covCdaC: Optimize a linear regression model by coordinate descent...
In hmlasso: Lasso with High Missing Rate

Optimize a linear regression model by coordinate descent algorithm using a covariance matrix

1 2	covCdaC(Gamma, gamma, lambda, R, init_beta, delta = 0, maxit = 10000, eps = 1e-04, warm = "lambda", strong = TRUE)

`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