conjurer: A Parametric Method for Generating Synthetic Data (original) (raw)
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 |
BugReports: | https://github.com/SidharthMacherla/conjurer/issues |
License: | MIT + file |
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:
Please use the canonical formhttps://CRAN.R-project.org/package=conjurerto link to this page.