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:	1.6.1
Depends:	R (≥ 3.1.2)
Imports:	MASS, survival, VGAM, stats, rlang, checkmate, abind
Suggests:	knitr, rmarkdown, mitools, ggplot2
Published:	2022-01-18
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_1.6.1.tar.gz
Windows binaries:	r-devel: smcfcs_1.6.1.zip, r-release: smcfcs_1.6.1.zip, r-oldrel: smcfcs_1.6.1.zip
macOS binaries:	r-release (arm64): smcfcs_1.6.1.tgz, r-oldrel (arm64): smcfcs_1.6.1.tgz, r-release (x86_64): smcfcs_1.6.1.tgz, r-oldrel (x86_64): smcfcs_1.6.1.tgz
Old sources:	smcfcs archive

Reverse dependencies:

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

Linking:

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