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TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class 'FastImputationPatterns'. FastImputation() function uses this 'FastImputationPatterns' object to impute (make a good guess at) missing data in a single line or a whole data frame of data. This approximates the process used by 'Amelia' <https://gking.harvard.edu/amelia> but is much faster when filling in values for a single line of data.
| Version: | 2.2.1 |
| Depends: | R (≥ 4.0) |
| Imports: | methods, Matrix |
| Suggests: | testthat, caret, e1071 |
| Published: | 2023-09-25 |
| Author: | Stephen R. Haptonstahl |
| Maintainer: | Stephen R. Haptonstahl <srh at haptonstahl.org> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | FastImputation citation info |
| In views: | MissingData |
| CRAN checks: | FastImputation results |
| Reference manual: | FastImputation.pdf |
| Package source: | FastImputation_2.2.1.tar.gz |
| Windows binaries: | r-devel: FastImputation_2.2.1.zip, r-release: FastImputation_2.2.1.zip, r-oldrel: FastImputation_2.2.1.zip |
| macOS binaries: | r-release (arm64): FastImputation_2.2.1.tgz, r-oldrel (arm64): FastImputation_2.2.1.tgz, r-release (x86_64): FastImputation_2.2.1.tgz, r-oldrel (x86_64): FastImputation_2.2.1.tgz |
| Old sources: | FastImputation archive |
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