{"id":23735381,"url":"https://github.com/murraylab/gemmr","last_synced_at":"2025-09-04T10:31:00.291Z","repository":{"id":57433424,"uuid":"282479318","full_name":"murraylab/gemmr","owner":"murraylab","description":"Generative Modeling of Multivariate Relationships","archived":false,"fork":false,"pushed_at":"2023-12-04T01:52:44.000Z","size":13709,"stargazers_count":20,"open_issues_count":0,"forks_count":3,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-04-27T05:21:13.078Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/murraylab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-07-25T16:12:25.000Z","updated_at":"2024-03-25T22:17:30.000Z","dependencies_parsed_at":"2023-12-04T01:34:55.335Z","dependency_job_id":null,"html_url":"https://github.com/murraylab/gemmr","commit_stats":{"total_commits":21,"total_committers":1,"mean_commits":21.0,"dds":0.0,"last_synced_commit":"672c2fbb8ff8f5b08afb16fe9b790536c69f2cf3"},"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/murraylab%2Fgemmr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/murraylab%2Fgemmr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/murraylab%2Fgemmr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/murraylab%2Fgemmr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/murraylab","download_url":"https://codeload.github.com/murraylab/gemmr/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":231949233,"owners_count":18450463,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-31T06:15:00.985Z","updated_at":"2024-12-31T06:15:01.559Z","avatar_url":"https://github.com/murraylab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Build Status](https://travis-ci.com/murraylab/gemmr.svg?branch=master)](https://travis-ci.com/murraylab/gemmr)\n[![codecov](https://codecov.io/gh/murraylab/gemmr/branch/master/graph/badge.svg)](https://codecov.io/gh/murraylab/gemmr)\n[![Documentation Status](https://readthedocs.org/projects/gemmr/badge/?version=latest)](https://gemmr.readthedocs.io/en/latest/?badge=latest)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gemmr)\n![PyPI](https://img.shields.io/pypi/v/gemmr)\n[![DOI](https://zenodo.org/badge/282479318.svg)](https://zenodo.org/doi/10.5281/zenodo.10253488)\n\ngemmr - Generative Modeling of Multivariate Relationships\n=========================================================\n\n*gemmr* calculates required sample sizes for Canonical Correlation Analysis (CCA) and\nPartial Least Squares (PLS). In addition, it can generate synthetic datasets for use \nwith CCA and PLS, and provides functionality to run and examine CCA and PLS analyses.\nIt also provides a Python wrapper for *PMA*, a sparse CCA implementation.\n\nHardware requirements\n---------------------\n\nGEMMR runs on standard hardware. To thoroughly sweep through parameters of the generative model a high-performance-computing (HPC) environment is recommended.\n\nDependencies\n------------\n\n  * numpy\n  * scipy\n  * pandas\n  * xarray\n  * netcdf4\n  * scikit-learn\n  * statsmodels\n  * joblib\n  * tqdm\n\nSome functions have additional dependencies that need to be installed separately if they are used:\n  * holoviews\n  * rpy2\n      \nThe 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\n```\n\u003e\u003e\u003e conda env create -f environment.yml\n```\n      \nInstallation\n------------\n\nThe easiest way to install *gemmr* is with `pip`:\n```\npip install gemmr\n```\n \nAlternatively, to install and use the most current code:\n```\ngit clone https://github.com/murraylab/gemmr.git\ncd gemmr\npython setup.py install\n```\n\nInstallation of *gemmr* itself (without potentially required dependencies) should take only a few seconds.\n\nDocumentation\n-------------\n \nExtensive documentation can be found [here](https://gemmr.readthedocs.io/en/latest/).\n\nThe documentation contains\n   * Demonstration of the *gemmr*'s functionality, including exptected outputs (all of which should execute quickly)\n   * Juyter notebooks detailing generation of the figures for the accompanying manuscripts\n   * API reference\n\nTo generate the documentation from source, install *gemmr* as described above and make sure you also have the following dependencies installed:\n   * ipython\n   * matplotlib\n   * sphinx\n   * nbsphinx\n   * sphinx_rtd_theme\nand run (in the `doc` subfolder):\n```\nmake html\n```\nand open `doc/_build/html/index.html`  .\n\nCitation\n--------\nIf you're using *gemmr* in a publication, please cite [Helmer et al. (2020)](https://www.biorxiv.org/content/10.1101/2020.08.25.265546v1)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmurraylab%2Fgemmr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmurraylab%2Fgemmr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmurraylab%2Fgemmr/lists"}