pSeven Core is an integrated toolkit for predictive modeling, data analysis and optimization. It provides a variety of proprietary and classical algorithms for local and global optimization, approximation, dimension reduction, design of experiments, and sensitivity analysis. See the homepage and documentation for full details.

Requirements

  • Python 3.6 or newer.
    • pSeven Core also maintains compatibility with the final Python 2.7 version.
  • NumPy 1.16 or newer.
    • pSeven Core v2024.06 and older versions are not compatible with NumPy 2. pSeven Core v2024.07 and newer are up to date with NumPy 2.

Additionally recommended:

While the above are not required, they are widely used in pSeven Core examples and guides.

Optional:

  • SHAP - implements a game theoretic approach to explain model output.

SHAP is required only by some pSeven Core approximation models and only if you are going to use the SHAP evaluation feature for that certain kind of models.

Windows requirements

pSeven Core is tested on Windows 10, 64-bit desktop editions. Newer versions and corresponding server editions are also supported but not regularly tested.

Linux requirements

pSeven Core works on any Linux x86_64 with:

  • Linux kernel 2.6.18 or newer.
  • GNU C Library (glibc) 2.5 or newer.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2026.9.23)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for dmitry_vetrov from gravatar.com dmitry_vetrov Avatar for pseven from gravatar.com pseven

Credits

Author/Maintainer: pSeven SAS

License expression

LicenseRef-pSeven-Proprietary
View SPDX License List

Requires

Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, >=2.7

Tags

adaptive design adaptive doe approximation approximation model approximation modeling approximator box-behnken correlation analysis design exploration design of experiments design optimization design space difference approximation dimension reduction doe engineering engineering optimization feature importance feature scoring feature selection fmi fmu fractional factorial gaussian processes gradient optimization kendall latin hypercube sampling low discrepancy sequence mixed integer mixture of approximators modeling multiple fidelity mutual information optimization optimizer orthogonal array parametric study pearson predictive modeling principal component analysis regression regression tree response surface robust optimization screening index screening indices sensitivity analysis shap shapley additive explanations sobol index sobol indices spearman surrogate based optimization taguchi index taguchi indices tensor approximation variable fidelity

Classifiers

Report project as malware

Metadata

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2026.9.23)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for dmitry_vetrov from gravatar.com dmitry_vetrov Avatar for pseven from gravatar.com pseven

Credits

Author/Maintainer: pSeven SAS

License expression

LicenseRef-pSeven-Proprietary
View SPDX License List

Requires

Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, >=2.7

Tags

adaptive design adaptive doe approximation approximation model approximation modeling approximator box-behnken correlation analysis design exploration design of experiments design optimization design space difference approximation dimension reduction doe engineering engineering optimization feature importance feature scoring feature selection fmi fmu fractional factorial gaussian processes gradient optimization kendall latin hypercube sampling low discrepancy sequence mixed integer mixture of approximators modeling multiple fidelity mutual information optimization optimizer orthogonal array parametric study pearson predictive modeling principal component analysis regression regression tree response surface robust optimization screening index screening indices sensitivity analysis shap shapley additive explanations sobol index sobol indices spearman surrogate based optimization taguchi index taguchi indices tensor approximation variable fidelity

Classifiers

Report project as malware

Release files for psevencore 2026.7.23

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

Built distributions (wheels)

Table of built distributions (wheels) for psevencore 2026.7.23
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psevencore-2026.7.23-py2.py3-none-win_amd64.whl 27.0 MB Python 2, Python 3 none Windows x86-64 Details
psevencore-2026.7.23-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl 54.9 MB Python 3, Python 2 none Linux glibc 2.17+ x86-64 Details

Total release size: 81.9 MB

Release files / psevencore-2026.7.23-py2.py3-none-win_amd64.whl

Download URL psevencore-2026.7.23-py2.py3-none-win_amd64.whl
Size 27.0 MB
Tags Python 2 Python 3 Windows x86-64
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Release files / psevencore-2026.7.23-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl

Download URL psevencore-2026.7.23-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Size 54.9 MB
Tags Linux glibc 2.17+ x86-64 Python 2 Python 3
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