Pacote do R multivariate analysis e suas aplicações na análise estatística de dados | R multivariate analysis package and its applications in statistical data analysis

Multivariate analysis comprises a set of statistical methods used when multiple variables are measured simultaneously in each sample element and can be applied across various fields of research. In Agricultural Sciences, it is frequently employed in studies related to entomology, soil fertility, plant nutrition, medicinal plants, weed control, genetic improvement, vegetable production, irrigation, fruit cultivation, among others. Besides enabling the study of the effects of "treatments" (independent variables) by considering multiple characteristics (dependent variables) simultaneously, multivariate analysis simplifies data handling, especially when dealing with large datasets involving many variables and sample data. Among the software currently used, R, with its RStudio interface, stands out for being free and open-source. For performing multivariate analyses, such as dissimilarity measurements, principal component analysis, dendrograms, canonical variables, discriminant analysis, and multivariate analysis of variance (MANOVA), there is a package called "MultivariateAnalysis". This paper aims to disseminate the R MultivariateAnalysis package through an article and an e-book, demonstrating its applications in plant production research. The package is available for download at <https://cran.r project.org/package=MultivariateAnalysis>. Released in 2021 by researcher Alcinei Místico Azevedo, the package reached 17,325 downloads by 2024. With MultivariateAnalysis, it is possible to perform techniques such as dissimilarity measurements, hierarchical clustering, principal component analyses, principal coordinate analyses, Mantel correlation, dendrograms, multivariate variance analyses, canonical variables, and others. The package consolidates several functions from various programs into one, facilitating the execution of multivariate analyses. The e-book consists of 11 chapters, providing examples of the MultivariateAnalysis package functions.

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