Description:  The permax library consists of 8 functions, intended to facilitate
Packaged: Wed May 26 13:47:08 2004; hornik

Authors@R: c(person(given = "Dries", family = "Debeer", role = c("aut", "cre"), email = "debeer.dries@gmail.com"),
person(given = "Torsten", family = "Hothorn", role = "aut"),
person(given = "Carolin", family = "Strobl", role = "aut"))
Description: An add-on to the 'party' package, with a faster implementation
of the partial-conditional permutation importance for random forests. The
standard permutation importance is implemented exactly the same as in
the 'party' package. The conditional permutation importance can be
computed faster, with an option to be backward compatible to the 'party'
implementation. The package is compatible with random forests fit using the
'party' and the 'randomForest' package. The methods are described in
Imports: graphics, grDevices, ipred (>= 0.9-6), methods, party (>=
1.3-3), randomForest (>= 4.6-14), stats, survival (>=
Suggests: knitr, rmarkdown, scales (>= 0.5.0), testthat

Title: Permutation test to compare variability within and distance between two groups
Description: The permtest uses permutations in order to compare the variability within and distance between two groups within a set of microarray data.
Packaged: Tue Aug 16 09:19:50 2005; biopara

Packaged: Mon Oct 22 10:25:48 2007; root

Title: Permutation test to compare variability within and distance
Description: The permtest uses permutations in order to compare the
variability within and distance between two groups within a set

Title: A Permutation Based Test for Biomarker Discovery in Microbiome
Description: All the functions compiled in this package were created to perform permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in pairwise manner, after random shuffling of data into the different groups of study.

Description: The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions', 'anders', and 'paulson'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse BIOM-like format; v1.2 and olders incorporates "optimize" function to maximize evaluation of features with largest inter-class variation.