Skip to main content

skillmodels

Introduction

Welcome to skillmodels, a Python implementation of estimators for skill formation models. The econometrics of skill formation models is a very active field and several estimators were proposed. None of them is implemented in standard econometrics packages.

Installation

Warning: To run skillmodels you need to install jax and jaxlib. At the time of writing, in most use cases, it is faster on a CPU than on a GPU, so it should be sufficient to install the CPU version, which is available on all platforms. In any case, for installation of jax and jaxlib, please consult the jax docs.

Skillmodels can be installed via PyPI or via GitHub. To do so, type the following in a terminal:

$ pip install skillmodels

or, for the latest development version, type:

$ pip install git+https://github.com/OpenSourceEconomics/skillmodels.git

Documentation

The documentation is hosted at readthedocs

Developing

We use pixi for our local development environment. If you want to work with or extend the skillmodels code base you can run the tests using

$ git clone https://github.com/OpenSourceEconomics/skillmodels.git
$ pixi run tests

This will install the development environment and run the tests. You can run mypy using

$ pixi run mypy

Before committing, install the pre-commit hooks using

$ pre-commit install

Documentation

You can build the documentation locally. After cloning the repository you can cd to the docs directory and type:

$ make html

Citation

It took countless hours to write skillmodels. I make it available under a very permissive license in the hope that it helps other people to do great research that advances our knowledge about the formation of cognitive and noncognitive skills. If you find skillmodels helpful, please don't forget to cite it. Below you can find the bibtex entry for a suggested citation. The suggested citation will be updated once the code becomes part of a published paper.

@Unpublished{Gabler2024,
  Title                    = {A Python Library to Estimate Nonlinear Dynamic Latent Factor Models},
  Author                   = {Janos Gabler},
  Year                     = {2024},
  Url                      = {https://github.com/OpenSourceEconomics/skillmodels}
}

Feedback

If you find skillmodels helpful for research or teaching, please let me know. If you encounter any problems with the installation or while using skillmodels, please complain or open an issue at GitHub

Metadata

Release files for skillmodels 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for skillmodels 0.1.2
File Size Uploaded Provenance Actions
skillmodels-0.1.2.tar.gz 1.3 MB With provenance Details for skillmodels-0.1.2.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for skillmodels 0.1.2
File Interpreter ABI Platform Actions
skillmodels-0.1.2-py3-none-any.whl Python 3 none any Details for skillmodels-0.1.2-py3-none-any.whl

Total release size: 2.7 MB

Release files / skillmodels-0.1.2.tar.gz

Download URL skillmodels-0.1.2.tar.gz
Size 1.3 MB
Tags Source
SHA-256 checksum
How to use checksums
2c8018bd21bea9cb1c8d580c0a1287ab6e9283772bb424f3bbdae0143eecd755
BLAKE2b-256 checksum
How to use checksums
ff4bd1dc1a06fab8fc3bd30da0f67317011977e69b865007a7a916620da3dad4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 29, 2026.

Transparency log

Release files / skillmodels-0.1.2-py3-none-any.whl

Download URL skillmodels-0.1.2-py3-none-any.whl
Size 1.4 MB
Tags Python 3
SHA-256 checksum
How to use checksums
161bd4585f06fc431fe737c326222584ac18506978595391434255f3c44a9629
BLAKE2b-256 checksum
How to use checksums
780bad3ccae00cf1d130fca51e065fdc8dcb794a59c650457b7e3c4d89111b2e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 29, 2026.

Transparency log

Security for skillmodels 0.1.2

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for skillmodels-0.1.2.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 29, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page