Description: The functions proposed in this package compute the density of the sum of a gaussian and a gamma random variables, estimate the parameters and correct the noise effect in a gamma-signal and gaussian-noise model. This package has been used to implement the background correction method for Illumina microarray data presented in Plancade S., Rozenholc Y. and Lund E. (http://arxiv.org/abs/1112.4180).

Description: The functions proposed in this package compute the density of the sum of a Gaussian and a gamma random variables, estimate the parameters and correct the noise effect in a gamma-signal and Gaussian-noise model. This package has been used to implement the background correction method for Illumina microarray data presented in Plancade S., Rozenholc Y. and Lund E. "Generalization of the normal-exponential model : exploration of a more accurate parameterization for the signal distribution on Illumina BeadArrays", BMC Bioinfo 2012, 13(329).

Author: David Scott <d.scott@auckland.ac.nz>, Jason Shicong Fu and
Depends: R (>= 2.3.0), DistributionUtils, GeneralizedHyperbolic
Description: This package provides functions for the normal Laplace
distribution. It is currently under development and provides
only limited functionality. Density, distribution and quantile
functions, random number generation, and moments are provided.

Description: Functions for the normal Laplace distribution. The package is
under development and provides only limited functionality.
Density, distribution and quantile functions, random number generation,

Title: A Graphical User Interface for Testing Normality Visually
person("Christopher", "Casement", , "casementc@gmail.com", role = c("cre", "aut")),
person("Laura", "McSweeney", role = "aut")
Description: Package including an interactive Shiny application for
Packaged: 2021-11-02 15:40:16 UTC; ccasement

Packaged: 2022-11-09 18:24:59 UTC; ccasement

Author: Alysha M De Livera, Gavriel Olshansky
Description: Metabolomics data are inevitably subject to a component of unwanted
variation, due to factors such as batch effects, matrix effects, and confounding
biological variation. This package is a collection of functions designed to
implement, assess, and choose a suitable normalization method
for a given metabolomics study (De Livera et al (2015) <doi:10.1021/ac502439y> ).
Imports: RGtk2, impute, crmn, gplots, limma, plotly, statTarget,
Biobase, DiffCorr, e1071, AUC, htmlwidgets, metabolomics,