{"id":19839838,"url":"https://github.com/qdata/simule","last_synced_at":"2025-05-01T19:30:29.365Z","repository":{"id":56937198,"uuid":"50793322","full_name":"QData/SIMULE","owner":"QData","description":"Machine Learning 2017 / \"A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models\", / https://cran.r-project.org/web/packages/simule/","archived":false,"fork":false,"pushed_at":"2019-08-28T16:27:42.000Z","size":13094,"stargazers_count":4,"open_issues_count":1,"forks_count":1,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-04-06T17:05:52.752Z","etag":null,"topics":["covariance-matrices","data-matrices","gaussian-graphical-models","graphical-models"],"latest_commit_sha":null,"homepage":"https://cran.r-project.org/web/packages/simule/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/QData.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}},"created_at":"2016-01-31T20:23:25.000Z","updated_at":"2022-06-08T09:20:06.000Z","dependencies_parsed_at":"2022-08-21T06:50:08.286Z","dependency_job_id":null,"html_url":"https://github.com/QData/SIMULE","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FSIMULE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FSIMULE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FSIMULE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QData%2FSIMULE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/QData","download_url":"https://codeload.github.com/QData/SIMULE/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251932522,"owners_count":21667158,"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":["covariance-matrices","data-matrices","gaussian-graphical-models","graphical-models"],"created_at":"2024-11-12T12:24:33.168Z","updated_at":"2025-05-01T19:30:25.142Z","avatar_url":"https://github.com/QData.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# SIMULE\nThis is an R implementation of the [SIMULE](https://arxiv.org/abs/1605.03468) algorithm proposed in the following paper:\n\n\"A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models\",\naccepted by Machine Learning @ [URL](https://link.springer.com/article/10.1007/s10994-017-5635-7)\n\nPlease run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper @ [URL](http://link.springer.com/article/10.1007/s10994-017-5635-7) or read the R-package Manual: @ [URL](https://cran.r-project.org/web/packages/simule/simule.pdf)\n\n+ CRAN R Library page: [URL](https://cran.r-project.org/web/packages/simule/)\n\n+ Presentation @ [URL](https://github.com/QData/SIMULE/blob/master/SIMULE-talk.pdf)\n\n## Dependency\nIt depends on the following existing packages. To use them, simply\n```r\nlibrary('pcaPP')\nlibrary('lpSolve')\nlibrary('parallel')\n```\nIf you don't have these packages, simply use\n```r\ninstall.packages('packageNameFromAbove')\n```\n\n## Usage\n\n0. install the R \"simule\" package through R console:\n```r\ninstall.packages('simule')\n```\n\n1. then load the library simule in R console, by running:\n```r\nlibrary(simule)\n```\n\n2. Then, simply run the function  ```simule``` on your favorite datasets\nFor example,\n```r\nsimule(CovarianceMatrixList, lambda = 0.05, epsilon = 0.5, parallel = TRUE)\n```\n\nThis function will returns a ```list``` (a data structure in R) of graphs estimated by the SIMULE package.\n\n## Three possible types of inputs for the Argument ``` CovarianceMatrixList ```\n\n1. The argument ``` CovarianceMatrixList ``` can represent a ```list``` of data matrices directly:\nThe i-th item ``` CovarianceMatrixList[[i]]``` represents the i-th matrix  in a ```list``` of data matrices ```CovarianceMatrixList```.\n** Please make sure the order of the feature variables are the same among all the data matrices in ```CovarianceMatrixList```.**\n\n\n2. If the input ``` CovarianceMatrixList ``` is Symmetric, the package automatically assumes that the data inputs belong to the following two types:\n\n- The argument ``` CovarianceMatrixList ``` can a ```list``` of covariance matrices.\nAssuming ``` X ``` represents a list of data matrices, whose i-th item ``` X[[i]]``` represents the data matrix of the i-th task.\n\nWe can use the following function to calculate the covariance matrices:\n```r\nCovarianceMatrixList[[i]] = cov(X[[i]])\n```\n\n- The argument ``` CovarianceMatrixList ``` can represent a ```list``` of kendall's tau correlation matrices.\nThe kendall's tau correlation matrices can be calculated by using the following command:\n```r\ncor.fk(X[[i]])  \n```\n(by the ``` 'pcaPP' ``` package.)\n\nThe kendall's tau correlation matrices can also be calculated through the following R functions:\n```r\ncor(X[[i]], method = 'kendall')\n```\nHowever the above way of calculating kendall's tau correlation matrix is very slow in R.\n\n\n## Other Arguments\n\n- ``` lambda ```\n\nThe parameter for the sparsity level of the estimated graphs. The larger ```lambda``` you choose, the ***sparser*** graphs you will estimate from the inputs.\n\n- ``` epsilon ```\n\nThe parameter reflects the differences of sparsity level between the shared subgraph versus the context-specific subgraphs. The larger ```epsilon``` you choose, the ***denser*** the shared subgraph is (while the context-specific subgraphs are ***sparser***) and vice versa.\n\n- ``` covType  ```\nThis parameter controls SIMULE estimates the sparse Gaussian Graphical models (sGGM) or the sparse Nonparanormal Graphical Models from the input data.  This parameter  matters only when the input argument ``` CovarianceMatrixList ```  represents a ```list``` of data matrices directly:\nWhen ``` covType = \"cov\" ``` the package estimates sGGMs from the input.\nWhen ``` covType == \"kendall\"```, the package estimates sNGMs from the input.\n\n- ``` parallel ```\n\nLogic parameter for parallel implementation or not. If you have a multi-core machine, let ```parallel = TRUE```.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqdata%2Fsimule","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqdata%2Fsimule","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqdata%2Fsimule/lists"}