Multivariate Multinomial Distribution Approximation and
Imputation for Incomplete Categorical Data
A method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.

Detecting Differentially Variable Genes Using the Mixture of
Marginal Distributions
c(
person("Xuan", "Li", role=c("aut", "ctb"),
email = "lixuan0759@gmail.com"),
person("Yuejiao", "Fu", role=c("aut", "ctb"),
email = "yuejiao@mathstat.yorku.ca"),
person("Xiaogang", "Wang", role = c("aut", "ctb"),
email = "stevenw@mathstat.yorku.ca"),
person("Dawn L.", "DeMeo", role = c("aut", "ctb"),
email = "redld@channing.harvard.edu"),
person("Kelan", "Tantisira", role = c("aut", "ctb"),
email = "rekgt@channing.harvard.edu"),
person("Scott T.", "Weiss", role = c("aut", "ctb"),
email = "restw@channing.harvard.edu"),
person("Weiliang", "Qiu", role=c("aut", "cre"),
email = "weiliang.qiu@gmail.com")
)
Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Xuan Li [aut, ctb],
Yuejiao Fu [aut, ctb],
Xiaogang Wang [aut, ctb],
Dawn L. DeMeo [aut, ctb],
Kelan Tantisira [aut, ctb],
Scott T. Weiss [aut, ctb],
Weiliang Qiu [aut, cre]

This package implements the Mixture Model on Graphs
developed in Sanguinetti et al., Bioinformatics 2008.  The
graph structure is used to infer quantities that were not
measured, based on the neighbours' measurements.  This approach
was developed to address problems typical of Quantitative
Proteomics but could be applicable to many other domains.

Microbe-Metabolite Interactions-Based Metabolic Profiles
Predictor
c(person(given = "Wenli",
family = "Tang",
role = c("aut", "cre"),
email = "1071429394@qq.com",
comment = c(ORCID = "0000-0002-6141-5854")),
person(given = "Guangchuang", family = "Yu",
email = "guangchuangyu@gmail.com",
role  = c("aut", "ths"),
comment = c(ORCID = "0000-0002-6485-8781")))
Implements a computational framework to predict microbial community-based
metabolic profiles with 'O2PLS' model. It provides procedures of model
training and prediction. Paired microbiome and metabolome data are needed
for modeling, and the trained model can be applied to predict metabolites
of analogous environments using new microbial feature abundances.
magrittr (>= 2.0.1), OmicsPLS (>= 2.0.2), utils, stats
rmarkdown, knitr, prettydoc, testthat (>= 3.0.0)