mixfdr: Computes false discovery rates and effect sizes using normal mixtures , This package fits normal mixture models to data and uses them to compute effect size estimates and local and tail area false discovery rates. To make this precise, suppose you have many normally distributed z’s, and each z[i] has mean delta[i]. This package will estimate delta[i] based on the z’s (effect sizes), P(delta[i]=0|z[i]) (local false discovery rates) and P(delta[i]=0||z[i]|>z) (tail area false discovery rates). (Source: http://cran.r-project.org/web/packages)

References in zbMATH (referenced in 20 articles )

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  1. Martin, Ryan: A survey of nonparametric mixing density estimation via the predictive recursion algorithm (2021)
  2. Bickel, David R.; Rahal, Abbas: Model fusion and multiple testing in the likelihood paradigm: shrinkage and evidence supporting a point null hypothesis (2019)
  3. Cai, Junhui; Mandelbaum, Avishai; Nagaraja, Chaitra H.; Shen, Haipeng; Zhao, Linda: Statistical theory powering data science (2019)
  4. Sinha, Shyamalendu; Hart, Jeffrey D.: Estimating the mean and variance of a high-dimensional normal distribution using a mixture prior (2019)
  5. Feng, Long; Dicker, Lee H.: Approximate nonparametric maximum likelihood for mixture models: a convex optimization approach to fitting arbitrary multivariate mixing distributions (2018)
  6. Madrid-Padilla, Oscar-Hernan; Polson, Nicholas G.; Scott, James: A deconvolution path for mixtures (2018)
  7. Weinstein, Asaf; Ma, Zhuang; Brown, Lawrence D.; Zhang, Cun-Hui: Group-linear empirical Bayes estimates for a heteroscedastic normal mean (2018)
  8. Musal, Rasim M.; Ekin, Tahir: Medical overpayment estimation: a Bayesian approach (2017)
  9. Pan, Jia-Chiun; Huang, Yufen; Hwang, J. T. Gene: Estimation of selected parameters (2017)
  10. Cipolli, William III; Hanson, Timothy; McLain, Alexander C.: Bayesian nonparametric multiple testing (2016)
  11. Lee, Donghwan; Lee, Youngjo: Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field model (2016)
  12. Mukhopadhyay, Subhadeep: Large-scale signal detection: a unified perspective (2016)
  13. Xie, Xianchao; Kou, S. C.; Brown, Lawrence: Optimal shrinkage estimation of mean parameters in family of distributions with quadratic variance (2016)
  14. Zehetmayer, Sonja; Graf, Alexandra C.; Posch, Martin: Sample size reassessment for a two-stage design controlling the false discovery rate (2015)
  15. Heller, Ruth; Yekutieli, Daniel: Replicability analysis for genome-wide association studies (2014)
  16. Miecznikowski, Jeffrey C.; Gaile, Daniel P.: A novel characterization of the generalized family wise error rate using empirical null distributions (2014)
  17. Phillips, Daisy; Ghosh, Debashis: Testing the disjunction hypothesis using Voronoi diagrams with applications to genetics (2014)
  18. Bickel, David R.: Simple estimators of false discovery rates given as few as one or two (p)-values without strong parametric assumptions (2013)
  19. Muralidharan, Omkar: High dimensional exponential family estimation via empirical Bayes (2012)
  20. Muralidharan, Omkar: An empirical Bayes mixture method for effect size and false discovery rate estimation (2010)