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mvMISE
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]
[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:
No previous versions
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imports, suggests or enhances
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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