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mvMISE  

A General Framework of Multivariate Mixed-Effects Selection Models
View on CRAN: Click here


Download and install mvMISE package within the R console
Install from CRAN:
install.packages("mvMISE")

Install from Github:
library("remotes")
install_github("cran/mvMISE")

Install by package version:
library("remotes")
install_version("mvMISE", "1.0")



Attach the package and use:
library("mvMISE")
Maintained by
Jiebiao Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-06-10
Latest Update: 2018-06-10
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) . 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.
How to cite:
Jiebiao Wang (2018). mvMISE: A General Framework of Multivariate Mixed-Effects Selection Models. R package version 1.0, https://cran.r-project.org/web/packages/mvMISE
Previous versions and publish date:
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Functions, R codes and Examples using the mvMISE R package
Some associated functions: mvMISE_b . mvMISE_e . mvMISE_e_perm . sim_dat . 
Some associated R codes: Full mvMISE package functions and examples
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