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The robustness of each branch was estimated by a nonparametric bootstrap procedure implemented in PHYML (100 replicates of the original data set and the same parameters).
The accuracy and robustness of the final population models were assessed by a (nonparametric) bootstrap method (Green and Duffull, 2003), visual predictive check and normalised prediction distribution errors (Brendel et al, 2006; Comets et al, 2008).
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To handle any type of data structure, the detection step is performed by a nonparametric bootstrapping procedure that estimates a probability distribution, according to which the existence of process shifts is determined.
Deviations of divergence times were estimated by a nonparametric bootstrapping method (Baldwin and Sanderson 1998; Sanderson and Doyle 2001).
For each matrix comparison, we assess the statistical significance of any deviation from the null hypothesis by calculating 95% confidence intervals based on a nonparametric bootstrap with 1,000 samples of the empirical contact data.
Using a nonparametric bootstrap on the contact data by participant, 95% percentile confidence intervals for the relative change in R0 can be obtained.
We chose to do this with a nonparametric bootstrap method which involved creating pseudo elements by taking a random sample from all element alignment columns.
In addition, we provide a nonparametric bootstrap test.
The model stability was validated using a nonparametric bootstrap (100 samples with replacement).
Confidence intervals for the model were obtained using a nonparametric bootstrap resampling procedure (n = 10,000 for each Monte Carlo data set).
A nonparametric bootstrap was used to assess robustness of clades, running 2,000 pseudoreplicates.
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