mifa: Multiple Imputation for Exploratory Factor Analysis

Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.

Version: 0.2.0
Imports: stats, mice, dplyr, checkmate
Suggests: psych, testthat, knitr, rmarkdown, ggplot2, tidyr, covr
Published: 2021-01-22
Author: Vahid Nassiri [aut], Anikó Lovik [aut], Geert Molenberghs [aut], Geert Verbeke [aut], Tobias Busch ORCID iD [aut, cre]
Maintainer: Tobias Busch <teebusch at gmail.com>
BugReports: https://github.com/teebusch/mifa/issues
License: MIT + file LICENSE
URL: https://github.com/teebusch/mifa
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mifa results

Downloads:

Reference manual: mifa.pdf
Package source: mifa_0.2.0.tar.gz
Windows binaries: r-devel: mifa_0.2.0.zip, r-release: mifa_0.2.0.zip, r-oldrel: mifa_0.2.0.zip
macOS binaries: r-release (arm64): mifa_0.2.0.tgz, r-release (x86_64): mifa_0.2.0.tgz, r-oldrel: mifa_0.2.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mifa to link to this page.