Authors@R: c(person("Martin", "Spindler", email="martin.spindler@gmx.de", role=c("cre", "aut")), person("Victor", "Chernozhukov", role="aut"), person("Christian", "Hansen", role="aut"),  person("Philipp", "Bach", email = "philipp.bach@uni-hamburg.de", role="ctb"))
Suggests: testthat, knitr, xtable, mvtnorm

Title: High-Dimensional Regression with Measurement Error
Description: Penalized regression for generalized linear models for
measurement error problems (aka. errors-in-variables). The package
contains a version of the lasso (L1-penalization) which corrects
for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>).
It also contains an implementation of the Generalized Matrix Uncertainty
Selector, which is a version the (Generalized) Dantzig Selector for the
Suggests: knitr, rmarkdown, testthat, flare, igraph, tidyverse, zeallot

Suggests: knitr, rmarkdown, testthat, flare, igraph, dplyr, tidyr,
Packaged: 2018-03-30 16:35:46 UTC; oysteinsorensen

Imports: glmnet (>= 2.0-13), ggplot2 (>= 2.2.1), Rdpack, Rcpp (>=
Suggests: Rglpk (>= 0.6-1), lpSolveAPI (>= 5.5.2.0-17), knitr,
rmarkdown, testthat, flare, igraph, dplyr, tidyr, zeallot

Imports: glmnet (>= 2.0-13), ggplot2 (>= 2.2.1), Rdpack, Rglpk (>=
Suggests: knitr, rmarkdown, testthat, flare, dplyr, tidyr, covr

Imports: glmnet (>= 3.0.0), ggplot2 (>= 2.2.1), Rdpack, Rcpp (>=

Suggests: knitr, rmarkdown, testthat, dplyr, tidyr, covr

Title: Nomograms for High-Dimensional Cox Models
Author: Miaozhu Li <miaozhu.li@duke.edu>, Nan Xiao <nanx@uchicago.edu>
Description: Build nomograms for high-dimensional Cox models, with support for model validation and calibration.
Imports: survival, glmnet, rms, foreach, survAUC
Suggests: knitr, doParallel, Hmisc, mice

Imports: survival, glmnet, penalized, ncvreg, rms, foreach, survAUC

Description: Build nomograms for high-dimensional Cox models, with support for
reproducible model validation, calibration and comparison.
Imports: survival, glmnet, penalized, ncvreg, rms, foreach, survAUC,

reproducible model validation, calibration, and comparison.