Robust Estimation for Generalized Additive Models
Raymond K. W. Wong <raymondkww.dev@gmail.com>
This package provides robust estimation for generalized
additive models. It implements a fast and stable algorithm in
Wong, Yao and Lee (2012). The implementation also contains
three automatic selection methods for smoothing parameter. They
are designed to be robust to outliers. For more details, see
Wong, Yao and Lee (2012).

This package provides robust estimation for generalized
additive models. It implements a fast and stable algorithm in
Wong, Yao and Lee (2013). The implementation also contains
three automatic selection methods for smoothing parameter. They
are designed to be robust to outliers. For more details, see
Wong, Yao and Lee (2013).

Raymond K. W. Wong, Fang Yao and Thomas C. M. Lee

Robust Variable Selection with Exponential Squared Loss
Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures.
c(
person(given = "Jin", family = "Zhu", email = "zhuj37@mail2.sysu.edu.cn", role = c("cre", "aut"), comment = c(ORCID = "0000-0001-8550-5822")),
person(given = "Borui", family = "Tang", email = "tangborui@mail.ustc.edu.cn", role = c("aut")),
person(given = "Yunlu", family = "Jiang", email = "tjiangyl@jnu.edu.cn", role = c("aut")),
person(given = "Xueqin", family = "Wang", email = "wangxq20@ustc.edu.cn", role = c("aut"), comment = c(ORCID = "0000-0001-5205-9950")))
Jin Zhu [cre, aut] (<https://orcid.org/0000-0001-8550-5822>),
Borui Tang [aut],
Yunlu Jiang [aut],
Xueqin Wang [aut] (<https://orcid.org/0000-0001-5205-9950>)

Robust mixed effects modeling by robustification of
scoring equations using the Design Adaptive Scale approach.
nlme, methods, lme4, Matrix, MatrixModels, robustbase (>=
0.9.5), stats4
digest, ggplot2, reshape2, rootSolve, xtable,
'accessors.R' 'fromLme4.R' 'AllClass.R' 'AllGeneric.R'
'DAS-scale.R' 'fit.effects.R' 'helpers.R' 'lmer.R'
'multiroot.R' 'mutators.R' 'psiFunc2.R' 'rlmer.R'
'zero.theta.R'