Helper to define list of parameters to control the ZIPLNnetwork fit. All arguments have defaults.

- backend
optimization back used, either "nlopt" or "torch". Default is "nlopt"

- inception_cov
Covariance structure used for the inception model used to initialize the PLNfamily. Defaults to "full" and can be constrained to "diagonal" and "spherical".

- trace
a integer for verbosity.

- n_penalties
an integer that specifies the number of values for the penalty grid when internally generated. Ignored when penalties is non

`NULL`

- min_ratio
the penalty grid ranges from the minimal value that produces a sparse to this value multiplied by

`min_ratio`

. Default is 0.1.- penalize_diagonal
boolean: should the diagonal terms be penalized in the graphical-Lasso? Default is

`TRUE`

- penalty_weights
either a single or a list of p x p matrix of weights (default: all weights equal to 1) to adapt the amount of shrinkage to each pairs of node. Must be symmetric with positive values.

- config_post
a list for controlling the post-treatment (optional bootstrap, jackknife, R2, etc).

- config_optim
a list for controlling the optimizer (either "nlopt" or "torch" backend). See details

- inception
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.

See `PLNnetwork_param()`

for a full description of the optimization parameters. Note that some defaults values are different than those used in `PLNnetwork_param()`

:

"ftol_out" (outer loop convergence tolerance the objective function) is set by default to 1e-6

"maxit_out" (max number of iterations for the outer loop) is set by default to 100