Suggests: rms, qvcalc, glmmML, nnet, pscl, VGAM, gee, mlogit, Ecdat,
geepack, mgcv, rpart, party, ordinal, lme4, vcdExtra, glmnet,
mboost, class, e1071, flexmix, lqa, lpSolve, GAMBoost,
Author: Gerhard Tutz, Marcus Gross, Sarah Maierhofer, Gunther
Description: This R-package contains examples from the book "Regression
for Categorical Data", Tutz 2011, Cambridge University Press.
The names of the examples refer to the chapter and the data set
Packaged: 2012-02-03 10:29:32 UTC; schaubergerg

Author: Gunther Schauberger, Gerhard Tutz
Packaged: 2012-07-26 16:21:59 UTC; schaubergerg

Description: This R-package contains examples from the book "Regression for Categorical Data", Tutz 2011, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Packaged: 2014-11-11 14:06:31 UTC; schaubergerg

mboost, class, e1071, flexmix, lpSolve, GAMBoost, penalized
Description: This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.

Packaged: 2014-02-24 11:00:29 UTC; schaubergerg

Title: High Dimensional Factor Analysis and Confounder Adjusted Testing
Authors@R: c(person("Jingshu", "Wang", email = "jingshuw@stanford.edu", role = c("aut")),
person("Qingyuan", "Zhao", email = "qyzhao@stanford.edu", role = c("aut", "cre")))
Description: Provides several methods for factor analysis in high dimension (both n,p >> 1) and methods to adjust for possible confounders in multiple hypothesis testing.
Imports: MASS, esaBcv, ruv, sva, corpcor, leapp

Authors@R: c(person("Jingshu", "Wang", email = "jingshuw@galton.uchicago.edu", role = c("aut")),
person("Qingyuan", "Zhao", email = "qz280@cam.ac.uk", role = c("aut", "cre")))
Imports: Matrix, MASS, esaBcv, ruv, sva, corpcor, leapp

Description: Simple, fast, and automatic encodings for category data using
a data.table backend. Most of the methods are an implementation
of "Sufficient Representation for Categorical Variables" by
Johannemann, Hadad, Athey, Wager (2019) <arXiv:1908.09874>,
particularly their mean, sparse principal component analysis,
low rank representation, and multinomial logit encodings.