Estimating forest above-ground biomass (AGB), or carbon (AGC), in tropical forests has become a major concern for scientists and stakeholders. However, AGB assessment procedures are not fully standardized and even more importantly, the uncertainty associated with AGB estimates is seldom assessed. Here, we present an r package designed to compute both AGB/AGC estimate and its associated uncertainty from forest plot datasets, using a Bayesian inference procedure. The package builds upon previous work on pantropical and regional biomass allometric equations and published datasets by default, but it can also integrate unpublished or complementary datasets in many steps. BIOMASS performs a number of standard tasks on input forest tree inventories: (i) tree species identification, if available, is automatically corrected; (ii) wood density is estimated from tree species identity; (iii) if height data are available, a local height–diameter allometry may be built; else height is inferred from pantropical or regional models; (iv) finally, AGB/AGC are estimated by propagating the errors associated with all the calculation steps up to the final estimate. R code is given in the paper and in the appendix for the purpose of illustration. The BIOMASS package should contribute to improved standards for AGB calculation for tropical forest stands, and will encourage users to report the uncertainties associated with stand-level AGB/AGC estimates in future studies.