dissCqN is a small package designed to make the process of calculating multiple or pairwise assemblage dissimilarity via the CqN generalisation of similarity indices (Chao et al., 2008; Jost et al., 2011) relatively straightforward and fast. Although CqN can also be calculated using the SpadeR package (e.g. SpadeR::SimilarityMult() and SpadeR::SimilarityPair()) – which generates a more comprehensive set of measures and also standard errors/confidence intervals – the main advantage of dissCqN is it’s simplicity and speed, when only the original empirical CqN measures are required (and also if dissimilarity is preferred to similarity). Everything can be accomplished with a single function, dissCqN(), which takes a matrix of assemblages x species as it’s first argument (or a list of species interaction matrices, for network dissimilarity).

## Installation

You can install the released version of dissCqN from CRAN with:

install.packages("dissCqN")

And the development version from GitHub with:

devtools::install_github("murphymv/dissCqN@dev")

## Examples

See the following vignette for a demonstration:

## References

Chao, A., Jost, L., Chiang, S. C., Jiang, Y.-H., & Chazdon, R. L. (2008). A Two-Stage Probabilistic Approach to Multiple-Community Similarity Indices. Biometrics, 64(4), 1178–1186. https://doi.org/10/fcvn63

Jost, L., Chao, A., & Chazdon, R. L. (2011). Compositional similarity and beta diversity. In A. E. Magurran & B. J. McGill (Eds.), Biological Diversity: Frontiers in Measurement and Assessment (pp. 66–84). Oxford University Press.