R is an open‐source programming environment for statistical computing and graphics structured by numerous contributed packages. The current packages used for biodiversity research focus on limited, particular aspects of biodiversity. Most packages focus on the number and abundance of species. I present an r package named adiv that provides additional methods to measure and analyse biodiversity. adiv contains approaches to quantify species‐based, trait‐based (functional) and phylogenetic diversity (a) within communities (α diversity), (b) between communities (β diversity) and (c) to partition it over space and time (α, β and γ levels of diversity). Partitioning approaches allow evaluating whether the levels of α and β diversity could have been obtained by chance. Moreover, groups of biological entities (e.g. species of the same clade or with similar biological characteristics) that drive each level of diversity (α, β and γ) can be identified via ordination analyses. Although the package focuses on interspecific diversity in its current state, the developed approaches can also be applied to analyse intraspecific diversity or, at another level, ecosystem diversity. More generally, the functions can be applied in any discipline interested in the concept of diversity, such as economics or linguistics. Indeed, all available approaches can be easily applied at other scales and to other disciplines provided that the data have the required format: a matrix of abundance or presence/absence data of some entities in some collections and information on the differences between the entities. adiv aims to complement existing r packages to provide scientists with a wide variety of diversity indices, as each index reflects a very specific facet of biodiversity. adiv will grow in the future to integrate as many validated approaches for biodiversity analysis as possible, not yet available in r. As it includes both traditional and recent viewpoints on how biodiversity should be evaluated, adiv offers a promising platform where methods to analyse biodiversity can be developed and compared in terms of their statistical behaviour and biological relevance. Applications of the most relevant tools for a given study aim will eventually improve research on human‐driven variations in biodiversity.