iCorShrink2Data: Adaptive regularized GLASSO of partial correlations from a...
In kkdey/CorShrink: Adaptive Shrinkage of Correlation and Covariance Matrices

Performs an adaptove regularized GLASSO of partial correlations from a data matrix with missing data using the Fisher Z-score formulation

1 2	iCorShrink2Data(data_with_missing, alpha, expo = 0.05, shift = 0.01, lambda = 0.8, max_iter = 10, epsilon = 0.001)

`data_with_missing`	The samples by features data matrix. May contain NA values.
`alpha`	The tuning parameter
`expo`	The exponent on the scaling used in the adaptive regularization of tuning parameter
`shift`	The shift in the scaling used in adaptive regularization of tuning parameter
`lambda`	The weight on the constraint for sample size bias
`max_iter`	The maximum number of iterations for the adaptive GLASSO run.
`epsilon`	The tolerance level for the relative error specifying when to stop

data("sample_by_feature_data")
out = iCorShrink2Data(sample_by_feature_data, alpha = 0.1, max_iter = 3)
corrplot::corrplot(as.matrix(out), diag = FALSE,
        col = colorRampPalette(c("blue", "white", "red"))(200),
        tl.pos = "td", tl.cex = 0.4, tl.col = "black",
        rect.col = "white",na.label.col = "white",
        method = "color", type = "upper")