Authors@R: c(person("Cody L","Marquart", role = c("aut", "cre"), email="cody.marquart@wisc.edu"),person("Zachari","Swiecki", role = c("aut"), email="swiecki@wisc.edu"),person("Brendan","Eagan", role = c("aut"), email="beagan@wisc.edu"),person("David", "Williamson Shaffer", role = c("aut"), email = "dws@education.wisc.edu"))
Packaged: 2018-08-17 19:07:28 UTC; clmarquart

Packaged: 2018-08-29 14:10:39 UTC; clmarquart

Author: Cody L Marquart [aut, cre] (<https://orcid.org/0000-0002-3387-6792>),
Authors@R: c(person("Cody L","Marquart", role = c("aut", "cre"), email="cody.marquart@wisc.edu", comment = c(ORCID = "0000-0002-3387-6792")),person("Zachari","Swiecki", role = c("aut"), email="swiecki@wisc.edu"),person("Brendan","Eagan", role = c("aut"), email="beagan@wisc.edu"),person("David", "Williamson Shaffer", role = c("aut"), email = "dws@education.wisc.edu"))
Suggests: testthat, magrittr, knitr, rmarkdown

Author: Peter J. Rousseeuw, Andreas Christmann <christmann@statistik.uni-dortmund.de>
regression depth method (RDM) to binary regression to approximate
the minimum number of observations that can be removed such that
the reduced data set has complete separation.

Author: Peter J. Rousseeuw, Andreas Christmann
Description: The library contains R-functions to perform the regression
depth method (RDM) to binary regression to approximate the
minimum number of observations that can be removed such that
Packaged: Fri Dec 10 19:17:41 2004; hornik

Title: Nonconvex Penalized Estimation for Generalized Linear Models
Authors@R: c(person("Dongshin", "Kim", email = "dongshin.kim@live.com",role = c("aut", "cre", "cph")),
person("Sunghoon", "Kwon", email = "shkwon0522@gmail.com",role = c("aut", "cph")),
person("Sangin", "Lee", email = "sanginlee44@gmail.com",role = c("aut", "cph")))
Description: An efficient unified algorithm for estimating the nonconvex penalized
linear, logistic and Poisson regression models. The unified algorithm is implemented
based on the convex concave procedure and
the algorithm can be applied to most of the existing nonconvex penalties.
The algorithm also supports convex penalty:
least absolute shrinkage and selection operator (LASSO).
smoothly clipped absolute deviation (SCAD),
minimax concave penalty (MCP), truncated LASSO penalty (TLP),
clipped LASSO (CLASSO), sparse ridge (SRIDGE),
modified bridge (MBRIDGE) and modified log (MLOG).
For a data set with many variables (high-dimensional data),
the algorithm selects relevant variables producing a parsimonious regression model.
(This project is funded by Julian Virtue Professorship from Center for Applied Research at
Graziadio School of Business and Management at Pepperdine University.)