Package: mvMISE 1.0

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.

Authors:Jiebiao Wang and Lin S. Chen

mvMISE_1.0.tar.gz
mvMISE_1.0.zip(r-4.5)mvMISE_1.0.zip(r-4.4)mvMISE_1.0.zip(r-4.3)
mvMISE_1.0.tgz(r-4.4-any)mvMISE_1.0.tgz(r-4.3-any)
mvMISE_1.0.tar.gz(r-4.5-noble)mvMISE_1.0.tar.gz(r-4.4-noble)
mvMISE_1.0.tgz(r-4.4-emscripten)mvMISE_1.0.tgz(r-4.3-emscripten)
mvMISE.pdf |mvMISE.html✨
mvMISE/json (API)

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Bug tracker:https://github.com/randel/mvmise/issues

On CRAN:

3 exports 4 stars 1.01 score 10 dependencies 4 scripts 120 downloads

Last updated 6 years agofrom:eaf70a15e6. Checks:OK: 7. Indexed: yes.

Exports:mvMISE_bmvMISE_emvMISE_e_perm

Dependencies:bootlatticelme4MASSMatrixminqanlmenloptrRcppRcppEigen