Skip to main content

Machine Learning approaches to Stochastic Frontier Analysis

Project description

ML-SFA

Machine Learning approaches to Stochastic Frontier Analysis.

Installation

pip install ml-sfa

# With neural network support
pip install ml-sfa[nn]

# With BART-SFM support
pip install ml-sfa[bart]

Development

uv sync --frozen --dev
make ci

License

MIT

Project details


Download files

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

Source Distribution

ml_sfa-0.1.0.tar.gz (228.0 kB view details)

Uploaded Source

Built Distribution

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

ml_sfa-0.1.0-py3-none-any.whl (34.2 kB view details)

Uploaded Python 3

File details

Details for the file ml_sfa-0.1.0.tar.gz.

File metadata

  • Download URL: ml_sfa-0.1.0.tar.gz
  • Upload date:
  • Size: 228.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ml_sfa-0.1.0.tar.gz
Algorithm Hash digest
SHA256 14fbe49dc8080a4a1c3448452b39658bead4ed6925e9b839a3a948796da57401
MD5 4218ad71b1700d25a1b293ccd9a51265
BLAKE2b-256 88a313f0b80e99d057ebf35005e5b6fcd861edc0f58bb8e5475642ff62eaae40

See more details on using hashes here.

Provenance

The following attestation bundles were made for ml_sfa-0.1.0.tar.gz:

Publisher: release.yml on nbx-liz/ML-SFA

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ml_sfa-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: ml_sfa-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 34.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ml_sfa-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 8b7cadd5505db6a69341a4b91960fe669c3b0203289765e585bff4d12f818e3b
MD5 2b39256453e4e6c1752be28243cd8181
BLAKE2b-256 647f82fc0509d1ec4a9944ed93ac8b12d6ae526a2abb961118eff4deaafda71b

See more details on using hashes here.

Provenance

The following attestation bundles were made for ml_sfa-0.1.0-py3-none-any.whl:

Publisher: release.yml on nbx-liz/ML-SFA

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page