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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 
[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 |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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