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FastImputation (version 2.2.1)

Learn from Training Data then Quickly Fill in Missing Data

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.

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Version

Install

install.packages('FastImputation')

Monthly Downloads

279

Version

2.2.1

License

GPL (>= 2)

Maintainer

Stephen Haptonstahl

Last Published

September 25th, 2023

Functions in FastImputation (2.2.1)

UnfactorColumns

Convert columns of a dataframe from factors to character or numeric.
NormalizeBoundedVariable

Take a variable bounded above/below/both and return an unbounded (normalized) variable.
BoundNormalizedVariable

Take a normalized variable and transform it back to a bounded variable.
FI_true

Imputation "True" Data
TrainFastImputation

Learn from the training data so that later you can fill in missing data
CovarianceWithMissing

Estimate covariance when data is missing
FastImputation

Use the pattern learned from the training data to impute (fill in good guesses for) missing values.
FI_test

Imputation Test Data
FI_train

Imputation Training Data