Learn from Training Data then Quickly Fill in Missing Data
Take a normalized variable and transform it back to a bounded variable...
Estimate covariance when data is missing
Use the pattern learned from the training data to impute (fill in good...
Take a variable bounded above/below/both and return an unbounded (norm...
Learn from the training data so that later you can fill in missing dat...
Convert columns of a dataframe from factors to character or numeric.
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.