cancer_multitask	example on the cancer data set of the multitask approach with a cooperative-Lasso grouping effect across tasks. Patient responses to the chemiotherapy (pCR or not-pCR) split the data set into two distinct samples. Network inference is performed jointly on these samples and graphical comparison is made between the two networks.
cancer_pooled	      example on the cancer data set which is designed to compare network inference when a clustering prior is used or not. Graphical comparison between the two inferred networks (with/without clustering prior) illustrates how inference is driven to a particular network topology when clustering is relevant (here, an affiliation structure).      
check_glasso		example that basically checks the consistency between the glasso package and the simone package to solve the l1-penalized Gaussian likelihood criterion in the n>p settings. In the n<p settings, simone provides sparser solutions than the glasso package since the underlying Lasso problems are solved with an active set algorithm instead of the shooting/pathwise coordinate algorithm.
simone_multitask	example of multitask learning on simulated, steady-state data: two networks are generated by randomly perturbing a common ancestor with the coNetwork function. These two networks are then used to generate two multivariate Gaussian samples. Multitask learning is applied and a simple illustration of the use of the setOptions function is given.
simone_steadyState	example of how to learn a single network from steady-state data. A sample is first generated with the rNetwork and rTranscriptData functions. Then the path of solutions of the neighborhood selection method (default for single task steady-state data) is computed.
simone_timeCourse             example of how to learn a single network from time-course data. A sample is first generated with the rNetwork and rTranscriptData functions and the path of solutions of the VAR(1) inference method is computed, with and without clustering prior.

