Survival analysis features heavily as an important part of health economic evaluation, an increasingly important component of medical research. In this setting, it is important to estimate the mean time to the survival endpoint using limited information (typically from randomized trials) and thus it is useful to consider parametric survival models. In this paper, we review the features of the R package survHE, specifically designed to wrap several tools to perform survival analysis for economic evaluation. In particular, survHE embeds both a standard, frequentist analysis (through the R package flexsurv) and a Bayesian approach, based on Hamiltonian Monte Carlo (via the R package rstan) or integrated nested Laplace approximation (with the R package INLA). Using this composite approach, we obtain maximum flexibility and are able to pre-compile a wide range of parametric models, with a view of simplifying the modelers' work and allowing them to move away from non-optimal work flows, including spreadsheets (e.g., Microsoft Excel).