Title: The ScottKnott Clustering Algoritm
Description: Multiple comparison test of means using the clustering method
Packaged: Fri Jan 23 09:16:24 2009; hornik

Author: Enio Jelihovschi <eniojelihovs@gmail.com>, Jose Claudio Faria
Description: Division of an ANOVA experiment treatment means into
homogeneous distinct groups using the clustering method of

Title: The ScottKnott Clustering Algorithm
<joseclaudio.faria@gmail.com> and Ivan Bezerra Allaman
Maintainer: Ivan Bezerra Allaman <ivanalaman@gmail.com>

Author: Enio Jelihovschi, Jos� Cl�udio Faria and Ivan Bezerra Allaman
Description: Division of an ANOVA experiment treatment means into homogeneous distinct groups using the clustering method of Scott & Knott.

Description: Perform the balanced (Scott and Knott, 1974) and unbalanced <doi:10.1590/1984-70332017v17n1a1> Scott & Knott algorithm.

Title: The Scott-Knott Effect Size Difference (ESD) Test
Description: An enhancement of the Scott-Knott test (which cluster distributions
into statistically distinct ranks) that takes effect size into consideration.
Depends: ScottKnott, reshape2, effsize, stats

Description: An enhancement of the Scott-Knott test (which clusters distributions
into statistically distinct ranks) that takes effect size into consideration
Depends: ScottKnott, reshape2, effsize, stats, car

Description: The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2017) <doi:10.1109/TSE.2016.2584050>].

Description: The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2018) <doi:10.1109/TSE.2018.2794977>].