hdme: High-Dimensional Regression with Measurement Error

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 case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).

Version: 0.2.0
Imports: glmnet (≥ 2.0-13), ggplot2 (≥ 2.2.1), Rdpack, Rcpp (≥ 0.12.15)
LinkingTo: Rcpp, RcppArmadillo
Suggests: Rglpk (≥ 0.6-1), lpSolveAPI (≥, knitr, rmarkdown, testthat, flare, igraph, dplyr, tidyr, zeallot
Published: 2018-05-19
Author: Oystein Sorensen
Maintainer: Oystein Sorensen <oystein.sorensen.1985 at gmail.com>
License: GPL-3
URL: https://github.com/osorensen/hdme
NeedsCompilation: yes
Citation: hdme citation info
Materials: README NEWS
CRAN checks: hdme results


Reference manual: hdme.pdf
Vignettes: The hdme package: regression methods for high-dimensional data with measurement error
Package source: hdme_0.2.0.tar.gz
Windows binaries: r-devel: hdme_0.2.0.zip, r-release: hdme_0.2.0.zip, r-oldrel: hdme_0.2.0.zip
OS X binaries: r-release: hdme_0.2.0.tgz, r-oldrel: hdme_0.2.0.tgz
Old sources: hdme archive


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