lambdaGaussianMCRcpp: Monte Carlo based adjustment of the SLOPE tuning parameters
In agisga/grpSLOPEMC: Monte Carlo methods for Group SLOPE

lambdaGaussianMC adjusts the SLOPE regularizing sequence for correlations in the data via a Monte Carlo approach which assumes normality of the error terms.

1	lambdaGaussianMCRcpp(lambda_BH, X, lambda_length, number_of_drawings = 5000L)

`lambda_BH`	The regualrizing sequence as used in Theorem 1.1 in Bogdan et. al. (2015)
`X`	The model matrix
`lambda_length`	The corrections of the entries of `lambda_BH` will be computed up to the index given by `lambda_length` only.
`number_of_drawings`	The number of iterations in the Monte Carlo procedure

M. Bogdan, E. van den Berg, C. Sabatti, W. Su, E. Candes (2015), SLOPE – Adaptive variable selection via convex optimization, http://arxiv.org/abs/1407.3824

agisga/grpSLOPEMC documentation built on May 10, 2019, 7:32 a.m.