OBJECTIVES BCEA is a R library specifically designed to post-process the result of a health economic model. Typically, this consists in the estimation of a set of relevant parameters that can be combined to produce suitable measures of cost (c) and clinical benefits (e) associated with an intervention. Within the Bayesian framework (which is the natural environment for BCEA), this amounts to estimating a posterior distribution for the pair (e,c).  Health economic evaluations then proceed by computing some relevant summaries of the resulting decision process: is the innovative intervention t more "cost-effective" than the standard intervention t?  METHODS BCEA provides a set of functions that can be used to produce a standardised analysis, by synthesising the decision process given the current evidence and uncertainty, as well as producing several indicators that can be used to perform Probabilistic Sensitivity Analysis (PSA) to parameter and model structure uncertainty. These include the Cost-Effectiveness Acceptability Curve and the analysis of the Expected Value of Information, which can be used to prioritize research. RESULTS BCEA uses as inputs vectors of simulations from the distributions of the average costs and benefits. This naturally fits the Bayesian framework, but a frequentist analysis can also be carried out by using tools such as the bootstrap. There is scope for linking R and programs such as Excel to facilitate a comprehensive health economic analysis, including extensive PSA. CONCLUSIONS In this talk, I will present the main feature of BCEA and its applicability to the wider context of health economic evaluation and cost-effectiveness analysis.