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Package: AgroR

Type: Package

Title: Experimental Statistics and Graphics for Agricultural Sciences

Version: 1.2.7

Date: 2022-02-04


Maintainer: Gabriel Danilo Shimizu

Description: Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi: 10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).

Encoding: UTF-8

RoxygenNote: 7.1.1

Imports: ggplot2, lmtest, nortest, ScottKnott, lme4, crayon, grid, gridExtra, stringr, Hmisc, emmeans, ARTool, multcomp, ggrepel, reshape2, MASS, cowplot, multcompView, hnp, RColorBrewer, drc Suggests: DT, knitr, rmarkdown, roxygen2

Depends: R (>= 3.6.0)

License: GPL (>= 2)


# Install release version from CRAN

# Install development version from GitHub


Data set

Descritive analysis

Analysis function

Analysis for testing of two independent or dependent samples by parametric or non-parametric method

Analysis of simple experiments

Analysis of simple experiments in DIC and DBC by generalized linear model (Binomial or Poisson)

Analysis of experiments in DIC, DBC or DQL with multiple assessments over time or disregarding the effect of another factor

Analysis of groups of experiments in DIC and DBC

Analysis of experiments in double factorial design in DIC and DBC

Analysis of double factorial design experiments in DIC or DBC with an additional treatment

Analysis of DIC or DBC experiments in a factorial scheme with three factors

Analysis of triple factorial design experiments in DIC or DBC with an additional treatment

Split-plot scheme in DIC or DBC

Splitsplitplot parcels scheme in DBC

Plot subdivided into randomized blocks with a subplot in a double factorial scheme

Dunnett’s Test for Comparison of Control vs. Treatments

Dunn’s non-parametric test

Logistic regression 3 or 4 parameters

Polynomial Regression to the Third Degree

Principal component analysis