Package: SelectBoost 2.2.2

SelectBoost: A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

Authors:Frederic Bertrand [cre, aut], Myriam Maumy-Bertrand [aut], Ismail Aouadi [ctb], Nicolas Jung [ctb]

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SelectBoost.pdf |SelectBoost.html✨
SelectBoost/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/fbertran/selectboost/issues

Datasets:

On CRAN:

confidencecorrelationcorrelation-structuremodellingprecisionrecallselection-algorithm

57 exports 7 stars 1.47 score 50 dependencies 1 dependents 13 scripts 377 downloads

Last updated 2 years agofrom:5610332658. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 30 2024
R-4.5-winOKAug 30 2024
R-4.5-linuxOKAug 30 2024
R-4.4-winOKAug 30 2024
R-4.4-macOKAug 30 2024
R-4.3-winOKAug 30 2024
R-4.3-macOKAug 30 2024

Exports:AICc_glmnetBalasso_enet_msgps_allalasso_msgps_allauto.analyzeautoboostBIC_glmnetBboost.adjustboost.applyboost.compcorrsboost.correlation_signboost.findgroupsboost.normalizeboost.randomboost.selectboost.Xpasscompsimenet_msgps_allenetf_msgps_AICcenetf_msgps_BICenetf_msgps_Cpenetf_msgps_GCVfastboostforce.non.incgroup_func_1group_func_2lasso_cascadelasso_cv_glmnet_1selasso_cv_glmnet_1se_weightedlasso_cv_glmnet_all_5flasso_cv_glmnet_bin_1selasso_cv_glmnet_bin_alllasso_cv_glmnet_bin_minlasso_cv_glmnet_minlasso_cv_glmnet_min_weightedlasso_cv_lars_1selasso_cv_lars_minlasso_glmnet_bin_AICclasso_glmnet_bin_alllasso_glmnet_bin_BIClasso_msgps_AICclasso_msgps_alllasso_msgps_BIClasso_msgps_Cplasso_msgps_GCVplotrerrridge_logisticselectboostsgpls_spls_allsimulation_corsimulation_DATAsimulation_Xspls_spls_allsplsda_spls_alltrajC0varbvs_binomial_allvarbvs_linear_all

Dependencies:abindanimationCascadecliclustercodetoolscpp11curldeldirforeachglmnetglueigraphinterpiteratorsjpeglarslatticelatticeExtralifecyclelimmamagicmagickmagrittrMASSMatrixmsgpsnnetnnlsnor1mixpkgconfigplspngRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratRfastrlangshapesplsstatmodsurvivaltnetvarbvsvctrsVGAM

Benchmarking the SelectBoost Package for Network Reverse Engineering

Frédéric Bertrand and Myriam Maumy-Bertrand

Rendered frombenchmarking-selectboost-networks.Rmdusingknitr::rmarkdownon Aug 30 2024.

Last update: 2022-11-29
Started: 2019-05-02

Simulation Tools Provided With the Selectboost Package

Frédéric Bertrand and Myriam Maumy-Bertrand

Rendered fromsim-with-sb.Rmdusingknitr::rmarkdownon Aug 30 2024.

Last update: 2022-11-29
Started: 2019-05-02

Towards Confidence Estimates in Cascade Networks using the SelectBoost Package

Frédéric Bertrand and Myriam Maumy-Bertrand

Rendered fromconfidence-indices-Cascade-networks.Rmdusingknitr::rmarkdownon Aug 30 2024.

