rmoriedata; no file
# paths) and map the PUMF columns to the canonical analysis names
cpads <- morie_canonicalize_cpads_data(morie_load_dataset("ocp21"))

# List all built-in datasets
morie_list_datasets()

# Browse the dataset catalog
morie_dataset_catalog()

# Estimate an average treatment effect
ate <- morie_estimate_ate(cpads, treatment = "cannabis_any_use",
                          outcome = "heavy_drinking_30d",
                          covariates = c("age_group", "gender"))

What MORIE does

A unified Python + R interface across the following surfaces. See Statistical Methods for methodology details and API Reference for function reference.

Causal estimators

ATE, ATT, ATC, GATE, CATE (T- / S-learner), LATE (2SLS / Wald), AIPW, IPW (Hájek), G-computation, propensity-score matching (1:1 NN, 5-strata subclass), Rosenbaum sensitivity bounds, E-value.

Double machine learning

Partially linear regression (PLR), interactive regression model (IRM), partially linear IV (PLIV); cross-fitted with pluggable nuisance learners. Multi-SE comparison (pooled, cluster, multi-way) on the IRM-DML primary estimate. Propensity calibration (Platt / isotonic) on IPW / AIPW / SuperLearner-AIPW with Brier score.

The MRM framework

Multilevel Reconciliation Methodology — a 10-estimator framework applied to OTIS / SIU / TPS data over a coordinated set of (treatment, outcome, covariates) designs. Per-row individual-level + aggregate (Poisson, NB GLM) modes. Mandela classifier (UN Mandela Rules 43 + 44) + provincial-vs-federal cross-comparison.

Spatial statistics

Moran’s \(I\), Geary’s \(C\), Getis-Ord general \(G\), join count, LISA, Getis-Ord \(G_i^{*}\), local Geary, Ripley’s K / L, geostatistical kriging (ordinary, universal, IDW, co-kriging), variogram fitting, GWR (basic, GW-PCA, ST-GWR), bivariate Moran, Moran sweep heatmap, DBSCAN / HDBSCAN, Kulldorff space-time scan.

Hawkes self-exciting point processes

Markovian Mohler-Bertozzi-Brantingham fit (exponential kernel + constant baseline) plus the non-stationary, non-Markovian Kwan-Chen-Dunsmuir (2024) family — Gamma, Weibull, Lomax kernels with sinusoidal baselines. Eight (kernel × baseline) combinations ranked by AIC and time-rescaling Kolmogorov-Smirnov goodness-of-fit.

Statistical physics of crime

Short-Brantingham reaction-diffusion PDE, Brockmann-Hufnagel-Geisel Lévy-flight tail (Hill estimator), Bettencourt urban-scaling exponent (HC3-OLS), D’Orsogna-Perc Lotka-Volterra predator-prey, SDB Turing-pattern demo, Helbing-Szolnoki inspection-game phase diagram, criminal-role co-occurrence networks.

Survey-weighted inference

Horvitz-Thompson totals, Hájek means, ratio estimators, calibration weights (raking / IPF), complex-survey GLM, subpopulation estimates, stratified / cluster / PPS sampling, bootstrap + jackknife variance, design-effect computations, effective-sample-size diagnostics.

Psychometrics

Cronbach’s \(\alpha\), McDonald’s \(\omega_t\) / \(\omega_h\), KMO sampling adequacy, Bartlett’s sphericity, parallel analysis, composite reliability, AVE, item-response-theory fits (1PL / 2PL / 3PL / GRM / PCM), differential item functioning (Mantel-Haenszel, logistic, generalised), measurement invariance, network psychometrics, Bayesian psychometrics. 250+ functions.

Signal processing + cryptography

Spectral analysis, biomedical-signal helpers, homomorphic deconvolution, classical and modern crypto primitives (ChaCha20-Poly1305, etc.), TurboQuant vector quantization with near-optimal distortion (Zandieh et al. 2026 ICLR).

Datasets

71 catalog keys (Canadian carceral, police, and oversight + epidemiological reference data). 70 download themselves on first use and are cached: open.canada.ca, data.ontario.ca, Statistics Canada, CIHI, the Health Infobase tables (with the data.rmorie.com copy as the fallback), the NAPS air-quality files, the Toronto Police feeds, the OTIS research environments from data.rmorie.com and the reviewed SIU corpus from rmoriedata on CRAN. One, the MAPQ workbook, is your own file, resolved through MORIE_DATA_DIR; morie list-datasets shows the route of every key and the curated data.rmorie.com tables once a key from https://rmorie.com/access is stored with morie login --token. Small synthetic samples of the core tables ship in the wheel for tests and tutorials. Auto dataset-profiling for arbitrary tabular input (morie.dataset.profile_dataset).

Function namespace ``morie.fn``

15,222 individual callables indexed by a registry, exposing short stable names for every estimator, every kernel, every weight matrix, every test. Use morie.fn.cheatsheet(name) for a per-function help card.

Federal SIU + Doob T-539-20 replication

Mandela classifier (Rules 43 + 44) with χ² verification, Sprott / Doob (± Iftene) IEDM analyses, full replication of Doob’s CCRSO 2018 Tables 1–3 and the imprisonment-vs-crime decoupling Pettitt change-point test. See Sprott-Doob CRIMSL + Schulich Law SIU analyses, Doob T-539-20 Federal Court affidavit replication, SIU IAP — Federal Structured Intervention Unit Implementation Advisory Panel.

