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 |
| DOI: |
10.32614/CRAN.package.hdme |
| 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:
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