car, caret, dplyr, gbm, ggplot2, glmnet, h2o, impute,
infotheo, limma, M3C, Metrics, MLmetrics, parallel, pathifier,
pathview, plyr, preprocessCore, pROC, RCy3, reticulate,
reshape, RWeka, scales, stats, stringr, survminer, survival

c(
person("Xinying", "Fang", email = "xinyingf@umich.edu", role = c("aut")),
person("Yu", "Liu", email = "uiluy@umich.edu", role = "aut"),
person("Zhijie", "Ren", email = "rzj@umich.edu", role = "aut"),
person("Fadhl", "Alakwaa", email = "falakwaa@hawaii.edu", role = "aut"),
person("Sijia", "Huang", email = "scarlettcanny@gmail.com", role = "aut"),
person("Lana", "Garmire", email = "lana.garmire.group@gmail.com", role = c("aut","cre")))

A comprehensive analysis tool for metabolomics data.  It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.

car, caret, dplyr, gbm, ggplot2, glmnet, h2o, impute,
infotheo, limma, M3C, Metrics, MLmetrics, parallel, princurve,
pathview, plyr, preprocessCore, pROC, RCy3, reticulate,
reshape, RWeka, scales, stats, stringr, survminer, survival

person(given = "Oleguer",
family = "Plana-Ripoll",
role = c("aut", "cre"),
email = "opr@econ.au.dk",
comment = c(ORCID = "0000-0002-6470-7465"))
Estimation of life expectancy and
Life Years Lost (LYL, or lillies for short) for a given population, for
example those with a given disease or condition. In addition, the package
can be used to compare estimates from different populations, or to estimate
confidence intervals. Technical details of the method are available in
Andersen (2017) <doi:10.1002/sim.7357>.
ddpcr, dplyr, knitr, pracma, progress, rlang, survival, tidyr,
utils
Oleguer Plana-Ripoll [aut, cre]
(<https://orcid.org/0000-0002-6470-7465>)