Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_statistics_montecarlo”.

  FT_STATISTICS_MONTECARLO performs a nonparametric statistical test by calculating
  Monte-Carlo estimates of the significance probabilities and/or critical values
  from the permutation distribution. This function should not be called
  directly, instead you should call the function that is associated with the
  type of data on which you want to perform the test.
  Use as
    stat = ft_timelockstatistics(cfg, data1, data2, data3, ...)
    stat = ft_freqstatistics    (cfg, data1, data2, data3, ...)
    stat = ft_sourcestatistics  (cfg, data1, data2, data3, ...)
  Where the data is obtained from FT_TIMELOCKANALYSIS, FT_FREQANALYSIS
  cfg.method = 'montecarlo'
  The configuration options that can be specified are:
    cfg.numrandomization = number of randomizations, can be 'all'
    cfg.correctm         = string, apply multiple-comparison correction, 'no', 'max', cluster', 'bonferroni', 'holm', 'hochberg', 'fdr' (default = 'no')
    cfg.alpha            = number, critical value for rejecting the null-hypothesis per tail (default = 0.05)
    cfg.tail             = number, -1, 1 or 0 (default = 0)
    cfg.correcttail      = string, correct p-values or alpha-values when doing a two-sided test, 'alpha','prob' or 'no' (default = 'no')
    cfg.ivar             = number or list with indices, independent variable(s)
    cfg.uvar             = number or list with indices, unit variable(s)
    cfg.wvar             = number or list with indices, within-cell variable(s)
    cfg.cvar             = number or list with indices, control variable(s)         = string, 'gui', 'text', 'textbar' or 'no' (default = 'text')
    cfg.randomseed       = string, 'yes', 'no' or a number (default = 'yes')
  If you use a cluster-based statistic, you can specify the following
  options that determine how the single-sample or single-voxel
  statistics will be thresholded and combined into one statistical
  value per cluster.
    cfg.clusterstatistic = how to combine the single samples that belong to a cluster, 'maxsum', 'maxsize', 'wcm' (default = 'maxsum')
                           option 'wcm' refers to 'weighted cluster mass',
                           a statistic that combines cluster size and
                           intensity; see Hayasaka & Nichols (2004) NeuroImage
                           for details
    cfg.clusterthreshold = method for single-sample threshold, 'parametric', 'nonparametric_individual', 'nonparametric_common' (default = 'parametric')
    cfg.clusteralpha     = for either parametric or nonparametric thresholding per tail (default = 0.05)
    cfg.clustercritval   = for parametric thresholding (default is determined by the statfun)
    cfg.clustertail      = -1, 1 or 0 (default = 0)
  To include the channel dimension for clustering, you should specify
    cfg.neighbours       = neighbourhood structure, see FT_PREPARE_NEIGHBOURS
  If you specify an empty neighbourhood structure, clustering will only be done
  over frequency and/or time and not over neighbouring channels.
  The statistic that is computed for each sample in each random reshuffling
  of the data is specified as
    cfg.statistic       = 'indepsamplesT'           independent samples T-statistic,
                          'indepsamplesF'           independent samples F-statistic,
                          'indepsamplesregrT'       independent samples regression coefficient T-statistic,
                          'indepsamplesZcoh'        independent samples Z-statistic for coherence,
                          'depsamplesT'             dependent samples T-statistic,
                          'depsamplesFmultivariate' dependent samples F-statistic MANOVA,
                          'depsamplesregrT'         dependent samples regression coefficient T-statistic,
                          'actvsblT'                activation versus baseline T-statistic.
  or you can specify your own low-level statistical function.
  You can also use a custom statistic of your choise that is sensitive
  to the expected effect in the data. You can implement the statistic
  in a "statfun" that will be called for each randomization. The
  requirements on a custom statistical function is that the function
  is called statfun_xxx, and that the function returns a structure
  with a "stat" field containing the single sample statistical values.
  Check the private functions statfun_xxx (e.g.  with xxx=tstat) for
  the correct format of the input and output.