Package: LRMiss 0.0.1

Benedict Risebrow

LRMiss: Linear Regression with Missing Data

Provides methods for linear regression in the presence of missing data, including missingness in covariates and responses. The package implements two estimators: 'oss_estimator', a low-dimensional semi-supervised method, and 'dantzig_missing', a high-dimensional approach. The tuning parameter can be selected automatically via 'cv_dantzig_missing'. See the associated methodology paper for details.

Authors:Benedict Risebrow [aut, cre], Thomas Berrett [aut]

LRMiss_0.0.1.tar.gz
LRMiss_0.0.1.zip(r-4.7)LRMiss_0.0.1.zip(r-4.6)LRMiss_0.0.1.zip(r-4.5)
LRMiss_0.0.1.tgz(r-4.6-any)LRMiss_0.0.1.tgz(r-4.5-any)
LRMiss_0.0.1.tar.gz(r-4.7-any)LRMiss_0.0.1.tar.gz(r-4.6-any)
LRMiss_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
LRMiss/json (API)

# Install 'LRMiss' in R:
install.packages('LRMiss', repos = c('https://benrisebrow.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/benrisebrow/lrmiss/issues

On CRAN:

Conda:

2.30 score 174 downloads 12 exports 17 dependencies

Last updated from:5ebaed63a4. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING117
source / vignettesOK164
linux-release-x86_64WARNING118
macos-release-arm64WARNING131
macos-oldrel-arm64WARNING131
windows-develWARNING88
windows-releaseWARNING77
windows-oldrelWARNING66
wasm-releaseOK97

Exports:compute_hatSigmacv_dantzig_missingdantzig_missingdantzig_missing_standardiseddantzig_missing_unstandardisedestimate_cov_rawoss_estimatoross_estimator_coreOSS_estimator_Crossfitprecompute_oss_metaprecompute_oss_meta_crossfitprecompute_oss_meta_from_formula

Dependencies:clidata.tablefastDummiesgluelifecyclemagrittrMASSpillarpkgconfigRglpkrlangslamstringistringrtibbleutf8vctrs