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MIIPW: IPW and Mean Score Methods for Time-Course Missing Data

Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.

Version: 0.1.1
Depends: R (≥ 2.10)
Imports: mice, Matrix, MASS
Suggests: knitr, rmarkdown
Published: 2023-02-13
DOI: 10.32614/CRAN.package.MIIPW
Author: Atanu Bhattacharjee [aut, cre, ctb], Bhrigu Kumar Rajbongshi [aut, ctb], Gajendra K Vishwakarma [aut, ctb]
Maintainer: Atanu Bhattacharjee <atanustat at gmail.com>
License: GPL-3
NeedsCompilation: no
In views: MissingData
CRAN checks: MIIPW results

Documentation:

Reference manual: MIIPW.pdf
Vignettes: Introduction to MIIPW

Downloads:

Package source: MIIPW_0.1.1.tar.gz
Windows binaries: r-devel: MIIPW_0.1.1.zip, r-release: MIIPW_0.1.1.zip, r-oldrel: MIIPW_0.1.1.zip
macOS binaries: r-release (arm64): MIIPW_0.1.1.tgz, r-oldrel (arm64): MIIPW_0.1.1.tgz, r-release (x86_64): MIIPW_0.1.1.tgz, r-oldrel (x86_64): MIIPW_0.1.1.tgz
Old sources: MIIPW archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=MIIPW 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.