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Computation of local false discovery rates.
Last Release on Jan 20, 2018
Estimates 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 ...
Last Release on Jan 20, 2018
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
Last Release on Jan 20, 2018
LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis.
Last Release on Jan 20, 2018
Provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with variable selection. The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism.
Last Release on Aug 16, 2018
KODAMA (KnOwledge Discovery by Accuracy MAximization) is an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data.
Last Release on Jan 20, 2018
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Last Release on Jan 20, 2018


iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes
Last Release on Jan 20, 2018
Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient.
Last Release on Jan 20, 2018
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
Last Release on Jan 20, 2018