frailty.vs: Penalized Variable Selection for Frailty Models
In frailtyHL: Frailty Models via Hierarchical Likelihood

frailty.vs is variable-selection procedures (LASSO, SCAD and HL) of fixed effects in frailty models.

1 2	frailty.vs(formula, model, penalty, data, B = NULL, v = NULL, alpha = NULL, tun1 = NULL, tun2 = NULL, varfixed = FALSE, varinit = 0.1)

`formula`	A formula object, with the response on the left of a ~ operator, and the terms for the fixed and random effects on the right. e.g. formula=Surv(time,status)~x+(1\|id), time : survival time, status : censoring indicator having 1 (0) for uncensored (censored) observation, x : fixed covariate, id : random effect.
`model`	Log-normal frailty models ("lognorm")
`penalty`	Penalty functions ("LASSO" or "SCAD" or "HL"))
`data`	Dataframe used
`B`	Initial values of fixed effects
`v`	Initial values of random effects. Zeros are default
`alpha`	Initial value of variance of random effects.
`tun1`	Tuning parameter gamma for LASSO, SCAD and HL
`tun2`	Tuning parameter omega for HL
`varfixed`	Logical value: if TRUE (FALSE), the value of one or more of the variance terms for the frailties is fixed (estimated).
`varinit`	Starting values for frailties, the default is 0.1.