Definition of various kinds of options in the algorithm.
1 2 3 4  lmmlassoControl(tol = 10^(4), trace = 1, maxIter = 1000,
maxArmijo = 20, number = 5, a_init = 1, delta = 0.1, rho = 0.001,
gamma = 0, lower = 10^(6), upper = 10^8, seed = 532,
VarInt = c(0, 10), CovInt = c(5, 5), thres = 10^(4))

tol 
convergence tolerance 
trace 
integer. 1 prints no output, 2 prints warnings, 3 prints the current function values and warnings (not recommended) 
maxIter 
maximum number of (outer) iterations 
maxArmijo 
maximum number of steps to be chosen in the Armijo Rule. If the maximum is reached, the algorithm continues with optimizing the next coordinate. 
number 
integer. Determines the active set algorithm. The zero fixedeffects coefficients are only updated each number iteration. It may be that a smaller number increases the speed of the algorithm. Use 0 ≤ number ≤ 5. 
a_init 
α_{init} in the Armijo step. See Schelldorfer et. al. (2010). 
delta 
δ in the Armijo step. See Schelldorfer et. al. (2010) 
rho 
ρ in the Armijo step. See Schelldorfer et. al. (2010) 
gamma 
γ in the Armijo step. See Schelldorfer et. al. (2010) 
lower 
lower bound for the Hessian 
upper 
upper bound for the Hessian 
seed 
set.seed for calculating the starting value, which performs a 10fold crossvalidation. 
VarInt 
Only for opt="optimize". The interval for the variance parameters used in "optimize". See help("optimize") 
CovInt 
Only for opt="optimize". The interval for the covariance parameters used in "optimize". See help("optimize") 
thres 
If a variance or covariance parameter has smaller absolute value than thres, the parameter is set to exactly zero. 
For the Armijo step parameters, see Bertsekas (2003)
Dimitri P. Bertsekas (2003) Nonlinear Programming, Athena Scientific.
J. Schelldorfer, P. B\"uhlmann and S. van de Geer (2011), Estimation for HighDimensional Linear MixedEffects Models Using \ell_1penalization, arXiv preprint 1002.3784v2
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