missMethods: Methods for Missing Data

Supply functions for the creation and handling of missing data as well as tools to evaluate missing data methods. Nearly all possibilities of generating missing data discussed by Santos et. al (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are implemented. Functions are supplied to compare parameter estimates and imputed values to true values to evaluate missing data methods. Evaluations of these types are done, for example, by Cetin-Berber et al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005) <doi:10.1093/bioinformatics/bth499>.

Version: 0.2.0
Imports: stats
Suggests: ggplot2, knitr, lpSolve, mvtnorm, norm, rmarkdown, testthat (≥ 2.1.0), tibble
Published: 2020-07-30
Author: Tobias Rockel [aut, cre]
Maintainer: Tobias Rockel <Rockel.To at gmail.com>
BugReports: https://github.com/torockel/missMethods/issues
License: GPL-3
URL: https://github.com/torockel/missMethods
NeedsCompilation: no
Materials: README NEWS
CRAN checks: missMethods results

Documentation:

Reference manual: missMethods.pdf
Vignettes: Generating-missing-values

Downloads:

Package source: missMethods_0.2.0.tar.gz
Windows binaries: r-devel: missMethods_0.2.0.zip, r-release: missMethods_0.2.0.zip, r-oldrel: missMethods_0.2.0.zip
macOS binaries: r-release (arm64): missMethods_0.2.0.tgz, r-release (x86_64): missMethods_0.2.0.tgz, r-oldrel: missMethods_0.2.0.tgz
Old sources: missMethods archive

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