Toronto Police Service surface

morie.tps_* modules: incident I/O, CSI, neighbourhood spatial / temporal analyses, Hawkes (basic + advanced), statistical physics, Hohl-style choropleths and proportional-symbol district maps. (See CITATION.cff for the companion Hawkes paper.)

LLM + assistant

The model you bring, in order: your own OpenAI-compatible server → a provider whose key is set (OpenAI, Anthropic, Gemini, OpenRouter, Groq, Mistral and more) → an Ollama server → a keyword-matched local fallback that needs no network. Nothing is hosted for you. See Language models and the data key. Vendored TurboQuant KV-cache compression. Polyglot REPL bridges variables across Python ↔ R ↔ shell ↔ 12 other languages.

Carbon-aware computing

Built-in pure-Python emissions tracker (morie.emissions) with 213-country IEA carbon-intensity data, per-module and pipeline-wide CO₂ accounting. CodeCarbon fallback on Python ≤ 3.14.

Key design principles

Lean terminal IDE.

Rich terminal output — progress bars, formatted ASCII tables, color-coded diagnostics. Run entire pipelines from a single morie command.

Python + R parity.

Every statistical estimator is implemented in both languages with matching morie_* names and arguments, and the two arms are checked against each other in the test suite. Neither arm depends on NumPy, pandas or an external estimator package: the numerics run on the family’s own cores.

Automated documentation.

Python API docs via Sphinx autodoc. R API docs via Roxygen2 → .. r:function:: (no manual writing). Run devtools::document() to regenerate.

Data governance built-in.

Raw microdata is never committed: the real files are fetched from their source portals and cached locally. Synthetic data (morie_generate_synthetic_data()) is labeled synthetic in all outputs, and morie verify validates module outputs after a run.

Statistically rigorous.

Target estimand is always an explicit parameter (ATE vs ATT vs CATE — never implicit). Overlap/positivity violations raise explicit warnings. Cross-fitting prevents data leakage. Convergence diagnostics are built into MCMC outputs.

Background

MORIE is a multi-domain scientific computing toolkit for observational inference. It sits between one-off research scripts and heavy enterprise analytics platforms, and is aimed at researchers who need:

  • A unified Python + R surface across the same estimators (no language-choice tax).

  • Causal estimators (ATE / ATT / ATC / GATE / CATE / LATE, AIPW, G-computation, DML–PLR, DML–IRM, propensity-score matching, E-value and Rosenbaum-bound sensitivity) with explicit estimands.

  • Survey-weighted inference (Horvitz-Thompson, Hájek, raking, cluster + stratified design) on top of the same DataFrame as the causal layer.

  • Spatial statistics (Moran’s \(I\), LISA, Getis-Ord \(G^{*}\), DBSCAN, Kulldorff space-time scan), Hawkes self-exciting point processes (Markovian and non-Markovian), and the statistical-physics-of-crime models (Short-Brantingham reaction-diffusion, Lévy-flight tail, Bettencourt urban scaling, Lotka-Volterra) — applied as first-class methods on the Toronto Police Service open-data feeds.

  • Reproducible pipelines that run unattended in CI / CD — outputs carry provenance manifests; synthetic data is labelled as such.

  • The MRM (Multilevel Reconciliation Methodology) framework as a primary application for Canadian carceral, police, and oversight data (Ontario OTIS, federal SIU, TPS).

The package catalogues 71 datasets (Canadian carceral, police, and oversight + epidemiological reference data): 70 download themselves from their portal, from rmoriedata or from data.rmorie.com on first use, one (the MAPQ workbook) is your own file placed under MORIE_DATA_DIR; the core tables also ship as synthetic samples.

MORIE is licensed under AGPL-3.0-or-later (Python and R). The AGPL is a strong copyleft license: a modified MORIE that is distributed, or offered to users over a network, must publish its source. The optional Linux-kernel adjuncts (kernel-module/morie.c and daemon/morie_lsm.py) remain GPL-2.0-only because the kernel ABI requires it; they are NOT part of the R / Python distribution. See the LICENSE file for the full text and LICENSING.md for the per-component breakdown.

Documentation index

If you prefer a single linear walkthrough rather than the sidebar navigation, every page on this site is listed below — top to bottom:

  • From Zero — Start Here If You’ve Never Done This Before — From-zero tutorial track. Start here if you have never opened a Python or R console before.

  • Installation — Installation instructions for Python, R, macOS, Linux, Windows, plus LLM provider setup.

  • CLI Reference — Reference for every morie … subcommand.

  • Language models and the data key — Language models (bring your own: providers, your own server, Ollama) and the data key for the curated tables.

  • Statistical Methods — Statistical-methods reference. Estimands, causal estimators, survey statistics, spatial methods, Hawkes processes, statistical physics of crime, OTIS / TPS / SIU pipelines, the MRM framework, key empirical findings.

  • API Reference — Python and R API reference (function signatures and docstrings).

  • Contributing — Development setup, test conventions, module-addition guide.