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FastImputation
View on CRAN: Click
here
Download and install FastImputation package within the R console
Install from CRAN:
install.packages("FastImputation")
Install from Github:
library("remotes")
install_github("cran/FastImputation") Install by package version:
library("remotes")
install_version("FastImputation", "2.2.1") Attach the package and use:
library("FastImputation")
Maintained by
Stephen R. Haptonstahl
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-04-11
Latest Update: 2023-09-25
Description:
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'
but is much faster when
filling in values for a single line of data.
How to cite:
Stephen R. Haptonstahl (2012). FastImputation: Learn from Training Data then Quickly Fill in Missing Data. R package version 2.2.1, https://cran.r-project.org/web/packages/FastImputation. Accessed 05 Aug. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for FastImputation
Functions, R codes and Examples using
the FastImputation R package
Some associated functions: BoundNormalizedVariable . CovarianceWithMissing . FI_test . FI_train . FI_true . FastImputation . NormalizeBoundedVariable . TrainFastImputation . UnfactorColumns .
Some associated R codes: BoundNormalizedVariable.R . CovarianceWithMissing.R . FI_test.R . FI_train.R . FI_true.R . FastImputation.R . NormalizeBoundedVariable.R . TrainFastImputation.R . UnfactorColumns.R . Full FastImputation package functions and examples
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