c(person("Kyoungjae","Lee",role="aut",email="leekjstat@gmail.com"),person("Lizhen", "Lin", role = "ctb", email="lizhen.lin@nd.edu"),person("Kisung", "You", role = c("aut", "cre"),email = "kyoustat@gmail.com",comment=c(ORCID="0000-0002-8584-459X")))

c(person("Kyoungjae","Lee",role="aut",email="leekjstat@gmail.com"),person("Lizhen", "Lin", role = "ctb", email="lizhen.lin@nd.edu"),person("Kisung", "You", role = c("aut", "cre"),email = "kisungyou@outlook.com",comment=c(ORCID="0000-0002-8584-459X")))

Mutation Analysis Toolkit for COVID-19 (Coronavirus Disease
2019)
c(
person(
"Shaoqian",
"Ma",
email = "shaoqianma@qq.com",
role = c("aut", "cre"),
comment = c(ORCID = '0000-0001-8950-0711')
),
person(
"Yongyou",
"Zhang",
email = "yongyouzhang@xmu.edu.cn",
role = c("aut"),
comment = c(ORCID = '0000-0003-2413-9106')
)
)
A feasible framework for mutation analysis and reverse transcription
polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including
mutation profile visualization, statistics and mutation ratio of each assay.
The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in
large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020)
<doi:10.20944/preprints202004.0529.v1>.
ggplot2, cowplot, seqinr, stringr, grDevices, graphics, utils,
ggpubr, dplyr, VennDiagram

Implements a pathwise algorithm for covariance selection

Bias-Corrected Sandwich Variance Estimators for Marginal Cox
Analysis of Cluster Randomized Trials
c(person("Xueqi","Wang", role = c("aut", "cre"),
email = "xueqi.wang@duke.edu"),
person("Elizabeth", "Turner", role = "aut",
email = "liz.turner@duke.edu"),
person("Fan", "Li", role = "aut",
email = "fan.f.li@yale.edu"))
The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).
Xueqi Wang [aut, cre],
Elizabeth Turner [aut],
Fan Li [aut]

Cox survival models by likelihood based boosting
This package provides routines for fitting Cox survival models by likelihood based boosting