BayesGLASSOAdaptive: Adaptive Bayesian Graphical LASSO.
In SachaEpskamp/BayesGGM: Bayesian Estimators for Gaussian Graphical Models

BayesGLASSOAdaptive Provides a regularized precision matrix estimate using a Bayesian GLASSO, in which different shrinkage parameter values are used for each off-diagonal element.

1 2	BayesGLASSOAdaptive(data, lambda_shape = 0.01, lambda_rate = 1e-06, nBurnin = 10000, nIter = 10000, verbose = TRUE, lambdaDiag = 1)

`data`	A data matrix with rows representinh participants and columns representing variables/nodes.
`lambda_shape`	The rate parameter for the hyperprior of lambda, the parameter that determines the amount of shirnkage.
`lambda_rate`	The scale parameter for the hyperprior of lambda, the parameter that determines the amount of shirnkage.
`nIter`	The number of iterations for the Gibbs-sampler.
`verbose`	If 'TRUE'displays a progress bar.
`lambdaDiag`	The shrinkage parameter value to be used for diagonal elements.
`nBurning`	The number of burn-in iterations for the Gibbs-sampler.

The function returns the following output:

`Omega`	A dataframe containing the estimated precision matrix for each itteration of the Gibbs-sampler.
`pcor`	A dataframe containing the estimated partial correlation matrix for each itteration of the Gibbs-sampler.
`lambda_sq`	A dataframe containing the estimates for the local hyper parameter lambda for each itteration of the Gibbs-sampler.
`tau_sq`	A dataframe containing the estimates for the global hyper parameter tau for each itteration of the Gibbs-sampler.
`Strength`	A dataframe containing the estimated strength centrality for each node for each itteration of the Gibbs-sampler.
`Closeness`	A dataframe containing the estimated closeness centrality for each node for each itteration of the Gibbs-sampler.
`Betweenness`	A dataframe containing the estimated betweenness centrality for each node for each itteration of the Gibbs-sampler.
`optwi`	The point estimate for the precision matrix obtained by taking the mode of the posterior distribution.
`optpcor`	The point estimate for the partial correlation matrix obtained by taking the mode of the posterior distribution.
`CredInt`	The 95% Credibility interval for the elements of the precision matrix.
`optStrength`	The point estimate for strength centrality of each node based on the point estimate of the partial correlation matrix.
`CIStrength`	The 95% Credibility Interval for the strength centrality of each node.
`optCloseness`	The point estimate for closeness centrality of each node based on the point estimate of the partial correlation matrix.
`CICloseness`	The 95% Credibility Interval for the closeness centrality of each node.
`optBetween`	The point estimate for betweenness centrality of each node based on the point estimate of the partial correlation matrix.
`CIBetween`	The 95% Credibility Interval for the betweenness centrality of each node.

The following output is only returned in case of missing data.

`Missing`	Indicator for which observations where missing.
`ImputedValues`	The imputed values for each missing datapoint for each itteration of the Gibbs-sampler.
`CompleteData`	The original dataset with missing values replaced by the mean imputed value for each missing data point.
`OriginalData`	The original dataset with missing values.