Fit solution paths for linear or logistic regression models penalized by lasso (alpha = 1) or elasticnet (1e4 < alpha < 1) over a grid of values for the regularization parameter lambda.
COPY_biglasso_main( X, y.train, ind.train, ind.col, covar.train, family = c("gaussian", "binomial"), alphas = 1, K = 10, ind.sets = NULL, nlambda = 200, lambda.min.ratio = if (n > p) 1e04 else 0.001, nlam.min = 50, n.abort = 10, base.train = NULL, pf.X = NULL, pf.covar = NULL, eps = 1e05, max.iter = 1000, dfmax = 50000, lambda.min = if (n > p) 1e04 else 0.001, power_scale = 1, power_adaptive = 0, return.all = FALSE, warn = TRUE, ncores = 1 )
family  Either "gaussian" (linear) or "binomial" (logistic). 

alphas  The elasticnet mixing parameter that controls the relative
contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is
defined as $$ \alpha\beta_1 + (1\alpha)/2\beta_2^2.$$

K  Number of sets used in the CrossModel Selection and Averaging
(CMSA) procedure. Default is 
ind.sets  Integer vectors of values between 
nlambda  The number of lambda values. Default is 
lambda.min.ratio  The smallest value for lambda, as a fraction of
lambda.max. Default is 
nlam.min  Minimum number of lambda values to investigate. Default is 
n.abort  Number of lambda values for which prediction on the validation
set must decrease before stopping. Default is 
base.train  Vector of base predictions. Model will be learned starting from these predictions. This can be useful if you want to previously fit a model with largeeffect variables that you don't want to penalize. 
pf.X  A multiplicative factor for the penalty applied to each coefficient.
If supplied, 
pf.covar  Same as 
eps  Convergence threshold for inner coordinate descent.
The algorithm iterates until the maximum change in the objective after any
coefficient update is less than 
max.iter  Maximum number of iterations. Default is 
dfmax  Upper bound for the number of nonzero coefficients. Default is

lambda.min  This parameter has been renamed 
power_scale  When using lasso (alpha = 1), penalization to apply that
is equivalent to scaling genotypes dividing by (standard deviation)^power_scale.
Default is 1 and corresponding to standard scaling. Using 0 would correspond
to using unscaled variables and using 0.5 is Pareto scaling. If you e.g. use

power_adaptive  Multiplicative penalty factor to apply to variables
in the form of 1 / m_j^power_adaptive, where m_j is the marginal statistic
for variable j. Default is 0, which effectively disables this option.
If you e.g. use 
return.all  Deprecated. Now always return all models. 
warn  Whether to warn if some models may not have reached a minimum.
Default is 
The objective function for linear regression (family = "gaussian"
) is
$$\frac{1}{2n}\textrm{RSS} + \textrm{penalty},$$ for logistic regression
(family = "binomial"
) it is $$\frac{1}{n} loglike +
\textrm{penalty}.$$