available, providing a GUI for the Livingston and Lewis approach for binary
classifications. For link, see documentation for the LL.CA() function.

Maintainer: Haakon Eidem Haakstad <h.t.haakstad@cemo.uio.no>

Maintainer: Haakon Eidem Haakstad <h.e.haakstad@gmail.com>
Four-parameter Beta and closely related distributions (i.e., the Gamma-
Binomial-, and Beta-Binomial distributions), including parameterization in
terms of moments, and fitting of Beta distributions to vectors of values.
Includes d/p/r- and a function and for calculating moments of Beta-Binomial
distributions. Also includes functions for estimating classification
accuracy, diagnostic performance, and consistency, making use of what is
generally known as the 'Livingston and Lewis approach' in the psychometric
literature which models observed-score distributions in terms of the Beta-
Binomial distribution. A shiny app is available, providing a GUI for the
Livingston and Lewis approach when used for binary classifications. For
url to the app, see documentation for the LL.CA() function.

Binomial-, and Beta-Binomial distributions). Includes d/p/q/r functions for
Four-Parameter Beta distributions and d/p/r- functions for Beta-Binomial
distributions. Includes moment-generating functions for Beta distributions,
Binomial distributions, Beta-Binomial distributions, and observed value
Includes functions for estimating classification accuracy, and consistency,
making use of the Classical Test-Theory based 'Livingston and Lewis' (L&L)
and 'Hanson and Brennan' approaches. A shiny app is available, providing a
GUI for the L&L approach when used for binary classifications. For url to
the app, see documentation for the LL.CA() function.

Title: Beta-Diversity Within a Metaweb of Species Interactions
Description: Measures of beta-diversity in networks, and easy visualization of why two networks are different.

Title: Beta-Diversity of Species Interactions
Description: Measures of beta-diversity in networks, and easy visualization of

Title: Partitioning beta diversity into turnover and nestedness
Description: betapart allows computing pair-wise dissimilarities
(distance matrices) and multiple-site dissimilarities,
separating the turnover and nestedness components of beta
diversity as described in Baselga (2010).