Package: hdme 0.6.0

Oystein Sorensen

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>).

Authors:Oystein Sorensen [aut, cre]

hdme_0.6.0.tar.gz
hdme_0.6.0.zip(r-4.7-x86_64)hdme_0.6.0.zip(r-4.7-arm64)hdme_0.6.0.zip(r-4.6-x86_64)hdme_0.6.0.zip(r-4.6-arm64)hdme_0.6.0.zip(r-4.5-x86_64)
hdme_0.6.0.tgz(r-4.6-x86_64)hdme_0.6.0.tgz(r-4.6-arm64)hdme_0.6.0.tgz(r-4.5-x86_64)hdme_0.6.0.tgz(r-4.5-arm64)
hdme_0.6.0.tar.gz(r-4.7-arm64)hdme_0.6.0.tar.gz(r-4.7-x86_64)hdme_0.6.0.tar.gz(r-4.6-arm64)hdme_0.6.0.tar.gz(r-4.6-x86_64)
hdme_0.6.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hdme/json (API)

# Install 'hdme' in R:

install.packages('hdme', repos = c('https://osorensen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/osorensen/hdme/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

5.12 score 9 stars 29 scripts 354 downloads 7 exports 32 dependencies

Last updated from:610d1c9c14. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	137
linux-devel-x86_64	OK	168
source / vignettes	OK	234
linux-release-arm64	OK	133
linux-release-x86_64	OK	157
macos-release-arm64	OK	186
macos-release-x86_64	OK	361
macos-oldrel-arm64	OK	191
macos-oldrel-x86_64	OK	330
windows-devel-arm64	OK	387
windows-devel-x86_64	OK	174
windows-release-arm64	OK	393
windows-release-x86_64	OK	151
windows-oldrel-x86_64	OK	140
wasm-release	OK	124

Exports:corrected_lasso cv_corrected_lasso cv_gds gds gmu_lasso gmus mus

Dependencies:cli codetools cpp11 farver foreach ggplot2 glmnet glue gtable isoband iterators labeling lattice lifecycle Matrix R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack Rglpk rlang S7 scales shape slam survival vctrs viridisLite withr

The hdme package: regression methods for high-dimensional data with measurement error

Rendered fromhdme.Rmdusingknitr::rmarkdown

Last update: 2023-05-16
Started: 2019-04-12

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Extract Coefficients of a Corrected Lasso object	coef.corrected_lasso
Extract Coefficients of a Generalized Dantzig Selector Object	coef.gds
Extract Coefficients of a GMU Lasso object	coef.gmu_lasso
Extract Coefficients of a GMUS object	coef.gmus
Corrected Lasso	corrected_lasso
Cross-validated Corrected lasso	cv_corrected_lasso
Cross-Validated Generalized Dantzig Selector	cv_gds
Generalized Dantzig Selector	gds
Generalized Matrix Uncertainty Lasso	gmu_lasso
Generalized Matrix Uncertainty Selector	gmus
Matrix Uncertainty Selector	mus
plot.corrected_lasso	plot.corrected_lasso
plot.cv_corrected_lasso	plot.cv_corrected_lasso
plot.cv_gds	plot.cv_gds
Plot the estimates returned by gds	plot.gds
Plot the estimates returned by gmu_lasso	plot.gmu_lasso
Plot the estimates returned by gmus and mus	plot.gmus
Print a Corrected Lasso object	print.corrected_lasso
Print a Cross-Validated Corrected Lasso object	print.cv_corrected_lasso
Print a Cross-Validated GDS Object	print.cv_gds
Print a Generalized Dantzig Selector Object	print.gds
Print a GMU Lasso object	print.gmu_lasso
Print a GMUS object	print.gmus