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StackImpute: Tools for Analysis of Stacked Multiple Imputations

Provides methods for inference using stacked multiple imputations augmented with weights. The vignette provides example R code for implementation in general multiple imputation settings. For additional details about the estimation algorithm, we refer the reader to Beesley, Lauren J and Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple imputation incorporating the substantive model” <doi:10.1111/biom.13372>, and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random missingness through imputation stacking” <doi:10.48550/arXiv.2101.07954>.

Version: 0.1.0
Depends: R (≥ 3.6.0)
Imports: sandwich, zoo, mice, dplyr, MASS, magrittr, boot
Suggests: knitr, rmarkdown
Published: 2021-09-10
Author: Lauren Beesley [aut], Mike Kleinsasser [cre]
Maintainer: Mike Kleinsasser <mkleinsa at umich.edu>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: StackImpute results

Documentation:

Reference manual: StackImpute.pdf
Vignettes: UsingStackImpute

Downloads:

Package source: StackImpute_0.1.0.tar.gz
Windows binaries: r-devel: StackImpute_0.1.0.zip, r-release: StackImpute_0.1.0.zip, r-oldrel: StackImpute_0.1.0.zip
macOS binaries: r-release (arm64): StackImpute_0.1.0.tgz, r-oldrel (arm64): StackImpute_0.1.0.tgz, r-release (x86_64): StackImpute_0.1.0.tgz, r-oldrel (x86_64): StackImpute_0.1.0.tgz

Reverse dependencies:

Reverse imports: SynDI

Linking:

Please use the canonical form https://CRAN.R-project.org/package=StackImpute to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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