Skip to main content

No project description provided

Project description

scikit-stan

Scikit-Stan is a package of Stan models wrapped in a Scikit-Learn style interface.

This package is currently under active development and should be treated as beta software.

Documentation is available at https://brianward.dev/scikit-stan/ or on ReadTheDocs (older versions and PDFs available).

Installation

Pre-compiled wheels for the package are available for MacOS, Windows, and Linux systems via pip install scikit_stan.

Source installation requires a working installation of CmdStan.

Basic usage

from scikit_stan import GLM

m = GLM(family='gamma') # Gamma family distribution with canonical inverse link
m.fit(X, y) # runs HMC-NUTS
predictions = m.predict(X) # generates new predictions from fitted model
score = m.score(X, y) # computes the R2 score of the fitted model on the data X and observations y

Attribution

This package is licensed under the BSD 3-clause license.

It is inspired by existing packages in the Stan ecosystem like rstanarm.

This package was initially developed at the Simons Foundation by Alexey Izmailov during a summer 2022 internship under the mentorship of Brian Ward.

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

scikit_stan-0.1.1.tar.gz (31.7 kB view details)

Uploaded Source

Built Distributions

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

scikit_stan-0.1.1-py3-none-win_amd64.whl (14.3 MB view details)

Uploaded Python 3Windows x86-64

scikit_stan-0.1.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.7 MB view details)

Uploaded Python 3manylinux: glibc 2.17+ x86-64

scikit_stan-0.1.1-py3-none-macosx_10_9_x86_64.whl (7.9 MB view details)

Uploaded Python 3macOS 10.9+ x86-64

File details

Details for the file scikit_stan-0.1.1.tar.gz.

File metadata

  • Download URL: scikit_stan-0.1.1.tar.gz
  • Upload date:
  • Size: 31.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for scikit_stan-0.1.1.tar.gz
Algorithm Hash digest
SHA256 6a8816e312bb89b2ae13c6b18cc9c59f2cbd85d21c8f7b7cbedee14b3a1bd685
MD5 d9eefb24ea97add9ba44c37e353a7f4d
BLAKE2b-256 884aae5c57d0695b96cf24d8a916830d985cfb5b7aae1e7dd7946ecd3955e8d4

See more details on using hashes here.

File details

Details for the file scikit_stan-0.1.1-py3-none-win_amd64.whl.

File metadata

  • Download URL: scikit_stan-0.1.1-py3-none-win_amd64.whl
  • Upload date:
  • Size: 14.3 MB
  • Tags: Python 3, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for scikit_stan-0.1.1-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 8ed0dd0babfd0ffb586d4293fef6d166152c978bd838f7e2376dd60a029fca9d
MD5 98484e2fad03019e6fb3e5951b666173
BLAKE2b-256 1ee16105041374c60e3c3e30a30e2677bf8e6d21ea06f37c74fdf0ffd52eac55

See more details on using hashes here.

File details

Details for the file scikit_stan-0.1.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_stan-0.1.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2e2d650376c71d6835e40409a0cd2e5875b7dbd45850a7c7ab7489cad37d9275
MD5 cd78c23a3db612f575fe571f42c1ad1c
BLAKE2b-256 2215e899ab2ff5aacbe39e2b938c7cd70230b02528cea9fbbd6cf764076e0a61

See more details on using hashes here.

File details

Details for the file scikit_stan-0.1.1-py3-none-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scikit_stan-0.1.1-py3-none-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ea7e66291e00acae8265b9cb1ff0bd4c2800e1e38b55a2a845bf24b9c476a14c
MD5 531ec926895012e4cb8d670f82eb3afa
BLAKE2b-256 fc6c990f6343becb34926ec5c05152cb3b752badbfca39f9fececea0e0d7e500

See more details on using hashes here.

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