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promotionImpact  

Analysis & Measurement of Promotion Effectiveness
View on CRAN: Click here


Download and install promotionImpact package within the R console
Install from CRAN:
install.packages("promotionImpact")

Install from Github:
library("remotes")
install_github("cran/promotionImpact")

Install by package version:
library("remotes")
install_version("promotionImpact", "0.1.5")



Attach the package and use:
library("promotionImpact")
Maintained by
Nahyun Kim
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-05
Latest Update: 2021-04-13
Description:
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) and some prespecified variables (e.g. start of a month).
How to cite:
Nahyun Kim (2019). promotionImpact: Analysis & Measurement of Promotion Effectiveness. R package version 0.1.5, https://cran.r-project.org/web/packages/promotionImpact. Accessed 03 Dec. 2024.
Previous versions and publish date:
0.1.2 (2019-06-05 14:40), 0.1.3 (2020-03-31 17:10), 0.1.4 (2020-06-29 16:40)
Other packages that cited promotionImpact R package
View promotionImpact citation profile
Other R packages that promotionImpact depends, imports, suggests or enhances
Complete documentation for promotionImpact
Functions, R codes and Examples using the promotionImpact R package
Some associated functions: compareModels . create.dummy.vars . create.smooth.vars . detectOutliers . format_time . promotion.model . promotionImpact . sim.data . sim.promotion . sim.promotion.sales . value_fit_plot . 
Some associated R codes: promotion_impact.R .  Full promotionImpact package functions and examples
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