Reference

All functions
contrast_matrix()	Create contrast matrix
cov_canonical()	Compute a list of canonical covariance matrices
cov_ed()	Perform "extreme deconvolution" (Bovy et al) on a subset of the data
cov_flash()	Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matrices
cov_pca()	Perform PCA on data and return list of candidate covariance matrices
cov_udi()	Compute a list of covariance matrices corresponding to the "Unassociated", "Directly associated" and "Indirectly associated" models
estimate_null_correlation_simple()	Estimate null correlations (simple)
extreme_deconvolution()	Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data
get_estimated_pi()	Return the estimated mixture proportions
get_log10bf()	Return the Bayes Factor for each effect
get_n_significant_conditions()	Count number of conditions each effect is significant in
get_pairwise_sharing()	Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean
get_pairwise_sharing_from_samples()	Compute the proportion of (significant) signals shared by magnitude in each pair of conditions
get_posterior_condition_wise_summary()	Condition-wise Posterior Summary
get_samples()	Return samples from a mash object
get_significant_results()	Find effects that are significant in at least one condition
mash()	Apply mash method to data
mash_1by1()	Perform condition-by-condition analyses
mash_compute_loglik()	Compute loglikelihood for fitted mash object on new data.
mash_compute_posterior_matrices()	Compute posterior matrices for fitted mash object on new data
mash_compute_vloglik()	Compute vector of loglikelihood for fitted mash object on new data
mash_estimate_corr_em()	Fit mash model and estimate residual correlations using EM algorithm
mash_plot_meta()	Plot metaplot for an effect based on posterior from mash
mash_set_data()	Create a data object for mash analysis.
mash_update_data()	Update the data object for mash analysis.
sim_contrast1()	Create simplest simulation, cj = mu 1 data used for contrast analysis
sim_contrast2()	Create simulation with signal data used for contrast analysis.
simple_sims()	Create some simple simulated data for testing purposes
simple_sims2()	Create some more simple simulated data for testing purposes