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: 2023-09-25 13:12:00 UTC; steve
Repository: CRAN
Date/Publication: 2023-09-25 15:10:02 UTC
Built: R 4.6.0; ; 2026-04-27 09:15:19 UTC; unix
