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Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. This methodology is generic and therefore benefits both the academic and industrial research.
Version: | 1.7.1 |
Depends: | R (≥ 2.10) |
Imports: | jsonlite (≥ 1.8.0), httr (≥ 1.4.2), methods |
Suggests: | knitr, rmarkdown |
Published: | 2023-01-18 |
DOI: | 10.32614/CRAN.package.conjurer |
Author: | Sidharth Macherla [aut, cre] |
Maintainer: | Sidharth Macherla <msidharthrasik at gmail.com> |
BugReports: | https://github.com/SidharthMacherla/conjurer/issues |
License: | MIT + file LICENSE |
URL: | https://www.foyi.co.nz/posts/documentation/documentationconjurer/ |
NeedsCompilation: | no |
Citation: | conjurer citation info |
Materials: | NEWS |
CRAN checks: | conjurer results |
Reference manual: | conjurer.pdf |
Vignettes: |
Industry Example Introduction to conjurer |
Package source: | conjurer_1.7.1.tar.gz |
Windows binaries: | r-devel: conjurer_1.7.1.zip, r-release: conjurer_1.7.1.zip, r-oldrel: conjurer_1.7.1.zip |
macOS binaries: | r-release (arm64): conjurer_1.7.1.tgz, r-oldrel (arm64): conjurer_1.7.1.tgz, r-release (x86_64): conjurer_1.7.1.tgz, r-oldrel (x86_64): conjurer_1.7.1.tgz |
Old sources: | conjurer archive |
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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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