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:	0.6.0
Imports:	glmnet (≥ 3.0.0), ggplot2 (≥ 2.2.1), Rdpack, Rcpp (≥ 0.12.15), Rglpk (≥ 0.6-1), rlang (≥ 1.0), stats
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, testthat, dplyr, tidyr, covr
Published:	2023-05-16
Author:	Oystein Sorensen [aut, cre]
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

Documentation:

Reference manual:	hdme.pdf
Vignettes:	The hdme package: regression methods for high-dimensional data with measurement error

Downloads:

Package source:	hdme_0.6.0.tar.gz
Windows binaries:	r-devel: hdme_0.6.0.zip, r-release: hdme_0.6.0.zip, r-oldrel: hdme_0.6.0.zip
macOS binaries:	r-release (arm64): hdme_0.6.0.tgz, r-oldrel (arm64): hdme_0.6.0.tgz, r-release (x86_64): hdme_0.6.0.tgz, r-oldrel (x86_64): hdme_0.6.0.tgz
Old sources:	hdme archive

Linking:

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