{"id":14068099,"url":"https://github.com/dirmeier/netReg","last_synced_at":"2025-07-30T02:33:03.135Z","repository":{"id":50152060,"uuid":"63184481","full_name":"dirmeier/netReg","owner":"dirmeier","description":" :bar_chart: Generalized linear regression models with network-regularization in R.","archived":false,"fork":false,"pushed_at":"2024-07-09T15:45:42.000Z","size":40338,"stargazers_count":14,"open_issues_count":3,"forks_count":3,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-12-02T18:12:00.271Z","etag":null,"topics":["graphs","linear-regression","network-regularization","networks","r","regression","regularization","tensorflow"],"latest_commit_sha":null,"homepage":"https://dirmeier.github.io/netReg/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dirmeier.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-07-12T18:52:42.000Z","updated_at":"2024-09-16T02:16:20.000Z","dependencies_parsed_at":"2024-10-28T10:28:51.183Z","dependency_job_id":"9bd81e73-524d-4e05-a2c7-3ba1f215c397","html_url":"https://github.com/dirmeier/netReg","commit_stats":null,"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FnetReg","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FnetReg/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FnetReg/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FnetReg/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dirmeier","download_url":"https://codeload.github.com/dirmeier/netReg/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":228075732,"owners_count":17865537,"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":["graphs","linear-regression","network-regularization","networks","r","regression","regularization","tensorflow"],"created_at":"2024-08-13T07:05:56.765Z","updated_at":"2024-12-04T08:31:36.546Z","avatar_url":"https://github.com/dirmeier.png","language":"R","readme":"# netReg \u003cimg src=\"https://rawgit.com/dirmeier/netReg/master/inst/sticker/sticker.png\" align=\"right\" width=\"160px\"/\u003e\n\n[![Project Status](http://www.repostatus.org/badges/latest/unsupported.svg)](http://www.repostatus.org/#unsupported)\n\nGeneralized linear regression models with network-regularization in `R`. Now with `TensorFlow`.\n\n## About\n\n\u003e [!NOTE]\n\u003e ⚠️ The package is no longer actively maintained or developed.\n\nModelling dependencies using linear regression models is often complicated when the \nanalysed data-sets are high-dimensional and less observations than variables \nare available (n \u003c\u003c p). `netReg` implements generalized linear models \nthat utilize network penalties for regularization. Network regularization uses graphs\nor trees to incorporate information about interactions of covariables, \nor responses, into the loss function of a GLM. Ideally this allows better (i.e., lower variance)\nestimation of regression coefficients. \n\nFor instance, in `R`, you could fit a network-regularized model like that:\n\n```r\n\u003e library(netReg)\n\n\u003e X \u003c- matrix(rnorm(100 * 10), 100)\n\u003e Y \u003c- matrix(rnorm(100 * 10), 100)\n\n\u003e G.X \u003c- abs(rWishart(1, 10, diag(10))[,,1])\n\u003e G.Y \u003c- abs(rWishart(1, 10, diag(10))[,,1])\n\n\u003e fit \u003c- edgenet(X, Y, G.X, G.Y)\n\n\u003e summary(fit)\n\n#\u003ecall:\n#\u003eedgenet(X = X, Y = Y, G.X = G.X, G.Y = G.Y)\n\n#\u003eparameters:\n#\u003elambda  psigx  psigy \n#\u003e     1      1      1 \n\n#\u003efamily:  gaussian\n#\u003elink:  identity \n\n#\u003e-\u003e call coef(x) for coefficients\n```\n\nFrom version `v1.9.0` on, we use `TensorFlow`, instead of custom `C++` and `Dlib`, for\nestimation of regression coefficients replacing a custom *cyclic coordinate descent*. This allowed deleting of major parts of the code base.\n`netReg` still uses some `RcppArmadillo` for some matrix algebra.\n\nIn order to estimate the optimal hyperparameters, i.e., the regularization parameters\nof the network models, we use Powell's BOBYQA algorithm in a standard cross-validation framework.\n\nFor more details, please check out the respective vignettes of the single models.\n\n## Installation\n\nBefore installing the package, make sure to have these Python dependencies installed:\n\n* `tensorflow\u003e=2.2.0`,\n* `tensorflow-probabiltiy\u003e=0.10.0`\n\nThe easiest way is probably to install `TensorFlow` from `R`\n\n```{r}\ninstall.packages(c(\"tensorflow\", \"tfprobability\"))\ntensorflow::install_tensorflow(extra_packages = \"tensorflow-probability\")\n```\n\nThen install `netReg` by downloading the latest [release](https://github.com/dirmeier/netReg/releases), for instance\n\n```{r}\nremotes::install_github(\"dirmeier/netReg@v1.12.0\")\n```\n\n## Documentation\n\n* Load the package using `library(netReg)`. We provide vignettes for the package that can be called using: `vignette(package=\"netReg\")`. \n* You can also use the online [vignette](https://dirmeier.github.io/netReg).\n\n## Citation\n\nIf `netReg` was useful for you or your work, it would be great if you cited it like this:\n\n```\n@article{,\n  title={netReg: network-regularized linear models for biological association studies},\n  author={Dirmeier, Simon and Fuchs, Christiane and Mueller, Nikola S and Theis, Fabian J},\n  journal={Bioinformatics},\n  volume={34},\n  number={5},\n  pages={896--898},\n  year={2017},\n  publisher={Oxford University Press}\n}\n\n```\n\n## Author\n\nSimon Dirmeier \u003ca href=\"mailto:sfyrbnd @ pm me\"\u003esfyrbnd @ pm me\u003c/a\u003e\n","funding_links":[],"categories":["R"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdirmeier%2FnetReg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdirmeier%2FnetReg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdirmeier%2FnetReg/lists"}