c(person("Andrew", "Gelman", role = "aut", email = "gelman@stat.columbia.edu"),
person("Yu-Sung", "Su", role =  c("aut", "cre"), email = "suyusung@tsinghua.edu.cn"),
person("Masanao", "Yajima", role =  "ctb", email = "yajima@stat.ucla.edu"),
person("Jennifer", "Hill", role =  "ctb", email = "jennifer.hill@nyu.edu"),
person("Maria Grazia", "Pittau", role =  "ctb", email = "grazia@stat.columbia.edu"),
person("Jouni", "Kerman", role =  "ctb", email = "jouni@kerman.com"),
person("Tian", "Zheng", role =  "ctb", email = "tzheng@stat.columbia.edu"),
person("Vicent", "Dorie", role =  "ctb", email = "vjd2106@columbia.edu")
)
Andrew Gelman [aut],
Yu-Sung Su [aut, cre],
Masanao Yajima [ctb],
Jennifer Hill [ctb],
Maria Grazia Pittau [ctb],
Jouni Kerman [ctb],
Tian Zheng [ctb] and
Vicent Dorie [ctb]
R functions for processing lm, glm, svy.glm, merMod and polr outputs.

c(person("Andrew", "Gelman", role = "aut", email = "gelman@stat.columbia.edu"),
person("Yu-Sung", "Su", role =  c("aut", "cre"), email = "suyusung@tsinghua.edu.cn"),
person("Masanao", "Yajima", role =  "ctb", email = "yajima@stat.ucla.edu"),
person("Jennifer", "Hill", role =  "ctb", email = "jennifer.hill@nyu.edu"),
person("Maria Grazia", "Pittau", role =  "ctb", email = "grazia@stat.columbia.edu"),
person("Jouni", "Kerman", role =  "ctb", email = "jouni@kerman.com"),
person("Tian", "Zheng", role =  "ctb", email = "tzheng@stat.columbia.edu"),
person("Vicent", "Dorie", role =  "ctb", email = "vjd4@nyu.edu")
)
Andrew Gelman [aut],
Yu-Sung Su [aut, cre],
Masanao Yajima [ctb],
Jennifer Hill [ctb],
Maria Grazia Pittau [ctb],
Jouni Kerman [ctb],
Tian Zheng [ctb],
Vicent Dorie [ctb]

R functions for processing 'lm', 'glm', 'svy.glm', 'merMod' and 'polr' outputs.

A Statistical Methodology to Select Covariates in
High-Dimensional Data under Dependence
c(person("Aurelie", "Gueudin", email = "aurelie.gueudin@univ-lorraine.fr", role = c("aut", "cre")),
person("Anne", "Gegout-Petit", email = "anne.gegout-petit@univ-lorraine.fr", role = c("aut")))
Two steps variable selection procedure in a context of high-dimensional dependent data
but few observations. First step is dedicated to eliminate dependence between variables (clustering
of variables, followed by factor analysis inside each cluster).
Second step is a variable selection using by aggregation of adapted methods.
Bastien B., Chakir H., Gegout-Petit A., Muller-Gueudin A., Shi Y.
A statistical methodology to select covariates in high-dimensional data under dependence.
Application to the classification of genetic profiles associated with outcome of a non-small-cell
lung cancer treatment. 2018. <https://hal.archives-ouvertes.fr/hal-01939694>.
stats, mvtnorm, ClustOfVar, FAMT, graphics, VSURF, glmnet,
anapuce, qvalue, parallel, doParallel, impute, ComplexHeatmap,
circlize
Aurelie Gueudin [aut, cre],
Anne Gegout-Petit [aut]