FastImputation: Learn from Training Data then Quickly Fill in Missing Data

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
DOI:	10.32614/CRAN.package.FastImputation
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

Documentation:

Reference manual:

FastImputation.pdf

Downloads:

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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