CRAN/E | mvMISE

mvMISE

A General Framework of Multivariate Mixed-Effects Selection Models

Installation

About

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.

github.com/randel/mvMISE
Bug report File report

Key Metrics

Version 1.0
Published 2018-06-10 2119 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks mvMISE results

Downloads

Yesterday 5 0%
Last 7 days 23 -34%
Last 30 days 114 -15%
Last 90 days 551 +42%
Last 365 days 1.746 -22%

Maintainer

Maintainer

Jiebiao Wang

randel.wang@gmail.com

Authors

Jiebiao Wang
Lin S. Chen

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

lme4
MASS