R: Multivariate Adaptive Shrinkage

# Multivariate Adaptive Shrinkage

## Documentation for package ‘mashr’ version 0.2.79

- DESCRIPTION file.

## Help Pages

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_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.
simple_sims | Create some simple simulated data for testing purposes
simple_sims2 | Create some more simple simulated data for testing purposes
sim_contrast1 | Create simplest simulation, cj = mu 1 data used for contrast analysis
sim_contrast2 | Create simulation with signal data used for contrast analysis.