conjurer: A Parametric Method for Generating Synthetic Data
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 |
| 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 |
Documentation:
| Reference manual: | conjurer.pdf |
| Vignettes: |
Industry Example Introduction to conjurer |
Downloads:
| 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 |
Linking:
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