A General Framework of Multivariate Mixed-Effects Selection Models

Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) . The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness.


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install.packages("mvMISE")

1.0 by Jiebiao Wang, 9 months ago


https://github.com/randel/mvMISE


Report a bug at https://github.com/randel/mvMISE/issues


Browse source code at https://github.com/cran/mvMISE


Authors: Jiebiao Wang and Lin S. Chen


Documentation:   PDF Manual  


GPL license


Depends on lme4, MASS


See at CRAN