FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.

Multivariate Dependence with FDG Copulas
FDG copulas are a class of copulas featuring an interesting balance between flexibility and tractability. This package provides tools to construct, calculate the pairwise dependence coefficients of, simulate from, and fit FDG copulas. The acronym FDG stands for 'one-Factor with Durante Generators', as an FDG copula is a one-factor copula -- that is, the variables are independent given a latent factor -- whose linking copulas belong to the Durante class of bivariate copulas (also referred to as exchangeable Marshall-Olkin or semilinear copulas).

FDR-Control in Multiscale Change-Point Segmentation
c(person("Housen", "Li", role = c("aut", "cre"), email = "housen.li@outlook.com"),
person("Hannes", "Sieling", role = "aut", email = "hsielin@uni-goettingen.de"))
Housen Li [aut, cre],
Hannes Sieling [aut]
Estimate step functions via multiscale inference with controlled false discovery rate (FDR). For details see H. Li, A. Munk and H. Sieling (2016) <doi:10.1214/16-EJS1131>.

c(person("Housen", "Li", role = "aut", email = "housen.li@outlook.com"),
person("Hannes", "Sieling", role = "aut", email = "hsielin@uni-goettingen.de"),
person("Timo", "Aspelmeier", role = "cre", email = "timo.aspelmeier@mathematik.uni-goettingen.de"))
Housen Li [aut],
Hannes Sieling [aut],
Timo Aspelmeier [cre]

Estimate, Plot, and Summarize False Discovery Rates
c(
person("Megan", "Murray", email = "megan.c.hollister@vanderbilt.edu", role = c("aut", "cre")),
person("Jeffrey", "Blume", email = "j.blume@vanderbilt.edu", role = c("aut"))
)
Megan Murray [aut, cre],
Jeffrey Blume [aut]
The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines. Megan H. Murray and Jeffrey D. Blume (2020) <arXiv:2010.04680>.