CorShrinkDataNuclear: Nuclear norm penalization of the covariance matrix
In kkdey/CorShrink: Adaptive Shrinkage of Correlation and Covariance Matrices

Performs a nuclear norm penalization of the coavriance matrix.

1 2	CorShrinkDataNuclear(data_with_missing, alpha = 0.01, stepsize = 1, max_iter = 1000, tol = 1e-04, verbose = TRUE)

`data_with_missing`	The samples by features data matrix. May contain NA values.
`alpha`	The tuning parameter for the gradient descent(GD) iteration update.
`stepsize`	The stepsize for the gradient descent algorithms.
`max_iter`	The maximum number of iterations for the GD.
`tol`	The tolerance level when to stop the iterations.
`verbose`	If TRUE, the function prints the objective value on each run, which can be used to check if the objective is decreasing over iterations (as it should be) or not.

data("sample_by_feature_data")
out = CorShrinkDataNuclear(sample_by_feature_data, stepsize = 1, max_iter = 100)
plot(svd(out$estS)$d)
corrplot::corrplot(as.matrix(out$estR), 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")