Helper to define list of parameters to control the PLN fit. All arguments have defaults.
optimization back used, either "nlopt" or "torch". Default is "nlopt"
Covariance structure used for the inception model used to initialize the PLNfamily. Defaults to "full" and can be constrained to "diagonal" and "spherical".
a integer for verbosity.
an integer that specifies the number of values for the penalty grid when internally generated. Ignored when penalties is non
the penalty grid ranges from the minimal value that produces a sparse to this value multiplied by
min_ratio. Default is 0.1.
boolean: should the diagonal terms be penalized in the graphical-Lasso? Default is
either a single or a list of p x p matrix of weights (default filled with 1) to adapt the amount of shrinkage to each pairs of node. Must be symmetric with positive values.
a list for controlling the post-treatment (optional bootstrap, jackknife, R2, etc).
a list for controlling the optimizer (either "nlopt" or "torch" backend). See details
Set up the parameters initialization: by default, the model is initialized with a multivariate linear model applied on log-transformed data, and with the same formula as the one provided by the user. However, the user can provide a PLNfit (typically obtained from a previous fit), which sometimes speeds up the inference.
list of parameters configuring the fit.
PLN_param() for a full description of the generic optimization parameters. PLNnetwork_param() also has two additional parameters controlling the optimization due the inner-outer loop structure of the optimizer:
"ftol_out" outer solver stops when an optimization step changes the objective function by less than xtol multiplied by the absolute value of the parameter. Default is 1e-6
"maxit_out" outer solver stops when the number of iteration exceeds maxit_out. Default is 50