A review of compositional data analysis and recent advances

Abstract

Compositional data are positive multivariate data with unity sum constraint that have emerged over the last years in numerous scientific fields. Ever since, numerous models and approaches have been proposed for analyzing such data in the last 40 years. We list some of their properties and difficulties and review many techniques proposed over this period. In particular, we focus on transformations, distributions, regression models, discriminant analysis and clustering techniques, dimensionality reduction techniques, variable selection algorithms and finally we list some books and R packages developed for compositional data analysis.

Topics & Concepts