| Name | Version | Summary | date |
| map2loop |
3.2.5 |
Generate 3D model data from 2D maps. |
2025-08-17 23:27:22 |
| timely-beliefs |
3.2.2 |
Data modelled as beliefs (at a certain time) about events (at a certain time). |
2025-08-15 11:10:57 |
| loopstructuralvisualisation |
0.1.17 |
3D geological modelling |
2025-08-14 06:44:23 |
| dstz |
0.0.5 |
一个轻量、易用的Python证据理论库，专注于Dempster-Shafer理论及其扩展。 |
2025-08-08 10:46:53 |
| LoopStructural |
1.6.18 |
3D geological modelling |
2025-08-04 04:20:06 |
| iemm |
0.1.0 |
Iterative Evidential Mistakeness Minimization - Explainable Evidential Clustering |
2025-08-01 19:39:22 |
| torch-uncertainty |
0.7.0.post1 |
Uncertainty quantification library in PyTorch |
2025-07-31 11:30:18 |
| bootplot |
0.0.14 |
bootplot is a package for black-box uncertainty visualization. |
2025-07-30 22:28:47 |
| undate |
0.5.1 |
library for working with uncertain, fuzzy, or partially unknown dates and date intervals |
2025-07-22 17:51:29 |
| probabilistic-quantum-reasoner |
1.0.0 |
A quantum-classical hybrid reasoning engine for uncertainty-aware AI inference |
2025-07-16 04:22:22 |
| gempy |
2025.2.0 |
An Open-source, Python-based 3-D structural geological modeling software. |
2025-07-14 16:22:05 |
| unpaac |
0.1.4 |
Pandas DataFrame and Series accessors to handle quantities with uncertainties. |
2025-03-03 00:13:12 |
| posteriors |
0.0.6 |
Uncertainty quantification with PyTorch |
2025-02-21 11:48:47 |
| spotpy |
1.6.6 |
A Statistical Parameter Optimization Tool. |
2025-02-18 09:55:48 |
| data-suite |
0.2.1 |
Data-centric identification of in-distribution incongruous examples |
2025-02-03 10:14:03 |
| sigfig |
1.3.18 |
Python library for rounding numbers (with expected results) |
2024-11-01 04:05:36 |
| loopsolver |
0.1.0 |
solvers used by loopstructural and other loop libraries |
2024-07-03 06:26:41 |
| uspekpy |
1.0.2 |
Radiation protection quantities for x-rays with uncertainties using SpekPy and Monte Carlo techniques |
2024-06-20 16:34:53 |
| thetis |
0.2.1 |
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects. |
2024-06-11 14:26:21 |
| thetiscore |
0.2.1 |
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects. |
2024-06-11 14:25:49 |