ERP: Significance analysis of Event-Related Potentials data in R
ERP is an R package which provides multiple testing procedures designed for Event-Related Potentials (ERP) data in a linear model framework. These procedures are reviewed and compared in Sheu, Perthame, Lee, & Causeur (2016). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust (see the R package stats). The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991), which accounts for the auto-correlation among tests to control erroneous detections of short intervals.
The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh, & Sheu (2012). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updtating of the dependence modelling (see Sheu et al., 2016 for further details).
R package available on the CRAN
David Causeur is Professor in the Department of Statistics and Computer Science, Agrocampus, Rennes, France
Ching-Fan Sheu is Professor in the Institute of Education, National Cheng-Kung University, Tainan, Taiwan
Sheu, C.-F., Perthame, E., Lee Y.-S. and Causeur, D. (2016). Accounting for time dependence in large-scale multiple testing of event-related potentials data. Annals of Applied Statistics. Vol. 10 (1). 219-245. (PDF)
Talk at User! 2014, UCLA, July, 3rd, 2014 (PDF slides)
Causeur, D., Chu, M.-C., Hsieh, S. and Sheu, C.-F. (2012) A factor-adjusted multiple testing procedure for ERP data analysis. Behavior Research Methods. 44, 635-643. (link to PDF)