Getting Started

Install svy-sae and run your first Small Area Estimation model in Python: requirements, dependencies, and where to begin.

Keywords

install svy-sae, small area estimation Python setup, SAE Python getting started

Installation

You can install svy-sae from PyPI:

pip install svy-sae

For isolated environments, any standard workflow works (venv, uv, conda, etc.). If you already use uv, it integrates well with the project’s development setup.

Requirements

svy-sae requires Python 3.12 or newer (3.12–3.14 are supported).

Dependencies

svy-sae is built on a small set of core dependencies:

• JAX / jaxlib — compiled numerical computing; model kernels compile once per machine and are cached to disk, so repeat fits skip compilation
• SciPy — scientific routines used in optimization and numerics
• svy (svy[report]) — survey design and direct estimation utilities used throughout workflows
• tqdm — progress reporting for long-running bootstraps

Usage

svy-sae provides implementations of both area-level and unit-level small area estimation workflows, along with supporting utilities for estimation, diagnostics, and uncertainty quantification.

Start with the Overview and Quick Tour sections to run an end-to-end example, then follow the model-specific tutorials for area-level and unit-level methods.

Development installation (optional)

If you are contributing or working from source, install the project in editable mode and include development dependencies:

pip install -e ".[dev]"

Note: In this repository, svy may be used as a local editable dependency during development.

License

See the LICENSE file in the repository for full terms.