  Applications on inference of biological networks have raised a strong
interest in the problem of graph estimation in high-dimensional Gaussian
graphical models. To handle this problem, we propose a two-stage procedure
which first builds a family of candidate graphs from the data, and then selects
one graph among this family according to a dedicated criterion. This estimation
procedure is shown to be consistent in a high-dimensional setting, and its risk
is controlled by a non-asymptotic oracle-like inequality. The procedure is
tested on a real data set concerning gene expression data, and its performances
are assessed on the basis of a large numerical study. The procedure is
implemented in the R-package GGMselect available on the CRAN.
