Package: ScottKnott 1.4-0

ScottKnott: The ScottKnott Clustering Algorithm

Performs the Scott & Knott (1974) clustering algorithm as a multiple comparison method in the Analysis of Variance context, for both balanced and unbalanced <doi:10.1590/1984-70332017v17n1a1> designs. Accepts input from 'formula', 'aov', 'lm', 'aovlist', and 'lmerMod' objects.

Authors:J. C. Faria [aut], E. G. Jelihovschi [aut], I. B. Allaman [aut, cre]

ScottKnott_1.4-0.tar.gz
ScottKnott_1.4-0.zip(r-4.7)ScottKnott_1.4-0.zip(r-4.6)ScottKnott_1.4-0.zip(r-4.5)
ScottKnott_1.4-0.tgz(r-4.6-any)ScottKnott_1.4-0.tgz(r-4.5-any)
ScottKnott_1.4-0.tar.gz(r-4.7-any)ScottKnott_1.4-0.tar.gz(r-4.6-any)
ScottKnott_1.4-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ScottKnott/json (API)

# Install 'ScottKnott' in R:
install.packages('ScottKnott', repos = c('https://ivanalaman.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ivanalaman/scottknott/issues

Datasets:
  • CRD1 - Completely Randomized Design
  • CRD2 - Completely Randomized Design
  • FE - Factorial Experiment
  • LSD - Latin Squares Design
  • RCBD - Randomized Complete Block Design
  • sorghum - Sorghum Yield: Balanced Squared Lattice Design
  • SPE - Split-Plot Experiment
  • SPET - Split-plot Experiment in Time
  • SSPE - Split-Split-Plot Experiment

On CRAN:

Conda:

6.82 score 1 stars 1 packages 89 scripts 2.1k downloads 6 mentions 7 exports 6 dependencies

Last updated from:bc84194340. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK131
source / vignettesOK164
linux-release-x86_64OK259
macos-release-arm64OK141
macos-oldrel-arm64OK293
windows-develOK92
windows-releaseOK110
windows-oldrelOK80
wasm-releaseOK100

Exports:boxplot.SKplot.SKprint.SKSKsummary.SKxtablextable.SK

Dependencies:emmeansestimabilitymvtnormnumDerivrlangxtable

Introduction to the ScottKnott Package
Overview | 1. Quick Start - Completely Randomized Design (CRD) | 2. Accepted Input Classes | 3. Unbalanced Data | 4. Randomized Complete Block Design (RCBD) | 5. Significance Level | 6. Factorial Experiment (FE) | 7. Split-Plot Experiment (SPE) | 8. Visualisation Options | 8.1 Dispersion bars | 8.2 Comparing all four options | 8.3 Boxplot | 9. Tabular Output | 10. Mixed Models with lme4 | References
Rendered fromSK_intro.Rmdusingknitr::rmarkdown

Last update: 2026-05-23
Started: 2026-05-23

Introduction to the ScottKnott Package (PDF)
Overview | Quick Start — Completely Randomized Design (CRD) | Accepted Input Classes | Unbalanced Data | Randomized Complete Block Design (RCBD) | Significance Level | Factorial Experiment (FE) | Split-Plot Experiment (SPE) | Visualisation Options | Tabular Output | Mixed Models with lme4 | References
Rendered fromSK_intro_pdf.Rnwusingknitr::knitr

Last update: 2026-05-23
Started: 2026-05-23