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
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mglasso.pdf |mglasso.html
mglasso/json (API)
NEWS

# Install 'mglasso' in R:
install.packages('mglasso', repos = c('https://desanou.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

On CRAN:

4.11 score 2 stars 13 scripts 232 downloads 24 exports 79 dependencies

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

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winERRORNov 10 2024
R-4.5-linuxERRORNov 10 2024
R-4.4-winERRORNov 10 2024
R-4.4-macERRORNov 10 2024
R-4.3-winERRORNov 10 2024
R-4.3-macERRORNov 10 2024

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:askpassbitbit64bootcapushecellrangerclassclicliprcolorspacecorpcorcpp11crayoncurldata.tableDescToolse1071Exactexpmfansifarverforcatsggplot2ggrepelgldgluegridExtragtablehavenherehmshttrisobandjsonlitelabelinglatticelifecyclelmommagrittrMASSMatrixmgcvmimemunsellmvtnormnlmeopensslpillarpkgconfigpngprettyunitsprogressproxyR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppTOMLreadrreadxlrematchreticulaterlangrootSolverprojrootrstudioapiscalessystibbletidyselecttzdbutf8vctrsviridisLitevroomwithr

Multiscale GLasso

Rendered frommglasso.Rmdusingknitr::knitron Nov 10 2024.

Last update: 2022-09-06
Started: 2022-01-28

Readme and manuals

Help Manual

Help pageTopics
Adjacency matrixadj_mat
Init Beta 1 matrixbeta_idty
Init Beta via OLSbeta_ols
vectorize beta matrixbeta_to_vector
return precision matrixbloc_diag
cah_glassocah_glasso
CONESTA solver.conesta
CONESTA solver for numerical experiments.conesta_rwrapper
cost functioncost
distances Betadist_beta
Mean error from classical regressionerror
Formula from Huge papererror_huge
TO DO: Fill upper triangular matrix then sum up with the transpose to have full matrixexpand_beta
doesn't work when dealing with matrix where diagonal of zero should be adjustedexpand_beta_deprecated
extracts meta-variables indicesextract_meta
weighted sum/difference of two regression vectorsfun_lines
Plot ROC curve and calculate AUCget_auc
compute range of number of clusters from ROC outputs take in parameter an object from reorder_mglasso_roc_calculationsget_range_nclusters
Titleggplot_roc
Neighborhood selection estimategraph_estimate
Plot the image of a matriximage_sparse
Install the python library pylearn-parsimony and other required librariesinstall_pylearn_parsimony
lagrangian functionlagrangian
Check first estimate coeffs with glmlasso_estimate
mean of randomly simulated precision matrices in the same configurationmean_prec_mat
Merge Beta Different types of merging and their effectmerge_beta
compute clusters partition from pairs of variables to mergemerge_clusters
Merge labelsmerge_labels
merge clusters from tablemerge_proc
Merge XmergeX
Inference of Multiscale Gaussian Graphical Model.mglasso
neighbor_selectneighbor_select
One simulation configurationone_config
compute TPR, FPR, SHD given estimated and true precision matricesperf_one
get performances from list of estimationsperf_vec
Plot MGLasso Clusterpathplot_clusterpath
fonction qui affiche les matrices d'adjacence à chaque niveau de la hiérarchie à automatiser utiliser niveau de legende communeplot_mglasso
Compute precision matrix from regression vectorsprecision_to_regression
pareil pour les clusters ACP dimensions ?repart
EBICselect_ebic_weighted
K-fold cross validation neghborhood lasso selectionselect_kfold
K-fold cross validation mglassoselect_kfold_mglasso
Finds the optimal number of clusters using slope heuristicselect_partition
stability selection mglassoselect_stab_mglasso
stability selection mglasso II stars wayselect_stars_mglasso
def sequences for lambda1s and lambda2s not sure if max of lambda1 s still the same as in the lasso case. But if find better equivalence will update this partseq_l1l2
simulate data with given graph structuresim_data
symmetrize matrix of regression vectors pxpsymmetrize