JOSS: https://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS
bioinformatics chemometrics data-fusion data-integration data-science machine-learning metabolomics multivariate-analysis multivariate-statistics pattern-recognition subspace-learning supervised-learning
Score: 10.976525590084808
Last synced: about 7 hours ago
JSON representation
Repository metadata:
(Multiblock) Partial Least Squares Regression for Python
- Host: GitHub
- URL: https://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS
- Owner: DTUComputeStatisticsAndDataAnalysis
- License: bsd-3-clause
- Created: 2018-01-08T09:55:21.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2020-01-14T21:19:54.000Z (over 6 years ago)
- Last Synced: 2026-03-01T17:37:05.024Z (4 months ago)
- Topics: bioinformatics, chemometrics, data-fusion, data-integration, data-science, machine-learning, metabolomics, multivariate-analysis, multivariate-statistics, pattern-recognition, subspace-learning, supervised-learning
- Language: Python
- Homepage: https://mbpls.readthedocs.io
- Size: 16.6 MB
- Stars: 33
- Watchers: 1
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.rst
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: .github/CODE_OF_CONDUCT.md
Owner metadata:
- Name: DTU Compute Statistics and Data Analysis
- Login: DTUComputeStatisticsAndDataAnalysis
- Email:
- Kind: organization
- Description:
- Website: https://www.compute.dtu.dk/english/research/research-sections/stat
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/44840859?v=4
- Repositories: 5
- Last Synced at: 2023-03-04T13:28:18.957Z
- Profile URL: https://github.com/DTUComputeStatisticsAndDataAnalysis
GitHub Events
Total
- Watch event: 2
- Total: 2
Last Year
- Watch event: 1
- Total: 1
Committers metadata
Last synced: 4 months ago
Total Commits: 146
Total Committers: 3
Avg Commits per committer: 48.667
Development Distribution Score (DDS): 0.39
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| Laurent Vermue | l****e | 89 |
| Andreas Baum | a****a@d****k | 49 |
| Laurent Vermue | 8 |
Issue and Pull Request metadata
Last synced: 10 months ago
Total issues: 9
Total pull requests: 3
Average time to close issues: 10 days
Average time to close pull requests: 2 days
Total issue authors: 4
Total pull request authors: 2
Average comments per issue: 0.67
Average comments per pull request: 0.0
Merged pull request: 3
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- arokem (6)
- sten2lu (1)
- lcwheeler (1)
- cwieder (1)
Top Pull Request Authors
- lvermue (2)
- b0nsaii (1)
Top Issue Labels
- accepted (1)
Top Pull Request Labels
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 551 last-month
- Total dependent packages: 2
- Total dependent repositories: 2
- Total versions: 15
- Total maintainers: 2
pypi.org: mbpls
An implementation of the most common partial least squares algorithms as multi-block methods
- Homepage: https://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS
- Documentation: https://mbpls.readthedocs.io/
- Licenses: new BSD
- Latest release: 1.0.4 (published over 6 years ago)
- Last Synced: 2026-02-21T12:03:46.584Z (4 months ago)
- Versions: 15
- Dependent Packages: 2
- Dependent Repositories: 2
- Downloads: 551 Last month
-
Rankings:
- Dependent packages count: 3.168%
- Average: 11.405%
- Dependent repos count: 11.524%
- Stargazers count: 12.489%
- Forks count: 12.55%
- Downloads: 17.292%
- Maintainers (2)
Dependencies
- numpy >=1.13.3
- pandas >=0.20.0
- scikit-learn >=0.20.0
- scipy >=1.0.0
- numpy >=
- pandas >=
- scikit-learn >=
- scipy >=