{ "_id": "6a169770acfb0bcc41d7c5dd", "Type": "Package", "Package": "mglasso", "Title": "Multiscale Graphical Lasso", "Version": "0.1.3", "Authors@R": "c(\nperson(\"Edmond\", \"Sanou\", , \"doedmond.sanou@univ-evry.fr\", role = c(\"aut\", \"cre\")),\nperson(\"Tung\", \"Le\", role = \"ctb\"),\nperson(\"Christophe\", \"Ambroise\", role = \"ths\"),\nperson(\"Geneviève\", \"Robin\", role = \"ths\")\n)", "Description": "Inference of Multiscale graphical models with neighborhood\nselection approach. The method is based on solving a convex\noptimization problem combining a Lasso and fused-group Lasso\npenalties. This allows to infer simultaneously a conditional\nindependence graph and a clustering partition. The optimization\nis based on the Continuation with Nesterov smoothing in a\nShrinkage-Thresholding Algorithm solver (Hadj-Selem et al.\n2018) implemented in python.", "License": "MIT + file LICENSE", "VignetteBuilder": "knitr", "ByteCompile": "true", "Config/reticulate": "list( packages = list( list(package = \"scipy\",\nversion = \"1.7.1\"), list(package = \"numpy\", version =\n\"1.22.4\"), list(package = \"matplotlib\"), list(package =\n\"scikit-learn\"), list(package = \"six\"), list(package =\n\"pylearn-parsimony\", version = \"0.3.1\", pip = TRUE) ) )", "Encoding": "UTF-8", "RoxygenNote": "7.2.3", "URL": "https://desanou.github.io/mglasso/", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "Config/pak/sysreqs": "cmake make libpng-dev libuv1-dev libssl-dev python3\nlibx11-dev zlib1g-dev", "Repository": "https://desanou.r-universe.dev", "Date/Publication": "2023-06-19 09:21:33 UTC", "RemoteUrl": "https://github.com/desanou/mglasso", "RemoteRef": "HEAD", 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[ "cah_glasso" ] }, { "page": "conesta", "title": "CONESTA solver.", "topics": [ "conesta" ] }, { "page": "conesta_rwrapper", "title": "CONESTA solver for numerical experiments.", "topics": [ "conesta_rwrapper" ] }, { "page": "cost", "title": "cost function", "topics": [ "cost" ] }, { "page": "dist_beta", "title": "distances Beta", "topics": [ "dist_beta" ] }, { "page": "error", "title": "Mean error from classical regression", "topics": [ "error" ] }, { "page": "error_huge", "title": "Formula from Huge paper", "topics": [ "error_huge" ] }, { "page": "expand_beta", "title": "TO DO: Fill upper triangular matrix then sum up with the transpose to have full matrix", "topics": [ "expand_beta" ] }, { "page": "expand_beta_deprecated", "title": "doesn't work when dealing with matrix where diagonal of zero should be adjusted", "topics": [ "expand_beta_deprecated" ] }, { "page": "extract_meta", "title": "extracts meta-variables indices", "topics": [ "extract_meta" ] }, { "page": "fun_lines", "title": "weighted sum/difference of two regression vectors", "topics": [ "fun_lines" ] }, { "page": "get_auc", "title": "Plot ROC curve and calculate AUC", "topics": [ "get_auc" ] }, { "page": "get_range_nclusters", "title": "compute range of number of clusters from ROC outputs take in parameter an object from reorder_mglasso_roc_calculations", "topics": [ "get_range_nclusters" ] }, { "page": "ggplot_roc", "title": "Title", "topics": [ "ggplot_roc" ] }, { "page": "graph_estimate", "title": "Neighborhood selection estimate", "topics": [ "graph_estimate" ] }, { "page": "image_sparse", "title": "Plot the image of a matrix", "topics": [ "image_sparse" ] }, { "page": "install_pylearn_parsimony", "title": "Install the python library pylearn-parsimony and other required libraries", "topics": [ "install_pylearn_parsimony" ] }, { "page": "lagrangian", "title": "lagrangian function", "topics": [ "lagrangian" ] }, { "page": "lasso_estimate", "title": "Check first estimate coeffs with glm", "topics": [ "lasso_estimate" ] }, { "page": "mean_prec_mat", "title": "mean of randomly simulated precision matrices in the same configuration", "topics": [ "mean_prec_mat" ] }, { "page": "merge_beta", "title": "Merge Beta Different types of merging and their effect", "topics": [ "merge_beta" ] }, { "page": "merge_clusters", "title": "compute clusters partition from pairs of variables to merge", "topics": [ "merge_clusters" ] }, { "page": "merge_labels", "title": "Merge labels", "topics": [ "merge_labels" ] }, { "page": "merge_proc", "title": "merge clusters from table", "topics": [ "merge_proc" ] }, { "page": "mergeX", "title": "Merge X", "topics": [ "mergeX" ] }, { "page": "mglasso", "title": "Inference of Multiscale Gaussian Graphical Model.", "topics": [ "mglasso" ] }, { "page": "neighbor_select", "title": "neighbor_select", "topics": [ "neighbor_select" ] }, { "page": "one_config", "title": "One simulation configuration", "topics": [ "one_config" ] }, { "page": "perf_one", "title": "compute TPR, FPR, SHD given estimated and true precision matrices", "topics": [ "perf_one" ] }, { "page": "perf_vec", "title": "get performances from list of estimations", "topics": [ "perf_vec" ] }, { "page": "plot_clusterpath", "title": "Plot MGLasso Clusterpath", "topics": [ "plot_clusterpath" ] }, { "page": "plot_mglasso", "title": "fonction qui affiche les matrices d'adjacence à chaque niveau de la hiérarchie à automatiser utiliser niveau de legende commune", "topics": [ "plot_mglasso" ] }, { "page": "precision_to_regression", "title": "Compute precision matrix from regression vectors", "topics": [ "precision_to_regression" ] }, { "page": "repart", "title": "pareil pour les clusters ACP dimensions ?", "topics": [ "repart" ] }, { "page": "select_ebic_weighted", "title": "EBIC", "topics": [ "select_ebic_weighted" ] }, { "page": "select_kfold", "title": "K-fold cross validation neghborhood lasso selection", "topics": [ "select_kfold" ] }, { "page": "select_kfold_mglasso", 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