A General Framework of Multivariate Mixed-Effects Selection Models R package mvMISE version 1.0 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) <doi:10.1093/biostatistics/kxy022>. 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. Software Depends: lme4, MASS Jiebiao Wang <randel.wang@gmail.com> Comprehensive R Archive Network (CRAN) Jiebiao Wang and Lin S. Chen GPL 2018-06-10 application/tgz https://CRAN.R-project.org/package=mvMISE doi:10.32614/CRAN.package.mvMISE