General function to apply network estimation methods in `EGAnet`

## Arguments

- data
Matrix or data frame. Should consist only of variables to be used in the analysis

- n
Numeric (length = 1). Sample size if

`data`

provided is a correlation matrix- corr
Character (length = 1). Method to compute correlations. Defaults to

`"auto"`

. Available options:`"auto"`

--- Automatically computes appropriate correlations for the data using Pearson's for continuous, polychoric for ordinal, tetrachoric for binary, and polyserial/biserial for ordinal/binary with continuous. To change the number of categories that are considered ordinal, use`ordinal.categories`

(see`polychoric.matrix`

for more details)`"cor_auto"`

--- Uses`cor_auto`

to compute correlations. Arguments can be passed along to the function`"pearson"`

--- Pearson's correlation is computed for all variables regardless of categories`"spearman"`

--- Spearman's rank-order correlation is computed for all variables regardless of categories

For other similarity measures, compute them first and input them into

`data`

with the sample size (`n`

)- na.data
Character (length = 1). How should missing data be handled? Defaults to

`"pairwise"`

. Available options:`"pairwise"`

--- Computes correlation for all available cases between two variables`"listwise"`

--- Computes correlation for all complete cases in the dataset

- model
Character (length = 1). Defaults to

`"glasso"`

. Available options:`"BGGM"`

--- Computes the Bayesian Gaussian Graphical Model. Set argument`ordinal.categories`

to determine levels allowed for a variable to be considered ordinal. See`?BGGM::estimate`

for more details`"glasso"`

--- Computes the GLASSO with EBIC model selection. See`EBICglasso.qgraph`

for more details`"TMFG"`

--- Computes the TMFG method. See`TMFG`

for more details

- network.only
Boolean (length = 1). Whether the network only should be output. Defaults to

`TRUE`

. Set to`FALSE`

to obtain all output for the network estimation method- verbose
Boolean (length = 1). Whether messages and (insignificant) warnings should be output. Defaults to

`FALSE`

(silent calls). Set to`TRUE`

to see all messages and warnings for every function call- ...
Additional arguments to be passed on to

`auto.correlate`

and the different network estimation methods (see`model`

for model specific details)

## References

**Graphical Least Absolute Shrinkage and Selection Operator (GLASSO)**

Friedman, J., Hastie, T., & Tibshirani, R. (2008).
Sparse inverse covariance estimation with the graphical lasso.
*Biostatistics*, *9*(3), 432–441.

**GLASSO with Extended Bayesian Information Criterion (EBICglasso)**

Epskamp, S., & Fried, E. I. (2018).
A tutorial on regularized partial correlation networks.
*Psychological Methods*, *23*(4), 617–634.

**Bayesian Gaussian Graphical Model (BGGM)**

Williams, D. R. (2021).
Bayesian estimation for Gaussian graphical models: Structure learning, predictability, and network comparisons.
*Multivariate Behavioral Research*, *56*(2), 336–352.

**Triangulated Maximally Filtered Graph (TMFG)**

Massara, G. P., Di Matteo, T., & Aste, T. (2016).
Network filtering for big data: Triangulated maximally filtered graph.
*Journal of Complex Networks*, *5*, 161-178.