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: 0.3.4
Imports: glmnet (≥ 3.0.0), ggplot2 (≥ 2.2.1), Rdpack, Rcpp (≥ 0.12.15), Rglpk (≥ 0.6-1), stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, dplyr, tidyr, covr
Published: 2021-09-02
Author: Oystein Sorensen ORCID iD [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_0.3.4.tar.gz
Windows binaries: r-devel: hdme_0.3.4.zip, r-devel-UCRT: hdme_0.3.4.zip, r-release: hdme_0.3.4.zip, r-oldrel: hdme_0.3.4.zip
macOS binaries: r-release (arm64): hdme_0.3.4.tgz, r-release (x86_64): hdme_0.3.4.tgz, r-oldrel: hdme_0.3.4.tgz
Old sources: hdme archive

Linking:

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