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: | 0.2.3 |
Imports: | glmnet (≥ 2.0-13), ggplot2 (≥ 2.2.1), Rdpack, Rglpk (≥ 0.6-1), Rcpp (≥ 0.12.15) |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | knitr, rmarkdown, testthat, flare, dplyr, tidyr, covr |
Published: | 2019-04-12 |
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_0.2.3.tar.gz |
Windows binaries: | r-devel: hdme_0.2.2.zip, r-release: hdme_0.2.2.zip, r-oldrel: hdme_0.2.2.zip |
OS X binaries: | r-release: hdme_0.2.2.tgz, r-oldrel: hdme_0.2.2.tgz |
Old sources: | hdme archive |
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