Miscellaneous functions for psychometric problems

Multi-Isotope Labeling for Metabolomics Analysis
An efficient approach for fishing out the dual (or multiple)
isotope labeled analytes using dual labeling of metabolites for
metabolome analysis (DLEMMA) approach, described in Liron (2009) <doi:10.1021/ac901495a>.

An efficient approach for fishing out the dual (or multiple)
isotope labeled analytes using dual labeling of metabolites for
metabolome analysis (DLEMMA) approach, described in Liron (2018) <doi:10.1021/acs.analchem.8b01644>.

An efficient approach for fishing out the dual (or multiple)
isotope labeled analytes using dual labeling of metabolites for
metabolome analysis (DLEMMA) approach, described in Liron (2018) <doi:10.1021/acs.analchem.8b01644>,
and Dong (2019) <doi:10.1093/bioinformatics/btz092>.
xcms, stats, utils, dplyr, ggplot2, scales, plotly

Testing Homoscedasticity, Multivariate Normality, and Missing
Completely at Random
Mortaza Jamshidian, Siavash Jalal, and Camden Jansen
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/.

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/.

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.

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
conditioned on a categorical variable.
gWidgetsRGtk2, cairoDevice, GGally, Hmisc, norm