Package: mvMISE
Title: A General Framework of Multivariate Mixed-Effects Selection
        Models
Version: 1.0
Date: 2018-06-04
Author: Jiebiao Wang and Lin S. Chen
Maintainer: Jiebiao Wang <randel.wang@gmail.com>
Description: 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.
License: GPL
Depends: lme4, MASS
URL: https://github.com/randel/mvMISE
BugReports: https://github.com/randel/mvMISE/issues
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-04 17:54:46 UTC; rande
Repository: CRAN
Date/Publication: 2018-06-10 16:47:54 UTC
Built: R 4.6.0; ; 2026-04-26 09:01:45 UTC; unix
