Provides simple functions to compute and plot two
types (sample-size- and coverage-based) rarefaction and extrapolation of species
diversity (Hill numbers) for individual-based (abundance) data or sampling-unit-
based (incidence) data. (Hsieh, Ma and Chao 2014) <doi: 10.1111/2041-210X.12613>.

c(person("T. C.", "Hsieh", role = c("aut", "cre", "cph"), email =
"euler96@gmail.com"), person("K. H.", "Ma", role = "ctb", email =
"smallmar123@gmail.com"), person("Anne", "Chao", role = "ths", email =
"chao@stat.nthu.edu.tws"))
Interpolation and extrapolation for species diversity: a unified approach for both individual-based (abundance) data and sample-based (incidence) data.
T. C. Hsieh [aut, cre, cph],
K. H. Ma [ctb],
Anne Chao [ths]

Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation of species diversity (Hill numbers) for individual-based (abundance) data or sampling-unit-based (incidence) data.

Provides simple functions to compute and plot two types
(sample-size- and coverage-based) rarefaction and extrapolation curves for species
diversity (Hill numbers) based on individual-based abundance data or sampling-unit-
based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent
theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution)
for an introduction of the R package.
stats, graphics, ggplot2, reshape2, Rcpp

Integrative Network Omnibus Total Effect Test
Su H. Chu <su_chu@brown.edu>
Yen-Tsung Huang <ythuang@stat.sinica.edu.tw>
Integrated joint analysis of multiple platform genomic data across biological gene sets or pathways using powerful variance-component based testing procedures.

Tools for Exploring Multiple Response Data
c(
person("Tom", "Elliott", role = c("aut", "cre"), email = "tom.elliott@auckland.ac.nz", comment = c(ORCID = "0000-0002-7815-6318")),
person("Junjie", "Zeng", role = c("aut"), comment = "Original author"),
person("Simon", "Potter", role = c("aut"))
)
Interaction and analysis of multiple response data,
along with other tools for analysing these types of data including
missing value analysis and calculation of standard errors for
a range of covariance matrix results (proportions, multinomial,
independent samples, and multiple response).
Tom Elliott [aut, cre] (<https://orcid.org/0000-0002-7815-6318>),
Junjie Zeng [aut] (Original author),
Simon Potter [aut]