kkt_check: Check of KKT Conditions for Linear Mixed Model
In sahirbhatnagar/ggmix: Variable Selection in Linear Mixed Models for SNP Data

This function checks the KKT conditions

kkt_check(eta, sigma2, beta, eigenvalues, x, y, nt, lambda, tol.kkt = 0.001)

grr_sigma2(eta, sigma2, beta, eigenvalues, x, y, nt)

grr_beta0(eta, sigma2, beta, eigenvalues, x, y, nt)

`eta`	current estimate of the eta parameter
`sigma2`	current estimate of the sigma2 parameter
`beta`	current estimate of the beta parameter including the intercept. this should be of length p+1, where p is the number of variables.
`eigenvalues`	non-zero eigenvalues of the kinship matrix, or the square of the singular values of the matrix used to construct the kinship matrix
`x`	rotated x. Should be U^T X, where U is the matrix of eigenvectors and X contains the first column of ones for the intercept. x should be a mtrix of dimension n x (p+1). These are outputted by the constructor functions. See `ggmix_data_object` for details
`y`	rotated y. Should be U^T Y, where U is the matrix of eigenvectors and Y is the response.
`nt`	total number of observations
`lambda`	A user supplied lambda sequence (this is the tuning parameter). Typical usage is to have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use with care. Do not supply a single value for lambda (for predictions after CV use predict() instead). Supply instead a decreasing sequence of lambda values. glmnet relies on its warms starts for speed, and its often faster to fit a whole path than compute a single fit.
`tol.kkt`	Tolerance for determining if an entry of the subgradient is zero