c(person(given="Mengyang",family="Gu",role=c("aut","cre"),email="mengyang@pstat.ucsb.edu"),
person(given="Jesus",family="Palomo", role=c("aut"),email="jesus.palomo@urjc.es"),
person(given="James",family="Berger", role="aut"))

c(person(given="Mengyang",family="Gu",role=c("aut","cre"),
email="mengyang@pstat.ucsb.edu"),
person(given="Jesus",family="Palomo", role=c("aut"),
email="jesus.palomo@urjc.es"),
person(given="James",family="Berger", role="aut"))
Robust parameter estimation and prediction of Gaussian stochastic
process emulators. It allows for robust parameter estimation and prediction using
Gaussian stochastic process emulator. It also implements the parallel partial
Gaussian stochastic process emulator for computer model with massive outputs
See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics;
Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

Robust Instrumental Variable Methods in Linear Models
c(person(given = "Taehyeon",
family = "Koo",
role = "aut"),
person(given = "Zhenyu",
family = "Wang",
role = "aut"),
person(given = "Hyunseung",
family = "Kang",
role = "aut"),
person(given = "Dylan",
family = "Small",
role = "aut"),
person(given = "Zijian",
family = "Guo",
role = c("aut","cre"),
email = "zijguo@stat.rutgers.edu"))
Inference for the treatment effect with possibly invalid instrumental variables via TSHT('Guo et al.' (2016) <arXiv:1603.05224>) and SearchingSampling('Guo' (2021) <arXiv:2104.06911>), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2016) <arXiv:1609.06713>).
glmnet, MASS, Matrix, igraph, intervals, CVXR
Taehyeon Koo [aut],
Zhenyu Wang [aut],
Hyunseung Kang [aut],
Dylan Small [aut],
Zijian Guo [aut, cre]

Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) <doi:10.1007/978-94-011-2546-8_20>, Sen, P. K. (1968) <doi:10.1080/01621459.1968.10480934>.