Package: mashr 0.2.79

Peter Carbonetto

mashr: Multivariate Adaptive Shrinkage

Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <doi:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.

Authors:Matthew Stephens [aut], Sarah Urbut [aut], Gao Wang [aut], Yuxin Zou [aut], Yunqi Yang [ctb], Sam Roweis [cph], David Hogg [cph], Jo Bovy [cph], Peter Carbonetto [aut, cre]

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Peer review:

Bug tracker:https://github.com/stephenslab/mashr/issues

On CRAN:

28 exports 87 stars 4.48 score 18 dependencies 3 dependents 6 mentions 596 scripts 461 downloads

Last updated 11 months agofrom:cbf046294c. Checks:OK: 1 NOTE: 8. Indexed: yes.

Exports:contrast_matrixcov_canonicalcov_edcov_flashcov_pcacov_udiestimate_null_correlation_simpleextreme_deconvolutionget_estimated_piget_log10bfget_n_significant_conditionsget_pairwise_sharingget_pairwise_sharing_from_samplesget_samplesget_significant_resultsmashmash_1by1mash_compute_loglikmash_compute_posterior_matricesmash_compute_vloglikmash_estimate_corr_emmash_plot_metamash_set_datamash_update_datasim_contrast1sim_contrast2simple_simssimple_sims2

Dependencies:abindashrassertthatetrunctinvgammairlbalatticeMatrixmixsqpmvtnormplyrRcppRcppArmadilloRcppGSLrmetasoftImputeSQUAREMtruncnorm