smcfcs: Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification

Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.

Version: 1.6.0
Depends: R (≥ 3.1.2)
Imports: MASS, survival, VGAM, stats, rlang, checkmate, abind
Suggests: knitr, rmarkdown, mitools, ggplot2
Published: 2021-06-17
Author: Jonathan Bartlett [aut, cre], Ruth Keogh [aut], Edouard F. Bonneville [aut], Claus Thorn Ekstrøm [ctb]
Maintainer: Jonathan Bartlett <j.w.bartlett at bath.ac.uk>
License: GPL-3
URL: https://github.com/jwb133/smcfcs
NeedsCompilation: no
Materials: README
In views: MissingData
CRAN checks: smcfcs results

Documentation:

Reference manual: smcfcs.pdf
Vignettes: smcfcs
smcfcs_measerror

Downloads:

Package source: smcfcs_1.6.0.tar.gz
Windows binaries: r-devel: smcfcs_1.6.0.zip, r-release: smcfcs_1.6.0.zip, r-oldrel: smcfcs_1.6.0.zip
macOS binaries: r-release (arm64): smcfcs_1.6.0.tgz, r-release (x86_64): smcfcs_1.6.0.tgz, r-oldrel: smcfcs_1.6.0.tgz
Old sources: smcfcs archive

Reverse dependencies:

Reverse imports: bootImpute
Reverse suggests: Publish, riskRegression
Reverse enhances: mdmb

Linking:

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