Package: FastImputation 2.2.1

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.

Authors:Stephen R. Haptonstahl

FastImputation_2.2.1.tar.gz
FastImputation_2.2.1.zip(r-4.5)FastImputation_2.2.1.zip(r-4.4)FastImputation_2.2.1.zip(r-4.3)
FastImputation_2.2.1.tgz(r-4.5-any)FastImputation_2.2.1.tgz(r-4.4-any)FastImputation_2.2.1.tgz(r-4.3-any)
FastImputation_2.2.1.tar.gz(r-4.5-noble)FastImputation_2.2.1.tar.gz(r-4.4-noble)
FastImputation_2.2.1.tgz(r-4.4-emscripten)FastImputation_2.2.1.tgz(r-4.3-emscripten)
FastImputation.pdf |FastImputation.html
FastImputation/json (API)

# Install 'FastImputation' in R:
install.packages('FastImputation', repos = c('https://shaptonstahl.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 5 scripts 239 downloads 6 exports 2 dependencies

Last updated 2 years agofrom:4279c96faf. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 21 2025
R-4.5-winOKMar 21 2025
R-4.5-macOKMar 21 2025
R-4.5-linuxOKMar 21 2025
R-4.4-winOKMar 21 2025
R-4.4-macOKMar 21 2025
R-4.4-linuxOKMar 21 2025
R-4.3-winOKMar 21 2025
R-4.3-macOKMar 21 2025

Exports:BoundNormalizedVariableCovarianceWithMissingFastImputationNormalizeBoundedVariableTrainFastImputationUnfactorColumns

Dependencies:latticeMatrix