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Provides a test bench for the comparison of missing data imputation methods in uni-variate time series. Imputation methods are compared using different error metrics. Proposed imputation methods and alternative error metrics can be used.
| Version: | 3.0.3 |
| Imports: | dplyr, forecast, ggplot2, imputeTS, reshape2, stats, tidyr, zoo |
| Suggests: | knitr, rmarkdown, magrittr |
| Published: | 2019-07-05 |
| DOI: | 10.32614/CRAN.package.imputeTestbench |
| Author: | Neeraj Bokde [aut], Marcus W. Beck [cre, aut] |
| Maintainer: | Marcus W. Beck <mbafs2012 at gmail.com> |
| BugReports: | https://github.com/neerajdhanraj/imputeTestbench/issues |
| License: | CC0 |
| NeedsCompilation: | no |
| Citation: | imputeTestbench citation info |
| In views: | MissingData, TimeSeries |
| CRAN checks: | imputeTestbench results |
| Reference manual: | imputeTestbench.pdf |
| Package source: | imputeTestbench_3.0.3.tar.gz |
| Windows binaries: | r-devel: imputeTestbench_3.0.3.zip, r-release: imputeTestbench_3.0.3.zip, r-oldrel: imputeTestbench_3.0.3.zip |
| macOS binaries: | r-release (arm64): imputeTestbench_3.0.3.tgz, r-oldrel (arm64): imputeTestbench_3.0.3.tgz, r-release (x86_64): imputeTestbench_3.0.3.tgz, r-oldrel (x86_64): imputeTestbench_3.0.3.tgz |
| Old sources: | imputeTestbench archive |
| Reverse imports: | cleanTS, ForecastTB |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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