bit, ggsn, ggmap, ggplot2, CircStats, circular, GeoLight,
fields, maptools, methods, mgcv, nlme, parallel, RcppArmadillo,
rgdal, rgeos, sp, truncnorm

bit, ggsn, ggmap, ggplot2, CircStats, circular, fields,
maptools, methods, mgcv, nlme, parallel, RcppArmadillo, rgdal,
rgeos, sp, truncnorm

c(person("Barry L.", "Nelson", role = c("aut"), email = "nelsonb@northwestern.edu"),
person("Xi", "Jiang", role = c("aut","cre"), email = "xijiang2020@u.northwestern.edu"))
Barry L. Nelson [aut],
Xi Jiang [aut, cre]
Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.
fitdistrplus, STAR, EnvStats, extraDistr, MASS, quadprog

Factorial Experiments with Minimum Level Changes
c(
person("Shwetank", "Lall", email = "shwetanklall@gmail.com",
role = c("aut", "cre")),
person("Arpan", "Bhowmik", email = "arpan.stat@gmail.com ",
role = "ctb"),
person("Eldho", "Varghese", email = "eldhoiasri@gmail.com",
role = "aut"),
person("Seema", "Jaggi", email = "seema@iasri.res.in ",
role = "ctb"),
person("Cini", "Varghese", email = "cini_v@iasri.res.in ",
role = "ctb")
)
Generate cost effective minimally changed run sequences
for symmetrical as well as asymmetrical factorial
designs.
Shwetank Lall [aut, cre],
Arpan Bhowmik [ctb],
Eldho Varghese [aut],
Seema Jaggi [ctb],
Cini Varghese [ctb]

Efficient Estimation of Clustered Current Status Data
c(person("Tong", "Wang", role = c("aut", "cre"),
email = "tong@stat.tamu.edu"),
person("Kejun", "He", role = "aut"),
person("Wei", "Ma", role = "aut"),
person("Dipankar", "Bandyopadhyay", role="aut"),
person("Samiran", "Sinha", role = "aut"))
Tong Wang [aut, cre],
Kejun He [aut],
Wei Ma [aut],
Dipankar Bandyopadhyay [aut],
Samiran Sinha [aut]
Current status data abounds
in the field of epidemiology and public health, where the only observable data for a subject is
the random inspection time and the event status at inspection. Motivated by such a current status
data from a periodontal study where data are inherently clustered,
we propose a unified methodology to analyze such complex data.