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hilldiv  

Integral Analysis of Diversity Based on Hill Numbers
View on CRAN: Click here


Download and install hilldiv package within the R console
Install from CRAN:
install.packages("hilldiv")

Install from Github:
library("remotes")
install_github("cran/hilldiv")

Install by package version:
library("remotes")
install_version("hilldiv", "1.5.1")



Attach the package and use:
library("hilldiv")
Maintained by
Antton Alberdi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-01
Latest Update: 2019-10-01
Description:
Tools for analysing, comparing, visualising and partitioning diversity based on Hill numbers. 'hilldiv' is an R package that provides a set of functions to assist analysis of diversity for diet reconstruction, microbial community profiling or more general ecosystem characterisation analyses based on Hill numbers, using OTU/ASV tables and associated phylogenetic trees as inputs. The package includes functions for (phylo)diversity measurement, (phylo)diversity profile plotting, (phylo)diversity comparison between samples and groups, (phylo)diversity partitioning and (dis)similarity measurement. All of these grounded in abundance-based and incidence-based Hill numbers. The statistical framework developed around Hill numbers encompasses many of the most broadly employed diversity (e.g. richness, Shannon index, Simpson index), phylogenetic diversity (e.g. Faith's PD, Allen's H, Rao's quadratic entropy) and dissimilarity (e.g. Sorensen index, Unifrac distances) metrics. This enables the most common analyses of diversity to be performed while grounded in a single statistical framework. The methods are described in Jost et al. (2007) , Chao et al. (2010) and Chiu et al. (2014) ; and reviewed in the framework of molecularly characterised biological systems in Alberdi & Gilbert (2019) .
How to cite:
Antton Alberdi (2019). hilldiv: Integral Analysis of Diversity Based on Hill Numbers. R package version 1.5.1, https://cran.r-project.org/web/packages/hilldiv. Accessed 22 Dec. 2024.
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