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 |
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