| Name | Version | Summary | date |
| pSevenCore |
2024.4.26 |
pSeven Core is an integrated toolkit for design space exploration, optimization, and predictive modeling. |
2024-04-26 09:21:11 |
| sklearn-ml |
2.2 |
A light package build on scikit-learn, which is used for machine learning missions.By using it, you could conveniently train and use models at the same time, and do some model comparison. |
2024-04-25 14:40:23 |
| memento-scorecard |
2.0.3 |
Scorecard development with Python |
2024-04-23 07:53:24 |
| skpro |
2.2.2 |
A unified framework for probability distributions and probabilistic supervised regression |
2024-04-20 19:36:09 |
| green |
4.0.2 |
Green is a clean, colorful, fast python test runner. |
2024-04-18 23:54:19 |
| linearmodels |
6.0 |
Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python |
2024-04-16 17:42:06 |
| feature-engineering |
2.1.4 |
Unleash the Power of Your Data with Feature Engineering: The Ultimate Python Library for Machine Learning Preprocessing and Enhancement |
2024-04-09 02:00:21 |
| pymer4 |
0.8.2 |
pymer4: all the convenience of lme4 in python |
2024-04-07 02:24:15 |
| deforce |
1.0.0 |
deforce: Derivative-Free Algorithms for Optimizing Cascade Forward Neural Networks |
2024-04-06 15:43:35 |
| stat-analysis |
1.0.0 |
A Python library providing hands on implementation of a collection of common statistical methods for data analysis. |
2024-04-04 19:33:43 |
| impactchart |
0.5.1 |
A package for generating impact charts. |
2024-04-03 17:30:27 |
| GANDLF |
0.0.19 |
PyTorch-based framework that handles segmentation/regression/classification using various DL architectures for medical imaging. |
2024-03-28 00:37:18 |
| intelelm |
1.1.1 |
IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine |
2024-03-27 11:51:12 |
| rsklpr |
0.7.0 |
Implementation of the Robust Local Polynomial Regression with Similarity Kernel draft paper |
2024-03-25 02:26:00 |
| thetis |
0.2.0 |
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects. |
2024-03-22 08:37:22 |
| thetiscore |
0.2.0 |
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects. |
2024-03-22 08:36:57 |
| GeneticEngine |
0.8.4 |
Genetic Programming with Types and Grammars |
2024-01-31 22:57:14 |
| spreg |
1.4.2 |
PySAL Spatial Econometric Regression in Python |
2023-11-07 04:14:02 |
| datafold |
2.0.0 |
Operator-theoretic models to identify dynamical systems and parametrize point cloud geometry |
2023-07-31 21:06:17 |