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  "Title": "Multivariate Adaptive Shrinkage",
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  "Authors@R": "c(person(\"Matthew\",\"Stephens\",role=\"aut\"),\nperson(\"Sarah\",\"Urbut\",role=\"aut\"),\nperson(\"Gao\",\"Wang\",role=\"aut\"),\nperson(\"Yuxin\",\"Zou\",role=\"aut\"),\nperson(\"Yunqi\",\"Yang\",role=\"ctb\"),\nperson(\"Sam\",\"Roweis\",role=\"cph\"),\nperson(\"David\",\"Hogg\",role=\"cph\"),\nperson(\"Jo\",\"Bovy\",role=\"cph\"),\nperson(\"Peter\",\"Carbonetto\",role=c(\"aut\",\"cre\"),\nemail=\"peter.carbonetto@gmail.com\"))",
  "Description": "Implements the multivariate adaptive shrinkage (mash)\nmethod of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8>\nfor estimating and testing large numbers of effects in many\nconditions (or many outcomes). Mash takes an empirical Bayes\napproach to testing and effect estimation; it estimates\npatterns of similarity among conditions, then exploits these\npatterns to improve accuracy of the effect estimates. The core\nlinear algebra is implemented in C++ for fast model fitting and\nposterior computation.",
  "URL": "https://github.com/stephenslab/mashr",
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  "License": "BSD_3_clause + file LICENSE",
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    "mash_compute_posterior_matrices",
    "mash_compute_vloglik",
    "mash_estimate_corr_em",
    "mash_plot_meta",
    "mash_set_data",
    "mash_update_data",
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    "sim_contrast2",
    "simple_sims",
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      "title": "Create contrast matrix",
      "topics": [
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      ]
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      "title": "Compute a list of canonical covariance matrices",
      "topics": [
        "cov_canonical"
      ]
    },
    {
      "page": "cov_ed",
      "title": "Perform \"extreme deconvolution\" (Bovy et al) on a subset of the data",
      "topics": [
        "cov_ed"
      ]
    },
    {
      "page": "cov_flash",
      "title": "Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matrices",
      "topics": [
        "cov_flash"
      ]
    },
    {
      "page": "cov_pca",
      "title": "Perform PCA on data and return list of candidate covariance matrices",
      "topics": [
        "cov_pca"
      ]
    },
    {
      "page": "cov_udi",
      "title": "Compute a list of covariance matrices corresponding to the \"Unassociated\", \"Directly associated\" and \"Indirectly associated\" models",
      "topics": [
        "cov_udi"
      ]
    },
    {
      "page": "estimate_null_correlation_simple",
      "title": "Estimate null correlations (simple)",
      "topics": [
        "estimate_null_correlation_simple"
      ]
    },
    {
      "page": "extreme_deconvolution",
      "title": "Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data",
      "topics": [
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      ]
    },
    {
      "page": "get_estimated_pi",
      "title": "Return the estimated mixture proportions",
      "topics": [
        "get_estimated_pi"
      ]
    },
    {
      "page": "get_log10bf",
      "title": "Return the Bayes Factor for each effect",
      "topics": [
        "get_log10bf"
      ]
    },
    {
      "page": "get_n_significant_conditions",
      "title": "Count number of conditions each effect is significant in",
      "topics": [
        "get_n_significant_conditions"
      ]
    },
    {
      "page": "get_pairwise_sharing",
      "title": "Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean",
      "topics": [
        "get_pairwise_sharing"
      ]
    },
    {
      "page": "get_pairwise_sharing_from_samples",
      "title": "Compute the proportion of (significant) signals shared by magnitude in each pair of conditions",
      "topics": [
        "get_pairwise_sharing_from_samples"
      ]
    },
    {
      "page": "get_samples",
      "title": "Return samples from a mash object",
      "topics": [
        "get_samples"
      ]
    },
    {
      "page": "get_significant_results",
      "title": "Find effects that are significant in at least one condition",
      "topics": [
        "get_significant_results"
      ]
    },
    {
      "page": "mash",
      "title": "Apply mash method to data",
      "topics": [
        "mash"
      ]
    },
    {
      "page": "mash_1by1",
      "title": "Perform condition-by-condition analyses",
      "topics": [
        "mash_1by1"
      ]
    },
    {
      "page": "mash_compute_loglik",
      "title": "Compute loglikelihood for fitted mash object on new data.",
      "topics": [
        "mash_compute_loglik"
      ]
    },
    {
      "page": "mash_compute_posterior_matrices",
      "title": "Compute posterior matrices for fitted mash object on new data",
      "topics": [
        "mash_compute_posterior_matrices"
      ]
    },
    {
      "page": "mash_compute_vloglik",
      "title": "Compute vector of loglikelihood for fitted mash object on new data",
      "topics": [
        "mash_compute_vloglik"
      ]
    },
    {
      "page": "mash_estimate_corr_em",
      "title": "Fit mash model and estimate residual correlations using EM algorithm",
      "topics": [
        "mash_estimate_corr_em"
      ]
    },
    {
      "page": "mash_plot_meta",
      "title": "Plot metaplot for an effect based on posterior from mash",
      "topics": [
        "mash_plot_meta"
      ]
    },
    {
      "page": "mash_set_data",
      "title": "Create a data object for mash analysis.",
      "topics": [
        "mash_set_data"
      ]
    },
    {
      "page": "mash_update_data",
      "title": "Update the data object for mash analysis.",
      "topics": [
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      ]
    },
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      "title": "Create simplest simulation, cj = mu 1 data used for contrast analysis",
      "topics": [
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    },
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      "title": "Create some simple simulated data for testing purposes",
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      ]
    },
    {
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      "title": "Create some more simple simulated data for testing purposes",
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