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minFactorial  

All Possible Minimally Changed Factorial Run Orders
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("minFactorial", "0.1.0")



Attach the package and use:
library("minFactorial")
Maintained by
Bijoy Chanda
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-11-05
Latest Update: 2024-11-05
Description:
In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimumnumber of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017)<doi:10.1080/03610926.2016.1152490>.This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign().
How to cite:
Bijoy Chanda (2024). minFactorial: All Possible Minimally Changed Factorial Run Orders. R package version 0.1.0, https://cran.r-project.org/web/packages/minFactorial. Accessed 23 Dec. 2024.
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