Cox Proportional Hazards Model with Componentwise Likelihood based 
    Boosting, tuned for the optimal number of boosting steps

Factors automatically get converted to dummy columns, 
    ordered factors to integer.

Argument of: CoxBoost::CoxBoost

vector of length p.unpen with indices of mandatory covariates, where parameter estimation should be performed unpenalized.

Argument of: CoxBoost::CoxBoost

logical value indicating whether covariates should be standardized for estimation. This does not apply for mandatory covariates, i.e., these are not standardized.

Argument of: CoxBoost::CoxBoost

penalty value for the update of an individual element of the parameter vector in each boosting step.

Argument of: CoxBoost::CoxBoost

indicates the criterion to be used for selection in each boosting step. "pscore" corresponds to the penalized score statistics, "score" to the un-penalized score statistics. Different results will only be seen for un-standardized covariates ("pscore" will result in preferential selection of covariates with larger covariance), or if different penalties are used for different covariates. "hpscore" and "hscore" correspond to "pscore" and "score". However, a heuristic is used for evaluating only a subset of covariates in each boosting step, as described in Binder et al. (2011). This can considerably speed up computation, but may lead to different results.

Argument of: CoxBoost::CoxBoost

determines the step-size modification factor by which the natural step size of boosting steps should be changed after a covariate has been selected in a boosting step. The default (value 1) implies constant penalties, for a value < 1 the penalty for a covariate is increased after it has been selected in a boosting step, and for a value > 1 the penalty it is decreased. If pendistmat is given, penalty updates are only performed for covariates that have at least one connection to another covariate.

Argument of: CoxBoost::cv.CoxBoost

logical value indicating whether progress in estimation should be indicated by printing the number of the cross-validation fold and the index  of the covariate updated.