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Abstract

EMMA – End Member Modelling Analysis of grain-size data is a technique to unmix multimodal grain-size data sets, i.e., to decompose the data into the underlying grain-size distributions (loadings) and their contributions to each sample (scores). The R package EMMAgeo contains a series of functions to perform EMMA based on eigenspace decomposition. The data are rescaled and transformed to receive results in meaningful units, i.e., volume percentage. EMMA can be performed in a deterministic and two robust ways, the latter taking into account incomplete knowledge about model parameters. The model outputs can be interpreted in terms of sediment sources, transport pathways and transport regimes (loadings) as well as their relative importance throughout the sample space (scores).