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. The
FastImputation function uses this FastImputationPatterns object
to impute (make a good guess at) missing data in a single line
or a whole dataframe of data.  This approximates the process
used by Amelia (\url{http://gking.harvard.edu/amelia/}) but is
much faster when filling in values for a single line of data.
'FastImputation.R' 'TrainFastImputation.R' 'UnfactorColumns.R'
'BoundNormalizedVariable.R' 'NormalizeBoundedVariable.R'
'LimitToSet.R'

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. The
FastImputation function uses this FastImputationPatterns object
to impute (make a good guess at) missing data in a single line
or a whole dataframe of data.  This approximates the process
used by Amelia [http://gking.harvard.edu/amelia/] but is much
faster when filling in values for a single line of 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. The FastImputation
function uses this FastImputationPatterns object to impute (make
a good guess at) missing data in a single line or a whole dataframe
of data.  This approximates the process used by Amelia
[http://gking.harvard.edu/amelia/] but is much faster when
filling in values for a single line of data.
'FastImputation.R' 'TrainFastImputation.R' 'UnfactorColumns.R'
'BoundNormalizedVariable.R' 'NormalizeBoundedVariable.R'
'LimitToSet.R' 'CovarianceWithMissing.R'

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
[http://gking.harvard.edu/amelia/] but is much faster when
filling in values for a single line of data.