c(person("Haotian", "Zheng", role = c("aut"),
email = "ht-zh14@mails.tsighua.edu.cn"),
person("Xiang", "Zhan", role = "aut"),
person("Anna", "Plantinga", role = "aut"),
person("Michael", "Wu", role = "aut"),
person("Ni", "Zhao", role = c("aut", "cre"), email = "nzhao10@jhu.edu"))
Haotian Zheng [aut], Xiang Zhan [aut], Anna Plantinga [aut], Michael Wu [aut], Ni Zhao [aut, cre]

c(person("Anna", "Plantinga", role = c("aut", "cre"), email = "amp9@williams.edu"),
person("Nehemiah", "Wilson", role = c("aut", "ctb")),
person("Haotian", "Zheng", role = c("aut", "ctb")),
person("Xiang", "Zhan", role = c("aut", "ctb")),
person("Michael", "Wu", role = "aut"),
person("Ni", "Zhao", role = c("aut", "ctb")),
person("Jun", "Chen", role = "aut"))
Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels.
The phenotype can be univariate continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>),
survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>),
multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and
structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>).
The package can also use robust and quantile regression (Fu et al. (2020+), in preparation).
In each case, the microbiome community effect is modeled nonparametrically through a kernel function,
which can incorporate phylogenetic tree information.
MASS, CompQuadForm, quantreg, GUniFrac, PearsonDS, lme4,
Matrix, permute, mixtools, survival, ecodist, stats
knitr, propr, cluster, dirmult, vegan, rmarkdown, tidyverse,
kableExtra
Anna Plantinga [aut, cre],
Nehemiah Wilson [aut, ctb],
Haotian Zheng [aut, ctb],
Xiang Zhan [aut, ctb],
Michael Wu [aut],
Ni Zhao [aut, ctb],
Jun Chen [aut]

MASS, CompQuadForm, quantreg, GUniFrac, PearsonDS, lme4,
Matrix, permute, mixtools, survival, stats

c(person("Anna", "Plantinga", role = c("aut", "cre"), email = "amp9@williams.edu"),
person("Nehemiah", "Wilson", role = c("aut", "ctb")),
person("Haotian", "Zheng", role = c("aut", "ctb")),
person("Xiang", "Zhan", role = c("aut", "ctb")),
person("Michael", "Wu", role = "aut"),
person("Ni", "Zhao", role = c("aut", "ctb")),
person("Jun", "Chen", role = "aut"),
person("Hongjiao", "Liu", role = c("aut", "ctb")))
Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels.
The phenotype can be univariate continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>),
survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>),
multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and
structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>).
The package can also use robust and quantile regression (unpublished work).
In each case, the microbiome community effect is modeled nonparametrically through a kernel function,
which can incorporate phylogenetic tree information.
Anna Plantinga [aut, cre],
Nehemiah Wilson [aut, ctb],
Haotian Zheng [aut, ctb],
Xiang Zhan [aut, ctb],
Michael Wu [aut],
Ni Zhao [aut, ctb],
Jun Chen [aut],
Hongjiao Liu [aut, ctb]