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conjurer (version 1.7.0)

A Parametric Method for Generating Synthetic Data

Description

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.

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Version

Install

install.packages('conjurer')

Monthly Downloads

292

Version

1.7.0

License

MIT + file LICENSE

Maintainer

Sidharth Macherla

Last Published

January 18th, 2023

Functions in conjurer (1.7.0)

Generate Frequency Distribution Matrix

Extracts the First Two Alphabets of the String

Build Spikes in the Data Distribution

Build Transaction Data

Generate complete m-ary connected graph

Generate a pattern

genIndepDepJson

Generate Body for the POST Function of Uncovr

Build a pattern

Build Product Data

Extract Dataframe from uncovr API Response

Extracts Three Consecutive Alphabets of the String

missingArgHandler

Handle Missing Arguments in Function

Generate Next Alphabet

A supporting function.

POST Function for Calling uncovr API

Build Outliers in Data Distribution

Map Factors Based on Pareto Arguments

Build a Unique Customer Identifier

Build Data Distribution

Build Dynamic Strings

Generate Synthetic Data using uncovr API

Build Numeric Data

Generate hierarchical data

Build identifier