R Individual Specialization (RInSp) package
Dr. Nicola ZACCARELLI, Dr. Giorgio MANCINELLI, and Prof. Dan BOLNICK
The package implements several ecological niche metrics to measure individual specialization. Monte Carlo resampling tools are available for hypothesis testing against multinomial models.

Dr. Nicola Zaccarelli, Dr. Giorgio Mancinelli, and Prof. Dan Bolnick
Functions to calculate several ecological indices of individual and population niche width
(Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's
WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be
quantified by counts of categories, measures of mass/lenght or proportions. Monte Carlo resampling
procedures are available for hypothesis testing against multinomial null models.

c(person("Dr. Nicola", "Zaccarelli", role = c("aut", "cre"),
email = "nicola.zaccarelli@gmail.com"),
person("Giorgio", "Mancinelli", role = "aut",
email = "giorgio.mancinelli@unisalento.it"),
person("Dan", "Bolnick", role = "ctb",
email = "daniel.bolnick@uconn.edu"))
Dr. Nicola Zaccarelli [aut, cre],
Giorgio Mancinelli [aut],
Dan Bolnick [ctb]
Functions to calculate several ecological indices of individual
and population niche width (Araujo's E, clustering and pairwise similarity
among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess
individual specialization based on data of resource use. Resource use can
be quantified by counts of categories, measures of mass or length, or
proportions. Monte Carlo resampling procedures are available for hypothesis
testing against multinomial null models.

Functions to calculate several ecological indices of individual
and population niche width (Araujo's E, clustering and pairwise similarity
among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess
individual specialization based on data of resource use. Resource use can
be quantified by counts of categories, measures of mass or length, or
proportions. Monte Carlo resampling procedures are available for hypothesis
testing against multinomial null models.
Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079>
and associated references.