Showing 26 of total 26 results (show query)
bioc
onlineFDR:Online error rate control
This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions.
Maintained by David S. Robertson. Last updated 5 months ago.
multiplecomparisonsoftwarestatisticalmethoderror-rate-controlfdrfwerhypothesis-testingcpp
19.8 match 14 stars 6.88 score 26 scripts
allenzhuaz
MHTdiscrete:Multiple Hypotheses Testing for Discrete Data
A comprehensive tool for almost all existing multiple testing methods for discrete data. The package also provides some novel multiple testing procedures controlling FWER/FDR for discrete data. Given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha and make decisions. For users' convenience, the functions also provide the output option for printing decision rules.
Maintained by Yalin Zhu. Last updated 6 years ago.
adjustment-computationsbenjamini-hochbergbonferronidiscrete-distributionsmultiple-testing-correction
15.5 match 1 stars 3.27 score 37 scriptsbioc
safe:Significance Analysis of Function and Expression
SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions.
Maintained by Ludwig Geistlinger. Last updated 5 months ago.
differentialexpressionpathwaysgenesetenrichmentstatisticalmethodsoftware
6.0 match 5.60 score 32 scripts 5 dependents
allenzhuaz
FixSeqMTP:Fixed Sequence Multiple Testing Procedures
Several generalized / directional Fixed Sequence Multiple Testing Procedures (FSMTPs) are developed for testing a sequence of pre-ordered hypotheses while controlling the FWER, FDR and Directional Error (mdFWER). All three FWER controlling generalized FSMTPs are designed under arbitrary dependence, which allow any number of acceptances. Two FDR controlling generalized FSMTPs are respectively designed under arbitrary dependence and independence, which allow more but a given number of acceptances. Two mdFWER controlling directional FSMTPs are respectively designed under arbitrary dependence and independence, which can also make directional decisions based on the signs of the test statistics. The main functions for each proposed generalized / directional FSMTPs are designed to calculate adjusted p-values and critical values, respectively. For users' convenience, the functions also provide the output option for printing decision rules.
Maintained by Yalin Zhu. Last updated 6 years ago.
multiple-testingpre-ordersequential-testing
8.3 match 3 stars 3.22 score 11 scriptsallenzhuaz
MHTmult:Multiple Hypotheses Testing for Multiple Families/Groups Structure
A Comprehensive tool for almost all existing multiple testing methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.
Maintained by Yalin Zhu. Last updated 3 years ago.
hierarchical-datamultiple-testingmultiplicity
6.7 match 2.70 score 9 scripts
leelabsg
SKAT:SNP-Set (Sequence) Kernel Association Test
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.
Maintained by Seunggeun (Shawn) Lee. Last updated 1 months ago.
1.7 match 45 stars 9.70 score 268 scripts 16 dependents
modal-inria
MLGL:Multi-Layer Group-Lasso
It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).
Maintained by Quentin Grimonprez. Last updated 2 years ago.
3.7 match 3 stars 3.61 score 27 scriptsbioc
stageR:stageR: stage-wise analysis of high throughput gene expression data in R
The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0
Maintained by Koen Van den Berge. Last updated 5 months ago.
1.9 match 5.72 score 87 scriptsbioc
IHW:Independent Hypothesis Weighting
Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.
Maintained by Nikos Ignatiadis. Last updated 5 months ago.
immunooncologymultiplecomparisonrnaseq
1.3 match 7.25 score 264 scripts 2 dependents
cran
PlatformDesign:Optimal Two-Period Multiarm Platform Design with New Experimental Arms Added During the Trial
Design parameters of the optimal two-period multiarm platform design (controlling for either family-wise error rate or pair-wise error rate) can be calculated using this package, allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) <doi:10.2307/2281208>.
Maintained by Xiaomeng Yuan. Last updated 2 years ago.
4.3 match 2.00 scorecran
TestCor:FWER and FDR Controlling Procedures for Multiple Correlation Tests
Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Maintained by Gannaz Irene. Last updated 4 years ago.
8.4 match 1 stars 1.00 score
s3alfisc
wildrwolf:Fast Computation of Romano-Wolf Corrected p-Values for Linear Regression Models
Fast Routines to Compute Romano-Wolf corrected p-Values (Romano and Wolf (2005a) <DOI:10.1198/016214504000000539>, Romano and Wolf (2005b) <DOI:10.1111/j.1468-0262.2005.00615.x>) for objects of type 'fixest' and 'fixest_multi' from the 'fixest' package via a wild (cluster) bootstrap.
Maintained by Alexander Fischer. Last updated 1 years ago.
fixestmultiple-comparisonsromano-wolfwild-bootstrapwild-cluster-bootstrap
1.6 match 7 stars 3.59 score 37 scripts 1 dependents
yuepan027
scpoisson:Single Cell Poisson Probability Paradigm
Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation.
Maintained by Yue Pan. Last updated 3 years ago.
1.8 match 2.70 score 4 scriptsbioc
multtest:Resampling-based multiple hypothesis testing
Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.
Maintained by Katherine S. Pollard. Last updated 5 months ago.
microarraydifferentialexpressionmultiplecomparison
0.5 match 9.34 score 932 scripts 136 dependents
kornl
mutoss:Unified Multiple Testing Procedures
Designed to ease the application and comparison of multiple hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are standardized and usable by the accompanying 'mutossGUI'.
Maintained by Kornelius Rohmeyer. Last updated 12 months ago.
0.5 match 4 stars 8.44 score 24 scripts 16 dependents
livioivil
someMTP:Some Multiple Testing Procedures
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Maintained by livio finos. Last updated 4 years ago.
1.7 match 2.08 score 9 scripts 4 dependentscran
regrap:Reverse Graphical Approaches
The graphical approach is proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal p-values. A reverse graphical approach starts from a set of singleton graphs, and gradually add vertices into graphs until rejection of a set of hypotheses is made. See Gou, J. (2020). Reverse graphical approaches for multiple test procedures. Technical Report.
Maintained by Jiangtao Gou. Last updated 5 years ago.
3.5 match 1 stars 1.00 scorebioc
hierinf:Hierarchical Inference
Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel.
Maintained by Claude Renaux. Last updated 5 months ago.
clusteringgenomewideassociationlinkagedisequilibriumregressionsnp
0.5 match 4.00 score 2 scriptsbioc
hierGWAS:Asessing statistical significance in predictive GWA studies
Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers.
Maintained by Laura Buzdugan. Last updated 5 months ago.
snplinkagedisequilibriumclustering
0.5 match 3.30 score 1 scripts
phillipmogensen
TMTI:Too Many, Too Improbable (TMTI) Test Procedures
Methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) <doi:10.48550/arXiv.2108.04731>.
Maintained by Phillip B. Mogensen. Last updated 5 months ago.
0.5 match 2.70 scorecran
flip:Multivariate Permutation Tests
It implements many univariate and multivariate permutation (and rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and Mc Nemar. Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters). The permutation and the rotation methods to get the null distribution of the test statistics are available. It also implements methods for multiplicity control such as Westfall & Young minP procedure and Closed Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed effects in mixed effects models.
Maintained by Livio Finos. Last updated 7 years ago.
0.5 match 2.26 score 3 dependents