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 (Journal of the American Statistical Association 2025, <doi:10.1080/01621459.2024.2422124>).
| Version: |
0.5.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr, mvtnorm, curl, data.table, ggplot2, rlang, magrittr, stats, utils |
| Suggests: |
knitr, rmarkdown, R.utils |
| Published: |
2026-06-16 |
| DOI: |
10.32614/CRAN.package.csmGmm |
| Author: |
Ryan Sun [aut, cre],
Emily Kim [aut] |
| Maintainer: |
Ryan Sun <ryansun.work at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
csmGmm results |
Documentation:
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