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
Config/pak/sysreqs: cmake make
Repository: https://randel.r-universe.dev
Date/Publication: 2018-07-07 01:31:45 UTC
RemoteUrl: https://github.com/randel/mvmise
RemoteRef: HEAD
RemoteSha: eaf70a15e68b5de3d4de78ea14d4003dd5c78135
NeedsCompilation: no
Packaged: 2026-06-19 08:58:19 UTC; root