Package: FastImputation
Type: Package
Title: Learn from Training Data then Quickly Fill in Missing Data
Version: 2.2.1
Date: 2023-09-25
Author: Stephen R. Haptonstahl
Maintainer: Stephen R. Haptonstahl <srh@haptonstahl.org>
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' <https://gking.harvard.edu/amelia>
        but is much faster when filling in values for a single line of
        data.
License: GPL (>= 2)
Depends: R (>= 4.0)
Collate: 'FastImputation.R' 'TrainFastImputation.R' 'UnfactorColumns.R'
        'BoundNormalizedVariable.R' 'NormalizeBoundedVariable.R'
        'CovarianceWithMissing.R' 'FI_train.R' 'FI_test.R' 'FI_true.R'
RoxygenNote: 7.2.3
Imports: methods, Matrix
Suggests: testthat, caret, e1071
NeedsCompilation: no
Encoding: UTF-8
Packaged: 2026-10-08 12:29:05 UTC; root
Repository: https://shaptonstahl.r-universe.dev
Date/Publication: 2023-09-25 16:36:24 UTC
RemoteUrl: https://github.com/cran/FastImputation
RemoteRef: HEAD
RemoteSha: 4279c96faf91dbc1b351a93ec5511c5f9b6040bc