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conjurer (version 1.7.1)

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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Install

install.packages('conjurer')

Monthly Downloads

329

Version

1.7.1

License

MIT + file LICENSE

Maintainer

Sidharth Macherla

Last Published

January 18th, 2023

Functions in conjurer (1.7.1)

genMatrix

Generate Frequency Distribution Matrix
genPattern

Generate a pattern
buildPattern

Build a pattern
buildProd

Build Product Data
nextAlphaProb

Generate Next Alphabet
treeDf

A supporting function.
uncovrApi

POST Function for Calling uncovr API
genTriples

Extracts Three Consecutive Alphabets of the String
genTree

Generate complete m-ary connected graph
missingArgHandler

Handle Missing Arguments in Function
genTrans

Build Transaction Data
buildNum

Build Numeric Data
buildId

Build identifier
buildOutliers

Build Outliers in Data Distribution
buildHierarchy

Generate hierarchical data
buildPareto

Map Factors Based on Pareto Arguments
buildNames

Generate Names
buildModelData

Generate Synthetic Data using uncovr API
buildName

Build Dynamic Strings
buildCust

Build a Unique Customer Identifier
buildDistr

Build Data Distribution
buildSpike

Build Spikes in the Data Distribution
extractDf

Extract Dataframe from uncovr API Response
genIndepDepJson

Generate Body for the POST Function of Uncovr
genFirstPairs

Extracts the First Two Alphabets of the String