Logiciels et données relatifs aux articles de Olivier Renaud


matlab toolbox for Multiscale Granger Causality: follow this link

For more information on the method or reference to cite:

Cekic, S., Grandjean, D., & Renaud, O. (2019). Multiscale Bayesian state-space model for Granger causality analysis of brain signal. Journal of Applied Statistics, 46(1), 66–84.




Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals: R package permuco available on cran:

Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. See the "Vignette":

Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).



Companion code in R sofware for a tutorial on robustness

Courvoisier, D. S. and Renaud, O. (2010), Robust analysis of the central tendency, simple and multiple regression and ANOVA: A step by step tutorial , International Journal of Psychological Research 3(1), 78--87. Available online

List of instructions/lines of calls for the methods presented in the article(right-click and select "Save as"). Note that the version on the editor webpage is an incorrect version that was not updated by the editor.

First dataset used: cigArt.csv

Second dataset used: creatArt.csv


This page used to contain a companion code in R for sofware for a robust coefficient of determination (or R^2)

Renaud, O. and Victoria-Feser, M.-P. (2010), A robust coefficient of determination for regression , Journal of Statistical Planning and Inference 140, 1852--1862. DOI: 10.1016/j.jspi.2010.01.008

However this code is now included (and improved) in the function lmrob and its summary (from the "robustbase" package). It is the implementation we highly recommand. See an example of use below:

> ## If not present, install the package 
> install.packages("robustbase")
> ## active it
> library(robustbase)
> ## data to be used:
> data(coleman)
> ## run the robust linear regression
> col2lmrob = lmrob(Y ~salaryP+motherLev, data=coleman, setting = "KS2014")
> ## get the output, including the 2 proposed robust R2 under "Multiple R-squared" and "Adjusted R-squared"
> summary(col2lmrob)

lmrob(formula = Y ~ salaryP + motherLev, data = coleman, setting = "KS2014")
 \--> method = "SMDM"
    Min      1Q  Median      3Q     Max
-8.5458 -1.9686  0.0873  2.4699  6.4041

            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -5.2269    10.6118  -0.493 0.628629    
salaryP       0.5099     2.2364   0.228 0.822370    
motherLev     6.2439     1.5613   3.999 0.000929 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Robust residual standard error: 4.266
Multiple R-squared:  0.509,    Adjusted R-squared:  0.4512
Convergence in 11 IRWLS iterations