Package: MLGL 1.0.0

Quentin Grimonprez

MLGL: Multi-Layer Group-Lasso

It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).

Authors:Quentin Grimonprez [aut, cre], Samuel Blanck [ctb], Alain Celisse [ths], Guillemette Marot [ths], Yi Yang [ctb], Hui Zou [ctb]

MLGL_1.0.0.tar.gz
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MLGL.pdf |MLGL.html✨
MLGL/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/modal-inria/mlgl/issues

On CRAN:

group-lassovariable-selection

3.61 score 3 stars 27 scripts 216 downloads 1 mentions 17 exports 104 dependencies

Last updated 2 years agofrom:8a23766cfc. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winNOTENov 04 2024
R-4.5-linuxNOTENov 04 2024
R-4.4-winNOTENov 04 2024
R-4.4-macNOTENov 04 2024
R-4.3-winOKNov 04 2024
R-4.3-macOKNov 04 2024

Exports:bootstrapHclustcomputeGroupSizeWeightcv.MLGLFtestfullProcesshierarchicalFDRhierarchicalFWERHMTlistToMatrixMLGLoverlapgglassopartialFtestselFDRselFWERsimuBlockGaussianstability.MLGLuniqueGroupHclust

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacliclustercolorspacecowplotcpp11crosstalkDerivdigestdoBydplyrDTellipseemmeansestimabilityevaluateFactoMineRfansifarverfastclusterfastmapflashClustfontawesomeFormulafsgenericsgglassoggplot2ggrepelgluegtablehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivparallelDistpbkrtestpillarpkgconfigpromisespurrrquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrmarkdownsassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
MLGLMLGL-package
Hierarchical Clustering with distance matrix computed using bootstrap replicatesbootstrapHclust
Get coefficients from a 'cv.MLGL' objectcoef.cv.MLGL
Get coefficients from a 'MLGL' objectcoef.MLGL
Compute the group size weight vector with an authorized maximal sizecomputeGroupSizeWeight
Multi-Layer Group-Lasso with cross V-fold validationcv.MLGL
F-testFtest
Full process of MLGLfullProcess fullProcess.default fullProcess.formula
Hierarchical testing with FDR controlhierarchicalFDR
Hierarchical testing with FWER controlhierarchicalFWER
Hierarchical Multiple Testing procedureHMT
Obtain a sparse matrix of the coefficients of the pathlistToMatrix
Multi-Layer Group-LassoMLGL MLGL.default MLGL.formula
Group-lasso with overlapping groupsoverlapgglasso
Partial F-testpartialFtest
Plot the cross-validation obtained from 'cv.MLGL' functionplot.cv.MLGL
Plot the path obtained from 'fullProcess' functionplot.fullProcess
Plot the path obtained from 'HMT' functionplot.HMT
Plot the path obtained from 'MLGL' functionplot.MLGL
Plot the stability path obtained from 'stability.MLGL' functionplot.stability.MLGL
Predict fitted values from a 'cv.MLGL' objectpredict.cv.MLGL
Predict fitted values from a 'MLGL' objectpredict.MLGL
Print Valuesprint.fullProcess
Print Valuesprint.HMT
Print Valuesprint.MLGL
Selection from hierarchical testing with FDR controlselFDR
Selection from hierarchical testing with FWER controlselFWER
Simulate multivariate Gaussian samples with block diagonal variance matrixsimuBlockGaussian
Stability Selection for Multi-Layer Group-lassostability.MLGL
Object Summariessummary.fullProcess
Object Summariessummary.HMT
Object Summariessummary.MLGL
Find all unique groups in 'hclust' resultsuniqueGroupHclust