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An R package to transform false discovery rates to posterior probability estimates

1 May 2018 Comments off

There are many estimators of false discovery rate. In this package we compute the Nonlocal False Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator.

Source: CRAN – Package CorrectedFDR

LFDR.MLE-package function | R Documentation

1 March 2018 Comments off

Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE):

LFDR.MLE-package function | R Documentation

Categories: empirical Bayes, software

Empirical Bayes software (R packages)

1 May 2016 Comments off
Categories: empirical Bayes, software

Local FDR estimation software

30 June 2012 1 comment

LFDRenrich is a suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-component or three-component parametric mixture model of 2X2 tables such as those used in gene enrichment analyses.

LFDRhat is a more general suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-component or three-component parametric mixture model.

Software for local false discovery rate estimation

15 August 2011 Comments off

LFDR-MLE is a suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-group parametric mixture model of test statistics.

Shrinkage estimation of expression fold change

9 June 2010 Comments off

Z. Montazeri*, C. M. Yanofsky*, and D. R. Bickel, “Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: Applications to differential gene expression,” Statistical Applications in Genetics and Molecular Biology 9 (1) 23 (2010). Article | Software

* the first two authors contributed equally

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Gene network reconstruction from transcriptional dynamics

28 March 2009 Comments off

D. R. Bickel, Z. Montazeri, P.-C. Hsieh, M. Beatty, S. J. Lawit, and N. J. Bate, “Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: A case for the second derivative,” Bioinformatics 25, 772-779 (2009).

Open access (PDF) | Supplement & software | Data