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PGaovR  

Analysis of Experimental Data using ANOVA and Mean Comparison
View on CRAN: Click here


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

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

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



Attach the package and use:
library("PGaovR")
Maintained by
Santosh Patil
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-28
Latest Update: 2026-04-28
Description:
Provides tools for designing and analyzing agricultural experiments. It includes functions for generating randomized treatment layouts for standard experimental designs such as Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Randomized Block Design (FRBD), split-plot design, and strip-plot design. The package implements one-factor and two-factor analysis of variance (ANOVA) and offers multiple comparison procedures, including Least Significant Difference (LSD), Tukey, and Duncan tests, to compare treatment means in single-factor and factorial experiments. The methods follow classical experimental design principles described in Gomez and Gomez (1984, Statistical Procedures for Agricultural Research, John Wiley & Sons, New York).
How to cite:
Santosh Patil (2026). PGaovR: Analysis of Experimental Data using ANOVA and Mean Comparison. R package version 0.1.0, https://cran.r-project.org/web/packages/PGaovR. Accessed 03 Jul. 2026.
Previous versions and publish date:
0.1.0 (2026-04-28 21:00)
Other packages that cited PGaovR R package
View PGaovR citation profile
Other R packages that PGaovR depends, imports, suggests or enhances
Complete documentation for PGaovR
Functions, R codes and Examples using the PGaovR R package
Full PGaovR package functions and examples
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