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

1 May 2018 Leave a comment

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 Leave a comment

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 Leave a comment
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 Leave a comment

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 Leave a comment

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 Leave a comment

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