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ashr: Methods for Adaptive Shrinkage, using Empirical Bayes

The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", &lt;<a href="https://doi.org/10.1093%2Fbiostatistics%2Fkxw041" target="_top">doi:10.1093/biostatistics/kxw041</a>&gt;. These methods can be applied whenever two sets of summary statistics&mdash;estimated effects and standard errors&mdash;are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accommodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).



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ashr: Methods for Adaptive Shrinkage, using Empirical Bayes

https://cran.r-project.org/package=ashr

The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", &lt;<a href="https://doi.org/10.1093%2Fbiostatistics%2Fkxw041" target="_top">doi:10.1093/biostatistics/kxw041</a>&gt;. These methods can be applied whenever two sets of summary statistics&mdash;estimated effects and standard errors&mdash;are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accommodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).



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https://cran.r-project.org/package=ashr

ashr: Methods for Adaptive Shrinkage, using Empirical Bayes

The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", &lt;<a href="https://doi.org/10.1093%2Fbiostatistics%2Fkxw041" target="_top">doi:10.1093/biostatistics/kxw041</a>&gt;. These methods can be applied whenever two sets of summary statistics&mdash;estimated effects and standard errors&mdash;are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accommodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).

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      The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", &lt;<a href="https://doi.org/10.1093%2Fbiostatistics%2Fkxw041" target="_top">doi:10.1093/biostatistics/kxw041</a>&gt;. These methods can be applied whenever two sets of summary statistics&mdash;estimated effects and standard errors&mdash;are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accommodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).
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