mvMISE: 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) <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.

Version: 1.0
Depends: lme4, MASS
Published: 2018-06-10
Author: Jiebiao Wang and Lin S. Chen
Maintainer: Jiebiao Wang <randel.wang at gmail.com>
BugReports: https://github.com/randel/mvMISE/issues
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://github.com/randel/mvMISE
NeedsCompilation: no
CRAN checks: mvMISE results

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Reference manual: mvMISE.pdf
Package source: mvMISE_1.0.tar.gz
Windows binaries: r-devel: mvMISE_1.0.zip, r-release: mvMISE_1.0.zip, r-oldrel: mvMISE_1.0.zip
OS X binaries: r-release: mvMISE_1.0.tgz, r-oldrel: not available

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