ERP
Significance Analysis of Event-Related Potentials Data
Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, doi:10.1214/15-AOAS888). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 doi:10.1111/j.1469-8986.1991.tb00417.x), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, doi:10.3758/s13428-012-0230-0). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, doi:10.1214/15-AOAS888 for further details).
- Version2.2
- R version≥ 3.0.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release12/13/2019
Documentation
Team
David Causeur
Ching-Fan Sheu
Show author detailsRolesAuthorMei-Chen Chu
Show author detailsRolesAuthorFlavia Rufini
Show author detailsRolesAuthor
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Last 30 days
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Last 365 days
This package has been downloaded 2,443 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 31 downloads.
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- Imports5 packages
- Suggests3 packages