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FastImputation: 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' <https://gking.harvard.edu/amelia> but is much faster when filling in values for a single line of data.
Getting started
Browse package contents
Package details |
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| Author | Stephen R. Haptonstahl |
| Maintainer | Stephen R. Haptonstahl <srh@haptonstahl.org> |
| License | GPL (>= 2) |
| Version | 2.2.1 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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