hlike.frailty: Competing Risk Frialty Models using H-Likelihood
In frailtyHL: Frailty Models via Hierarchical Likelihood

Perform hierarchical likelihood estimation of the univariate frailty model, cause-specific frailty model and subhazard frailty model. Assuming either a univariate normal or multivariate normal distribution for the random effects V, where different covariance structures can be assumed for the multivariate normal distribution.

1 2	hlike.frailty(formula, data, inits, order = 1, frailty.cov = "none", subHazard = FALSE, alpha = 0.05, MAX.ITER = 100, TOL = 1e-06)

`formula`	left-hand side is a CmpRsk object (see details), right-hand side is predictors (currently limited to numeric main effects), must include a cluster term that identifies the cluster variable.
`data`	dataframe containing the variables used in the formula
`inits`	list of initial values, three named components: beta, v and theta
`order`	numeric, order of the Laplace approximation, 0=no order, 1=first-order, 2=second-order; second-order only applies to models with a univariate normal distribution
`frailty.cov`	character string "none", "independent" or "unstructured" specifying the covariance structure for a multivariate normal distribution; "none" indicates univariate normal distribution
`subHazard`	logical, if TRUE fits the subhazard frailty model
`alpha`	numeric, 100(1-alpha) percent confidence intervals
`MAX.ITER`	numeric, maximum number of iterations
`TOL`	numeric, tolerance limit