metabolome analysis (DLEMMA) approach, described in Liron (2018) <doi:10.1021/acs.analchem.8b01644>.

Imports: S4Vectors, stats, utils, plotly

Imports: S4Vectors, stats, utils, dplyr, plotly

metabolome analysis (DLEMMA) approach, described in Liron (2018) <doi:10.1021/acs.analchem.8b01644>,
Imports: xcms, stats, utils, dplyr, ggplot2, scales, plotly

Title: Testing Homoscedasticity, Multivariate Normality, and Missing
Author: Mortaza Jamshidian, Siavash Jalal, and Camden Jansen
Description: For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)," Journal of Statistical Software,  56(6), 1-31. URL http://www.jstatsoft.org/v56/i06/.

Description: To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR).
For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)," Journal of Statistical Software,  56(6), 1-31. URL http://www.jstatsoft.org/v56/i06/.

Description: For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2013). ``MissMech: An R package for testing homoscedasticity, multivariate normality, and missing completely at random (MCAR),'' Journal of Statistical Software.

Title: A GUI for Missing Data Exploration
Description: This package provides numeric and graphical summaries for
the missing values from both discrete and continuous variables.
A variety of imputation methods are applied, including
univariate imputations like fixed or random values, multiple
imputations based on other packages, and imputations
Depends: gWidgetsRGtk2, cairoDevice, GGally, Hmisc, norm

Depends: gWidgetsRGtk2, Hmisc, norm, GGally, ggplot2
Imports: cairoDevice, grid, plyr, reshape2, reshape

Description: This package provides numeric and graphical
summaries for the missing values from both categorical
and quantitative variables. A variety of imputation
methods are applied, including the univariate imputations
like fixed or random values, multivariate imputations
like the nearest neighbors and multiple imputations,
and imputations conditioned on a categorical variable.
Imports: GGally, cairoDevice, grid, reshape