Last update: 2022-11-29
Started: 2019-05-02

Readme and manuals

Help Manual

Help pageTopics
AICc and BIC for glmnet logistic modelsAICc_BIC_glmnetB AICc_glmnetB BIC_glmnetB rerr ridge_logistic
Find limits for selectboost analysisauto.analyze auto.analyze.selectboost
Autoboostautoboost
Autoboost lasso diabetes first order.autoboost.res.x
Autoboost adaptative lasso diabetes first order.autoboost.res.x.adapt
Autoboost lasso diabetes second order.autoboost.res.x2
Autoboost adaptative lasso diabetes second order.autoboost.res.x2.adapt
Boost step by step functionsboost boost.adjust boost.apply boost.compcorrs boost.correlation_sign boost.findgroups boost.normalize boost.random boost.select boost.Xpass
Confidence indicesCascade_confidence net_confidence net_confidence_.5 net_confidence_thr
Simulated Cascade network and inferenceCascade_example M Net Net_inf_C
Fastboostfastboost
Fastboost lasso diabetes first order.fastboost.res.x
Fastboost adaptative lasso diabetes first order.fastboost.res.x.adapt
Fastboost lasso diabetes second order.fastboost.res.x2
Fastboost adaptative lasso diabetes second order.fastboost.res.x2.adapt
Non increasing post processinng step for selectboost analysisforce.non.inc
Generate groups by thresholding.group_func_1
Generate groups using community analysis.group_func_2
Miscellaneous plot functionsmiscplot plot.matrix
Network confidence class.network.confidence-class
plot_Selectboost_cascadeplot,network.confidence,ANY-method plot,network.confidence,network.confidence-method plot_selectboost_cascade
Plot selectboost objectplot.selectboost
Plot a summary of selectboost resultsplot.summary.selectboost
Simulations for reverse-engineeringF_score_C F_score_PB F_score_PB_075_075 F_score_PB_095_075 F_score_PB_W F_score_PL F_score_PL2 F_score_PL2_tW F_score_PL2_W F_score_PSel F_score_PSel.5 F_score_PSel.5.e2 F_score_PSel.e2 F_score_PSel_W F_score_robust nv_C nv_PB nv_PB_075_075 nv_PB_095_075 nv_PB_W nv_PL nv_PL2 nv_PL2_tW nv_PL2_W nv_PSel nv_PSel.5 nv_PSel.5.e2 nv_PSel.e2 nv_PSel_W nv_robust predictive_positive_value_C predictive_positive_value_PB predictive_positive_value_PB_075_075 predictive_positive_value_PB_095_075 predictive_positive_value_PB_W predictive_positive_value_PL predictive_positive_value_PL2 predictive_positive_value_PL2_tW predictive_positive_value_PL2_W predictive_positive_value_PSel predictive_positive_value_PSel.5 predictive_positive_value_PSel.5.e2 predictive_positive_value_PSel.e2 predictive_positive_value_PSel_W predictive_positive_value_robust results_simuls_reverse_engineering_v3 sensitivity_C sensitivity_PB sensitivity_PB_075_075 sensitivity_PB_095_075 sensitivity_PB_W sensitivity_PL sensitivity_PL2 sensitivity_PL2_tW sensitivity_PL2_W sensitivity_PSel sensitivity_PSel.5 sensitivity_PSel.5.e2 sensitivity_PSel.e2 sensitivity_PSel_W sensitivity_robust test.seq_C test.seq_PB test.seq_PB_075_075 test.seq_PB_095_075 test.seq_PB_W test.seq_PL test.seq_PL2 test.seq_PL2_tW test.seq_PL2_W test.seq_PSel test.seq_PSel.5 test.seq_PSel.5.e2 test.seq_PSel.e2 test.seq_PSel_W test.seq_robust
SelectBoostSelectBoost
Selectboost_cascadeselectboost selectboost,micro_array,micro_array-method selectboost,micro_array-method selectboost_cascade
Miscellaneous simulation functionscompsim compsim.simuls simulation simulation_cor simulation_DATA simulation_X
Summarize a selectboost analysissummary.selectboost
Plot trajectoriestrajC0 trajC0.selectboost
Variable selection functionsenetf_msgps_AICc enetf_msgps_BIC enetf_msgps_Cp enetf_msgps_GCV lasso_cascade lasso_cv_glmnet_1se lasso_cv_glmnet_1se_weighted lasso_cv_glmnet_bin_1se lasso_cv_glmnet_bin_min lasso_cv_glmnet_min lasso_cv_glmnet_min_weighted lasso_cv_lars_1se lasso_cv_lars_min lasso_glmnet_bin_AICc lasso_glmnet_bin_BIC lasso_msgps_AICc lasso_msgps_BIC lasso_msgps_Cp lasso_msgps_GCV var_select
Variable selection functions (all)alasso_enet_msgps_all alasso_msgps_all enet_msgps_all lasso_cv_glmnet_all_5f lasso_cv_glmnet_bin_all lasso_glmnet_bin_all lasso_msgps_all sgpls_spls_all splsda_spls_all spls_spls_all varbvs_binomial_all varbvs_linear_all var_select_all