README.md

In SidharthMacherla/conjurer: A Parametric Method for Generating Synthetic Data

Documentation

The official documentation for conjurer is at foyi

Feedback

Please share your feedback and feature requests by filling in this 2 question survey

:bell:If you are looking for an easy to use GUI for generating synthetic data please check out the app UnReal :tada:

Author

Sidharth Macherla

License

This project is licensed under the MIT License - see the LICENSE file for details.

Statement of Need

Data science applications need data to prototype and demonstrate to potential clients. For such purposes, using production data is a possibility. However, it is not always feasible due to legal and/or ethical considerations. This resulted in a need for generating synthetic data. This need is the key motivator for the package conjurer.

Data across multiple domains are known to exhibit some form of seasonality, cyclicality and trend. Although there are synthetic data generation packages currently available, they focus primarily on synthetic versions of microdata containing confidential information or for machine learning purposes. There is a need for a more generic synthetic data generation package that helps for multiple purposes such as forecasting, customer segmentation, insight generation etc. This package conjurer helps in generating such synthetic data.

Installation instructions

Firstly, install R from here

From the R console, install the package by using the following code

install.packages('conjurer')

Example usage

The package page on CRAN(Comprehensive R Archive Network) is here. The reference manual is here. The package vignette with the detailed documentation for usage with illustrative examples is here

Community guidelines

For guidelines regarding code contributions, refer to CONTRIBUTING. For guidelines on reporting security vulnerabilities, refer to SECURITY

SidharthMacherla/conjurer documentation built on April 23, 2023, 6:55 a.m.