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aimz: Scalable probabilistic impact modeling\n\n[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n![Run Pytest](https://github.com/markean/aimz/actions/workflows/coverage.yaml/badge.svg)\n[![PyPI](https://img.shields.io/pypi/v/aimz)](https://pypi.org/project/aimz/)\n[![Conda](https://img.shields.io/conda/vn/conda-forge/aimz.svg)](https://anaconda.org/conda-forge/aimz)\n[![Python](https://img.shields.io/pypi/pyversions/aimz.svg)](https://pypi.org/project/aimz/)\n[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)\n[![codecov](https://codecov.io/gh/markean/aimz/graph/badge.svg?token=34OH7KQBXE)](https://codecov.io/gh/markean/aimz)\n[![DOI](https://zenodo.org/badge/1009062911.svg)](https://doi.org/10.5281/zenodo.16101876)\n\n[**Installation**](https://aimz.readthedocs.io/stable/getting_started/installation.html) |\n[**Tutorial**](https://aimz.readthedocs.io/latest/getting_started/tutorial.html) |\n[**User Guide**](https://aimz.readthedocs.io/latest/user_guide/index.html) |\n[**FAQs**](https://aimz.readthedocs.io/latest/faq.html) |\n[**Changelog**](https://aimz.readthedocs.io/latest/changelog.html)\n\n## Overview\n\naimz is a Python library for scalable probabilistic impact modeling, enabling assessment of intervention effects on outcomes with a streamlined interface for fitting, sampling, prediction, and effect estimation—minimal boilerplate, accelerated execution, and powered by [NumPyro](https://num.pyro.ai/en/stable/), [JAX](https://jax.readthedocs.io/en/latest/), [Xarray](https://xarray.dev/), and [Zarr](https://zarr.readthedocs.io/en/stable/).\n\n## Features\n\n- Intuitive API combining the ease of use from ML frameworks with the flexibility of probabilistic modeling.\n- Accelerated computation via parallelism and distributed data.\n- Support for interventional causal inference for counterfactuals and causal effects.\n- MLflow integration for experiment tracking and model management.\n\n## Usage\n\n```python\nfrom aimz import ImpactModel\n\n# Define probabilistic model (kernel) using Numpyro primitives\ndef model(X, y=None):\n    ...\n\n# Load or prepare data\nX, y = ...\n\n# Initialize ImpactModel\nim = ImpactModel(\n    model,\n    rng_key=...,      # e.g., jax.random.key(0)\n    inference=...,    # e.g., SVI (or MCMC)\n)\n\n# Fit model and draw posterior samples\nim.fit(X, y)\n\n# Make predictions or posterior predictive samples\ndt = im.predict(X)\n```\n\n## Contributing\n\nSee the [Contributing Guide](https://aimz.readthedocs.io/latest/development/contributing.html) to get started.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkean%2Faimz","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarkean%2Faimz","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkean%2Faimz/lists"}