Package: BlockMissingData 0.1.0
Fei Xue
BlockMissingData: Integrating Multi-Source Block-Wise Missing Data in Model Selection
Model selection method with multiple block-wise imputation for block-wise missing data; see Xue, F., and Qu, A. (2021) <doi:10.1080/01621459.2020.1751176>.
Authors:
BlockMissingData_0.1.0.tar.gz
BlockMissingData_0.1.0.tar.gz(r-4.5-noble)BlockMissingData_0.1.0.tar.gz(r-4.4-noble)
BlockMissingData_0.1.0.tgz(r-4.4-emscripten)BlockMissingData_0.1.0.tgz(r-4.3-emscripten)
BlockMissingData.pdf |BlockMissingData.html✨
BlockMissingData/json (API)
| # Install 'BlockMissingData' in R: |
| install.packages('BlockMissingData', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:198910dbc2. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 10 2024 |
| R-4.5-linux | OK | Sep 10 2024 |
Exports:imputeglm.predictMBI
Dependencies:clicodetoolscpp11crayondoParallelforeachglmnetglmnetcrglueiteratorslatticelifecyclelobstrmagrittrMASSMatrixprettyunitspryrRcppRcppEigenrlangshapestringistringrsurvivalvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Imputation using generalized linear models for missing values | imputeglm.predict |
| Variable selection method with multiple block-wise imputation (MBI) | MBI |