Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness. 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) the mvMISE_b model that allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) the mvMISE_e model that 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.
|Author||Jiebiao Wang and Lin S. Chen|
|Maintainer||Jiebiao Wang <[email protected]>|
|Package repository||View on GitHub|
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