for a given metabolomics study (De Livera et al (2015) <doi:10.1021/ac502439y>).

Imports: impute, crmn, limma, plotly, e1071, AUC, htmlwidgets, ggplot2,

Maintainer: Hien D. Nguyen <h.nguyen7@uq.edu.au>
Description: Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.
Packaged: 2015-09-23 13:35:46 UTC; hiennguyen

Title: Computes PDF, CDF, Quantile, Random Numbers and Measures of
Inference for 3 General Families of Distributions
Author: Mutua Kilai, Gichuhi Waititu, Wanjoya Kibira
Description: Computes the probability density function, the cumulative density function, quantile function, random numbers and measures of inference for the following families exponentiated generalized gull alpha power family, exponentiated gull alpha powerfamily, gull alpha power family.
Imports: AdequacyModel, gsl, rootSolve, stats

Description: The Nozzle package provides an API to generate HTML
reports with dynamic user interface elements based on
JavaScript and CSS (Cascading Style Sheets). Nozzle was
designed to facilitate summarization and rapid browsing of
complex results in data analysis pipelines where multiple
analyses are performed frequently on big data sets. The package
can be applied to any project where user-friendly reports need
Copyright: Nozzle is copyright 2013 The President and Fellows of
Harvard College and The Broad Institute, Inc. All rights
reserved. jQuery is licensed under the MIT license. Copyright
2012 jQuery Foundation. jQuery Tablesorter plugin is licensed
under the MIT license. Copyright 2008 Christian Bach.

Copyright: Nozzle is copyright 2013 The Fellows and President of

Title: Statistical Framework to Define Subgroups in Complex Datasets
Authors@R: c(person("Song", "Gao", role = "aut"),
person("Stefan", "Mutter", role = "aut"),
person("Aaron E.", "Casey", role = "aut"),
person("Ville-Petteri", "Makinen", role = c("aut", "cre"),
Description: High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues [Makinen V-P et al. (2011) J Proteome Res 11:1782-1790, <doi:10.1021/pr201036j>]. The framework includes the necessary functions to import large data files, to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results in scalable vector graphics.
Packaged: 2017-11-25 11:41:24 UTC; stefan.mutter