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. 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.

Version: 1.2
Suggests: testthat
Published: 2013-11-25
Author: Stephen R. Haptonstahl
Maintainer: Stephen R. Haptonstahl <srh at haptonstahl.org>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: FastImputation citation info
CRAN checks: FastImputation results

Downloads:

Reference manual: FastImputation.pdf
Package source: FastImputation_1.2.tar.gz
Windows binaries: r-devel: FastImputation_1.2.zip, r-release: FastImputation_1.2.zip, r-oldrel: FastImputation_1.2.zip
OS X Snow Leopard binaries: r-release: FastImputation_1.2.tgz, r-oldrel: FastImputation_1.2.tgz
OS X Mavericks binaries: r-release: FastImputation_1.2.tgz
Old sources: FastImputation archive