# Entropy Partitioning to Measure Diversity

entropart is an R package that provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.

Estimation-bias corrections are available.

## Details

In the entropart package, individuals of different *species* are counted in several *communities* which may (or not) be agregated to define a *metacommunity*. In the metacommunity, the probability to find a species in the weighted average of probabilities in communities. This is a naming convention, which may correspond to plots in a forest inventory or any data organized the same way.

Basic functions allow computing diversity of a community. Data is simply a vector of probabilities (summing up to 1) or of abundances (integer values that are numbers of individuals). Calculate entropy with functions such as *Tsallis*, *Shannon*, *Simpson*, *Hurlbert* or *GenSimpson* and explicit diversity (i.e. effective number of species) with *Diversity* and others. By default, the best available estimator of diversity will be used, according to the data.

Communities can be simulated by *rCommunity*, explicitely declared as a species distribution (*as.AbdVector* or *as.ProbaVector*), and plotted.

Phylogenetic entropy and diversity can be calculated if a phylogenetic (or functional), ultrametric tree is provided. See *PhyloEntropy*, *Rao* for examples of entropy and *PhyloDiversity* to calculate phylodiversity, with the state-of-the-art estimation-bias correction. Similarity-based diversity is calculated with *Dqz*, based on a similarity matrix.

# Vignettes

A full documentation is available in the main vignette. In R, type: `vignette("entropart")`

. It is a continuous update of the paper published in the Journal of Statistical Software (Marcon & Hérault, 2015).

A quick introduction is in `vignette("docs", "entropart")`

.

## Reference

Marcon, E. and Herault, B. (2015). entropart: An R Package to Measure and Partition Diversity. *Journal of Statistical Software*. 67(8): 1-26.