The problem of model selection is inevitable in an increasingly large number of applications involving partial theoretical knowledge and vast amounts of information, like in medicine, biology or economics. The associated techniques are intended to determine which variables are "important" to "explain a phenomenon under investigation. The terms "important" and "explain" can have very different meanings according to the context and, in fact, model selection can be applied to any situation where one tries to balance variability with complexity.
This package implements the prediction divergence criterion model selection apporach proposed in Avella Medina et al., (2017).
This package can be installed as
# install.packages("devtools")
devtools::install_github("smac-group/pdc")
TO DO
TO DO