R package 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 30 articles )

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  1. Tristan Mary-Huard, Sarmistha Das, Indranil Mukhopadhyay, Stéphane Robin: Querying multiple sets of p-values through composed hypothesis testing (2021) arXiv
  2. Martin, Ryan: Empirical priors and posterior concentration rates for a monotone density (2019)
  3. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  4. Darnell, Gregory; Georgiev, Stoyan; Mukherjee, Sayan; Engelhardt, Barbara E.: Adaptive randomized dimension reduction on massive data (2017)
  5. Mahmoudi, Mohammad Reza; Maleki, Mohsen: A new method to detect periodically correlated structure (2017)
  6. Miok, Viktorian; Wilting, Saskia M.; van Wieringen, Wessel N.: Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data (2017)
  7. Nematollahi, A. R.; Soltani, A. R.; Mahmoudi, M. R.: Periodically correlated modeling by means of the periodograms asymptotic distributions (2017)
  8. Nie, Zixin; Liang, Kun: Adaptive filtering increases power to detect differentially expressed genes (2017)
  9. Yuan, Ao; Giurcanu, Mihai; Luta, George; Tan, Ming T.: U-statistics with conditional kernels for incomplete data models (2017)
  10. Lee, Namgil; Choi, Hyemi; Kim, Sung-Ho: Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise (2016)
  11. Mukhopadhyay, Subhadeep: Large-scale signal detection: a unified perspective (2016)
  12. Bickel, David R.: Inference after checking multiple Bayesian models for data conflict and applications to mitigating the influence of rejected priors (2015)
  13. Huang, Furong; Niranjan, U. N.; Hakeem, Mohammad Umar; Anandkumar, Animashree: Online tensor methods for learning latent variable models (2015)
  14. Heller, Ruth; Yekutieli, Daniel: Replicability analysis for genome-wide association studies (2014)
  15. Kris Sankaran; Susan Holmes: structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data (2014) not zbMATH
  16. Phillips, Daisy; Ghosh, Debashis: Testing the disjunction hypothesis using Voronoi diagrams with applications to genetics (2014)
  17. Won, Joong-Ho; Lim, Johan; Yu, Donghyeon; Kim, Byung Soo; Kim, Kyunga: Monotone false discovery rate (2014)
  18. Dvorkin, Daniel; Biehs, Brian; Kechris, Katerina: A graphical model method for integrating multiple sources of genome-scale data (2013)
  19. Martin, Ryan; Tokdar, Surya T.: A nonparametric empirical Bayes framework for large-scale multiple testing (2012)
  20. Sacha Epskamp; Angélique Cramer; Lourens Waldorp; Verena Schmittmann; Denny Borsboom: qgraph: Network Visualizations of Relationships in Psychometric Data (2012) not zbMATH

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