Package: mglasso 0.1.3

mglasso: Multiscale Graphical Lasso

Inference of Multiscale graphical models with neighborhood selection approach. The method is based on solving a convex optimization problem combining a Lasso and fused-group Lasso penalties. This allows to infer simultaneously a conditional independence graph and a clustering partition. The optimization is based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018) <doi:10.1109/TMI.2018.2829802> implemented in python.

Authors:Edmond Sanou [aut, cre], Tung Le [ctb], Christophe Ambroise [ths], Geneviève Robin [ths]

mglasso_0.1.3.tar.gz
mglasso_0.1.3.zip(r-4.5)mglasso_0.1.3.zip(r-4.4)mglasso_0.1.3.zip(r-4.3)
mglasso_0.1.3.tgz(r-4.4-any)mglasso_0.1.3.tgz(r-4.3-any)
mglasso_0.1.3.tar.gz(r-4.5-noble)mglasso_0.1.3.tar.gz(r-4.4-noble)
mglasso_0.1.3.tgz(r-4.4-emscripten)mglasso_0.1.3.tgz(r-4.3-emscripten)
mglasso.pdf |mglasso.html✨
mglasso/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/desanou/mglasso/issues

On CRAN:

24 exports 2 stars 0.84 score 70 dependencies 13 scripts 295 downloads

Last updated 1 years agofrom:0cbbafe519. Checks:OK: 1 ERROR: 6. Indexed: yes.

Exports:adj_matconestaconesta_rwrapperconfigs_simuestimate_variabilityextract_metafun_linesget_clusters_mglget_mean_ROC_statget_perf_from_rawggplot_rocinstall_pylearn_parsimonymglassomglasso_pair_paramneighbor_selectone_simu_extendedone_simu_ROCplot_clusterpathplot_mglassoplot_resreformat_roc_res_for_ggplotseq_l2_l1_fixedsim_datasymmetrize

Dependencies:askpassbootcapushecellrangerclassclicolorspacecorpcorcpp11crayoncurldata.tableDescToolse1071Exactexpmfansifarverggplot2ggrepelgldgluegridExtragtableherehmshttrisobandjsonlitelabelinglatticelifecyclelmommagrittrMASSMatrixmgcvmimemunsellmvtnormnlmeopensslpillarpkgconfigpngprettyunitsprogressproxyR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppTOMLreadxlrematchreticulaterlangrootSolverprojrootrstudioapiscalessystibbleutf8vctrsviridisLitewithr