Use multiple feature selection algorithms to derive robust
feature sets for two or multiclass classification problems.
To select a set of features used for successful classification of two or more groups of samples, multiple classification and feature selection algorithms are utilised. By combining the results of all methods and applying a bootstrapping approach a robust set of features with high power to distinguish the sample groups is selected.
igraph, ROCR, gbm, colorRamps, gplots, gtools, pamr,
randomForest, Boruta, caret, tgp, mlegp, penalizedSVM

Bootstrap Methods for Various Network Estimation Routines
IsingFit, qgraph, dplyr (>= 0.3.0.2), gtools, corpcor
Bootstrap standard errors on various network estimation routines, such as EBICglasso from the qgraph package and IsingFit from the IsingFit package.

methods, IsingFit, qgraph, dplyr (>= 0.3.0.2), gtools,
corpcor, IsingSampler, mvtnorm, abind, Matrix
Bootstrap standard errors on various network estimation routines, such as EBICglasso() from the 'qgraph' package and IsingFit() from the 'IsingFit' package.

methods, IsingFit, qgraph, dplyr (>= 0.3.0.2), gtools,
corpcor, IsingSampler, mvtnorm, abind, Matrix, parallel, huge,
parcor
Bootstrap methods to assess accuracy and stability of estimated network structures
and centrality indices. Allows for flexible specification of any undirected network
estimation procedure in R, and offers default sets for 'qgraph', 'IsingFit', 'IsingSampler',
'glasso', 'huge' and 'parcor' packages.

methods, IsingFit, qgraph, dplyr (>= 0.3.0.2), gtools,
corpcor, IsingSampler, mvtnorm, abind, Matrix, parallel, huge,
parcor, mgm

methods, igraph, IsingFit, qgraph, dplyr (>= 0.3.0.2), tidyr,
gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix,
parallel, huge, parcor, mgm, relaimpo

methods, igraph, IsingFit, qgraph, dplyr (>= 0.3.0.2), tidyr,
gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix,
parallel, huge, parcor, mgm (>= 1.2), relaimpo, NetworkToolbox
(>= 1.1.0), pbapply, graphicalVAR, BDgraph, psych