This function is intended to design lowlevel uses of SIMoNe by specifying various parameters of the underlying algorithms.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  setOptions(normalize = TRUE,
verbose = TRUE,
penalties = NULL,
penalty.min = NULL,
penalty.max = NULL,
n.penalties = 100,
edges.max = Inf,
edges.sym.rule = NULL,
edges.steady = "neighborhood.selection",
edges.coupling = "coopLasso",
clusters.crit = "BIC",
clusters.meth = "bayesian",
clusters.qmin = 2,
clusters.qmax = 4)

normalize 
logical specifying wether the data should be normalized to unit
variance. The normalization is made taskwisely in the multiple
sample setting. Default is 
verbose 
a logical that indicates verbose mode to display
progression. Default is 
penalties 
vector of decreasing penalty levels for the network
estimation. If 
penalty.min 
The minimal value of the penalty that will be tried for network
inference. If 
penalty.max 
The maximal value of the penalty that will be tried for network
inference. If 
n.penalties 
integer that indicates the number of penalties to put in the

edges.max 
integer giving an upper bound for the number of edges to select: if
a network is inferred along the algorithm with a number of edges
overstepping 
edges.steady 
a character string indicating the method to use for the network
inference associated to steadystate data, one task
framework. Either 
edges.coupling 
character string (either 
edges.sym.rule 
character string ( 
clusters.crit 
criterion to select the network that is used to find an underlying
clustering. Either 
clusters.qmin 
minimum number of classes for clustering. Default is 2. 
clusters.qmax 
maximum number of classes for clustering. Default is 4. 
clusters.meth 
character string indicating the strategy used for the estimation:

A list that contains all the specified parameters.
If the user specifies its own penalties
vector, all the
networks inferred during the algorithm will be kept, even if they
share the very same number of edges.
On the other hand, if you only specify penalty.max
and/or
penalty.min
and/or n.penalties
, the algorithm will only
kept the networks who show different numbers of edges. That is to say,
the number of networks stocked in the output of simone
generally does not have a length equal to n.penalties
.
J. Chiquet
1 2  ## generate an object (list) with the default parameters
setOptions()

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