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
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-prerel: MIIPW_0.1.1.zip, r-release: MIIPW_0.1.1.zip, r-oldrel: MIIPW_0.1.1.zip
macOS binaries:	r-prerel (arm64): MIIPW_0.1.1.tgz, r-release (arm64): MIIPW_0.1.1.tgz, r-oldrel (arm64): MIIPW_0.1.1.tgz, r-prerel (x86_64): MIIPW_0.1.1.tgz, r-release (x86_64): MIIPW_0.1.1.tgz
Old sources:	MIIPW archive

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