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 | 
| 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.html , conjurer.pdf | 
| Vignettes: | Industry Example (source, R code) Introduction to conjurer (source, R code) | 
| 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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