A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets
AICc and BIC for glmnet logistic models
Find limits for selectboost analysis
Autoboost
Boost step by step functions
Fastboost
Non increasing post processinng step for selectboost analysis
Generate groups by thresholding.
Generate groups using community analysis.
Miscellaneous plot functions
Network confidence class.
Plot selectboost object
Plot a summary of selectboost results
plot_Selectboost_cascade
Simulations for reverse-engineering
SelectBoost
Selectboost_cascade
Miscellaneous simulation functions
Summarize a selectboost analysis
Plot trajectories
Variable selection functions
Variable selection functions (all)
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.
Useful links