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csmGmm

This R package 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 K sets of test statistics corresponding to the same J SNPs, where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each SNP, we want to know if we can reject all K individual nulls. The preprint “Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies” by R Sun, Z McCaw, and X Lin is available upon request from the package maintainer. Please see the vignette for a quickstart guide.

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Version

Install

install.packages('csmGmm')

Monthly Downloads

499

Version

0.3.0

License

GPL-3

Maintainer

Ryan Sun

Last Published

December 3rd, 2024

Functions in csmGmm (0.3.0)

symm_fit_cor_EM

symm_fit_cor.R
calc_dens_ind_3d

Calculate J trivariate normal densities (all dimensions are independent) under fitted csmGmm.
calc_dens_ind_multiple

Calculate the density of K-dimensional multivariate normal (all dimensions are independent) under fitted acsGmm.
calc_dens_cor

calc_dens_cor.R
find_2d

Tells if row x if allTestStats is an incongruous result (has a higher lfdr than a set of test statistics with lower magnitudes). For K=2 case.
find_max_means

find_max_means.R
calc_dens_ind_2d

calc_dens_ind.R
check_incongruous

check_incongruous.R
find_3d

Tells if row x if allTestStats is an incongruous result (has a higher lfdr than a set of test statistics with lower magnitudes). For K=3 case.
symm_fit_cor_EM_fulllik

symm_fit_cor_fulllik.R
symm_fit_ind_EM_noAssumption

symm_fit_ind_noAssumption.R
symm_fit_cor_EM_rho

symm_fit_cor_rho.R
symm_fit_ind_EM

symm_fit_ind.R
symm_fit_cor_EM_noAssumption

symm_fit_cor_noAssumption.R