fdrtool: Estimation of (Local) False Discovery Rates and Higher Criticism. This package allows to estimate both tail area-based false discovery rates (Fdr) as well as local false discovery rates (fdr) for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold.

References in zbMATH (referenced in 12 articles )

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  1. Bickel, David R.: Inference after checking multiple Bayesian models for data conflict and applications to mitigating the influence of rejected priors (2015)
  2. Huang, Furong; Niranjan, U.N.; Hakeem, Mohammad Umar; Anandkumar, Animashree: Online tensor methods for learning latent variable models (2015)
  3. Heller, Ruth; Yekutieli, Daniel: Replicability analysis for genome-wide association studies (2014)
  4. Phillips, Daisy; Ghosh, Debashis: Testing the disjunction hypothesis using Voronoi diagrams with applications to genetics (2014)
  5. Won, Joong-Ho; Lim, Johan; Yu, Donghyeon; Kim, Byung Soo; Kim, Kyunga: Monotone false discovery rate (2014)
  6. Martin, Ryan; Tokdar, Surya T.: A nonparametric empirical Bayes framework for large-scale multiple testing (2012)
  7. Bücher, Axel; Dette, Holger; Volgushev, Stanislav: New estimators of the Pickands dependence function and a test for extreme-value dependence (2011)
  8. Lin, Wan-Yu; Lee, Wen-Chung: Floating prioritized subset analysis: A powerful method to detect differentially expressed genes (2011)
  9. Morris, Jeffrey S.; Baladandayuthapani, Veerabhadran; Herrick, Richard C.; Sanna, Pietro; Gutstein, Howard: Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data (2011)
  10. Ahdesmäki, Miika; Strimmer, Korbinian: Feature selection in omics prediction problems using cat scores and false nondiscovery rate control (2010)
  11. Muralidharan, Omkar: An empirical Bayes mixture method for effect size and false discovery rate estimation (2010)
  12. von Borries, George; Wang, Haiyan: Partition clustering of high dimensional low sample size data based on $p$-values (2009)