cov_cda_r2: (Experimental) Optimize a ULasso linear regression model by...
In tkdmah/iilasso: Independently Interpretable Lasso

(Experimental) Optimize a ULasso linear regression model by coordinate descent algorithm using a covariance matrix with R

1 2	cov_cda_r2(Gamma, gamma, lambda, R, init.beta, delta, maxit, eps, warm, strong, sparse)

`Gamma`	covariance matrix of explanatory variables
`gamma`	covariance vector of explanatory and objective variables
`lambda`	lambda sequence
`R`	matrix using exclusive penalty term
`init.beta`	initial values of beta
`delta`	ratio of regularization between l1 and exclusive penalty terms
`maxit`	max iteration
`eps`	convergence threshold for optimization
`warm`	warm start direction: "lambda" (default) or "delta"
`strong`	whether use strong screening or not
`sparse`	whether use sparse matrix or not