`permuco`

This package provides functions to compute permutation tests in linear models with nuisances variables. The package is focused on several goals :

- Providing to users the most recent methods to handle nuisance variables for permutation tests in the linear models.
- Giving to users tools to compute most common tests in linear model (t test, ANOVA and repeated measure ANOVA).
- Providing an extension for the multiple comparisons problems in linear models with a focus for EEG data.

`lmperm()`

functionThis function is constructed as an extension of the the `lm()`

function for permutation test. It produce t statistics with univariate and bivariate pvalue by permutation.

`aovperm()`

functionThis function is constructed as an extension of the the `aov()`

function for permutation test. It produces marginal F statistics (type III). Repeated measures anova are feasible using the same notations used in an `aov()`

formula with `+Error(id/within)`

to specify the random effects.

`clusterlm()`

functionThis function compute cluster-mass statistics for multiple comparisons. It is designed for ERP analysis of unichannel EEG data. The left part of formula object must be a matrix or dataframe which columns represents multiple responses tested on the same experimental design (specified by right part of the formula). This function provides several methods to handle nuisance variables, a F or t statistics, an extension for repeated measure anova and several methods for the multiple comparisons lit the threshold-free cluster enhancement.

If you need help to use the package or want to report errors, contact Jaromil Frossard at [email protected].

For permutation tests with nuisance variables :

- Kherad-Pajouh, S., & Renaud, O. (2010). An exact permutation method for testing any effect in balanced and unbalanced fixed effect ANOVA. Computational Statistics & Data Analysis, 54(7), 1881-1893.
- Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92, 381-397.

For permutation test in repeated measure ANOVA :

- Kherad-Pajouh, S., & Renaud, O. (2015). A general permutation approach for analyzing repeated measures ANOVA and mixed-model designs. Statistical Papers, 56(4), 947-967.

For cluster-mass statistics for the muliple comparison problems :

- Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods, 164(1), 177-190.

For the Threshold-free cluster enhancement method :

- Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage, 44(1), 83-98.

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