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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
DOI: 10.32614/CRAN.package.conjurer
Author: Sidharth Macherla ORCID iD [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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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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