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We demonstrate that solutions for gamma shared frailty models can be obtained exactly via penalized estimation. Similarly, Gaussian frailty models are closely linked to penalized models. This makes it possible to apply penalized estimation to other frailty models using Laplace approximations. Fitting frailty models with penalized likelihoods can be made quite rapid by taking advantage of computational methods available for penalized models. We have implemented penalized regression for the coxph function of Splus and illustrate the algorithms with examples using the Cox model.