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>).">

hdme: High-Dimensional Regression with Measurement Error (original) (raw)

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
DOI: 10.32614/CRAN.package.hdme
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:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=hdmeto link to this page.