Prova tipográfica ; The Cox proportional hazards regression model has become the traditional choice for modeling survival data in medical studies. Usually, this model is assumed to be (semi)parametric, and the effects of continuous predictors on log-hazards are modeled linearly. In practice, however, the effect of a given continuous predictor can be unknown. To introduce flexibility into the Cox model, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuous covariate has on the outcome, results can be expressed in terms of splines-based hazard ratio (HR) curves, taking a specific covariate value as reference. Despite the potential advantages of using spline smoothing methods in survival analysis, there is currently no analytical method in the R software to choose the optimal degrees of freedom in multivariable Cox models (with two or more nonlinear covariate e ects). This paper describes an R package, called smoothHR, that allows the computation of pointwise estimates of the HRs -and their corresponding confidence limits- of continuous predictors introduced nonlinearly. In addition the package provides a function for choosing automatically the degrees of freedom in multivariable Cox models. The package is available from the R homepage http://cran.r-project.org. We illustrate the use of the key functions of the smoothHR package using data from a study on breast cancer and data on acute coronary syndrome, from Galicia, Spain.