A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets
AICc and BIC for glmnet logistic models
Summarize a selectboost analysis
SelectBoost: A General Algorithm to Enhance the Performance of Variabl...
Miscellaneous simulation functions
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_cascade
Plot selectboost object
Plot a summary of selectboost results
Simulations for reverse-engineering
Selectboost_cascade
Plot trajectories
Variable selection functions (all)
Variable selection functions
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