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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 |
| 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 |
| macOS binaries: | r-release (arm64): mvMISE_1.0.tgz, r-oldrel (arm64): mvMISE_1.0.tgz, r-release (x86_64): mvMISE_1.0.tgz, r-oldrel (x86_64): mvMISE_1.0.tgz |
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