DABEST and permuco
This week’s post is about two very useful libraries for statistical evaluations of behavioral, neuronal, and other types of data, especially when the data deviate from assumptions such as having a Gaussian (aka normal) distribution. (This is often the case with behavioral data.) We hope that you find it useful and the overall awareness of these tools increases.
is a cross-platform library (Python, R, Matlab) for using estimation stats, developed by and . The approach is a combination of graphics-driven exploratory data analysis, bootstrapped confidence intervals, and permutation (aka randomization) tests. There is extensive documentation of and . Methods exist for the Unpaired Student’s t-test (), the Paired Student’s t-test (), One-way ANOVA + multiple comparisons (), Repeated measures ANOVA (), and Ordered groups ANOVA (). In our experience, DABEST works well for typical datasets from behavioral experiments and little time was needed to incorporate the approach into our data flow. We were literally using the library effectively within a morning given the quality of the documentation.
Another useful library for data analysis is permuco. It was developed by and , and is available on and . This is a library for R. (It can be used seamlessly in Python notebooks and scripts using the very handy library.) permuco allows for permutation testing in regression, ANOVA with interactions among predictors, and comparisons over signals such EEG (or LFP) over time and experimental conditions. A detailed reference manual and vignette are available on Similar to DABEST, we have been able to use permuco effectively in a very short period of time, especially because it uses syntax that is common to stock R functions such as aov and lm.
This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications. DABEST RRID:SCR_022340; Permuco RRID:SCR_022341
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