Provides a function to check the
importance of a feature based on a dependent classification
variable. An ensemble of feature selection methods
is used to determine the normalized importance value of
all features. Combining these methods in one function
(building the cumulative importance values) provides a
stable feature selection tool. This selection
can also be viewed in a barplot using the barplot_fs() function
and proved using the evaluation function efs_eval().

party, pROC, randomForest, ROCR, grDevices, graphics, stats,
utils

Exploratory Graph Analysis: A Framework for Estimating the
Number of Dimensions in Multivariate Data Using Network
Psychometrics
c(person("Hudson", "Golino", email = "hfg9s@virginia.edu",
role = c("aut", "cre")), person("Alexander", "Christensen",
email = "alexpaulchristensen@gmail.com", role = "ctb"))
An implementation of the Exploratory Graph Analysis (EGA) framework for dimensionality assessment. EGA is part of a new area called network psychometrics that focuses on the estimation of undirected network models in psychological datasets. EGA estimates the number of dimensions or factors using graphical lasso or Triangulated Maximally Filtered Graph (TMFG) and a weighted network community analysis. A bootstrap method for verifying the stability of the estimation is also available. The fit of the structure suggested by EGA can be verified using confirmatory factor analysis and a direct way to convert the EGA structure to a confirmatory factor model is also implemented. Documentation and examples are available. Golino, H. F., & Epskamp, S. (2017) <doi:10.1371/journal.pone.0174035>. Golino, H. F., & Demetriou, A. (2017) <doi:10.1016/j.intell.2017.02.007> Golino, H., Shi, D., Garrido, L. E., Christensen, A. P., Nieto, M. D., Sadana, R., & Thiyagarajan, J. A. (2018) <doi:10.31234/osf.io/gzcre>. Christensen, A. P. & Golino, H.F. (2019)  <doi:10.31234/osf.io/9deay>.
Hudson Golino [aut, cre],
Alexander Christensen [ctb]

Exploratory Graph Analysis - A Framework for Estimating the
Number of Dimensions in Multivariate Data Using Network
Psychometrics
c(person("Hudson", "Golino", email = "hfg9s@virginia.edu",
role = c("aut", "cre")), person("Alexander", "Christensen",
email = "alexpaulchristensen@gmail.com", role = "aut"),
person("Maria", "Dolores Nieto",
email = "acinodam@gmail.com", role = "ctb"),
person("Luis Eduardo", "Garrido",
email = "garrido.luiseduardo@gmail.com", role = "ctb"))
Hudson Golino [aut, cre],
Alexander Christensen [aut],
Maria Dolores Nieto [ctb],
Luis Eduardo Garrido [ctb]