All functions 


Create contrast matrix 

Compute a list of canonical covariance matrices 

Perform "extreme deconvolution" (Bovy et al) on a subset of the data 

Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matrices 

Perform PCA on data and return list of candidate covariance matrices 

Compute a list of covariance matrices corresponding to the "Unassociated", "Directly associated" and "Indirectly associated" models 

Estimate null correlations (simple) 

Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data 

Return the estimated mixture proportions 

Return the Bayes Factor for each effect 

Count number of conditions each effect is significant in 

Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean 

Compute the proportion of (significant) signals shared by magnitude in each pair of conditions 

Return samples from a mash object 

Find effects that are significant in at least one condition 

Apply mash method to data 

Perform conditionbycondition analyses 

Compute loglikelihood for fitted mash object on new data. 

Compute posterior matrices for fitted mash object on new data 

Compute vector of loglikelihood for fitted mash object on new data 

Fit mash model and estimate residual correlations using EM algorithm 

Plot metaplot for an effect based on posterior from mash 

Create a data object for mash analysis. 

Update the data object for mash analysis. 

Create simplest simulation, cj = mu 1 data used for contrast analysis 

Create simulation with signal data used for contrast analysis. 

Create some simple simulated data for testing purposes 

Create some more simple simulated data for testing purposes 