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gemmr

Generative Modeling of Multivariate Relationships

https://github.com/murraylab/gemmr

Science Score: 46.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: biorxiv.org, zenodo.org
  • ✓
    Committers with academic emails
    1 of 1 committers (100.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (18.9%) to scientific vocabulary
Last synced: 7 months ago · JSON representation

Repository

Generative Modeling of Multivariate Relationships

Basic Info
  • Host: GitHub
  • Owner: murraylab
  • License: gpl-3.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 13.1 MB
Statistics
  • Stars: 25
  • Watchers: 2
  • Forks: 3
  • Open Issues: 0
  • Releases: 1
Created over 5 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

Build Status codecov Documentation Status PyPI - Python Version PyPI DOI

gemmr - Generative Modeling of Multivariate Relationships

gemmr calculates required sample sizes for Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS). In addition, it can generate synthetic datasets for use with CCA and PLS, and provides functionality to run and examine CCA and PLS analyses. It also provides a Python wrapper for PMA, a sparse CCA implementation.

Hardware requirements

GEMMR runs on standard hardware. To thoroughly sweep through parameters of the generative model a high-performance-computing (HPC) environment is recommended.

Dependencies

  • numpy
  • scipy
  • pandas
  • xarray
  • netcdf4
  • scikit-learn
  • statsmodels
  • joblib
  • tqdm

Some functions have additional dependencies that need to be installed separately if they are used: * holoviews * rpy2

The repository also contains an environment.yml file specifying a conda-environment with specific versions of all dependencies. We have tested the code with this environment. To instantiate the environment run ```

conda env create -f environment.yml ```

Installation

The easiest way to install gemmr is with pip: pip install gemmr

Alternatively, to install and use the most current code: git clone https://github.com/murraylab/gemmr.git cd gemmr python setup.py install

Installation of gemmr itself (without potentially required dependencies) should take only a few seconds.

Documentation

Extensive documentation can be found here.

The documentation contains * Demonstration of the gemmr's functionality, including exptected outputs (all of which should execute quickly) * Juyter notebooks detailing generation of the figures for the accompanying manuscripts * API reference

To generate the documentation from source, install gemmr as described above and make sure you also have the following dependencies installed: * ipython * matplotlib * sphinx * nbsphinx * sphinxrtdtheme and run (in the doc subfolder): make html and open doc/_build/html/index.html .

Citation

If you're using gemmr in a publication, please cite Helmer et al. (2020)

Owner

  • Name: murraylab
  • Login: murraylab
  • Kind: organization

GitHub Events

Total
Last Year

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 21
  • Total Committers: 1
  • Avg Commits per committer: 21.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
markus m****r@y****u 21
Committer Domains (Top 20 + Academic)
yale.edu: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 1
  • Total pull requests: 1
  • Average time to close issues: 26 minutes
  • Average time to close pull requests: 24 days
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 1.0
  • Average comments per pull request: 1.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • pauldhami (1)
Pull Request Authors
  • MIZwally (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 24 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 4
  • Total maintainers: 1
pypi.org: gemmr

Generative Modeling of Multivariate Relationships

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 24 Last month
Rankings
Dependent packages count: 10.0%
Dependent repos count: 11.6%
Stargazers count: 13.9%
Average: 17.4%
Forks count: 19.1%
Downloads: 32.4%
Maintainers (1)
mdhelmer
Last synced: 8 months ago

Dependencies

doc/requirements.txt pypi
  • gemmr *
  • holoviews *
  • ipython *
  • matplotlib *
  • nbsphinx *
  • sphinx *
  • sphinx_rtd_theme *
requirements.txt pypi
  • joblib >=0.15.1
  • netcdf4 >=1.5.3
  • numpy >=1.18.1
  • pandas >=0.25.3
  • scikit-learn >=0.22.1
  • scipy >=1.3.2
  • statsmodels >=0.10.1
  • tqdm >=4.41.1
  • xarray ==0.15.1
setup.py pypi
  • joblib *
  • netcdf4 *
  • numpy >=1.20.1
  • pandas *
  • scikit-learn *
  • scipy *
  • statsmodels *
  • tqdm *
  • xarray ==0.18.0
pyproject.toml pypi
  • joblib *
  • netcdf4 *
  • numpy *
  • pandas *
  • scikit-learn *
  • scipy *
  • statsmodels *
  • tqdm *
  • xarray *
environment.yml pypi