csmGmm: Conditionally Symmetric Multidimensional Gaussian Mixture Model

Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (2024, <doi:10.1080/01621459.2024.2422124>). The paper is accepted and published online (but not yet in print) in the Journal of the American Statistical Association as of Dec 1 2024.

Version:	0.3.0
Imports:	dplyr, mvtnorm, rlang, magrittr
Suggests:	knitr, rmarkdown
Published:	2024-12-03
DOI:	10.32614/CRAN.package.csmGmm
Author:	Ryan Sun [aut, cre]
Maintainer:	Ryan Sun <ryansun.work at gmail.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	csmGmm results

Documentation:

Reference manual:	csmGmm.pdf
Vignettes:	Tutorial (source, R code)

Downloads:

Package source:	csmGmm_0.3.0.tar.gz
Windows binaries:	r-devel: csmGmm_0.3.0.zip, r-release: csmGmm_0.3.0.zip, r-oldrel: csmGmm_0.3.0.zip
macOS binaries:	r-release (arm64): csmGmm_0.3.0.tgz, r-oldrel (arm64): csmGmm_0.3.0.tgz, r-release (x86_64): csmGmm_0.3.0.tgz, r-oldrel (x86_64): csmGmm_0.3.0.tgz

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