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dfm.io | ||
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dsaber.com
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| | | | | Warning: This is a love story between a man and his Python module As I mentioned previously, one of the most powerful concepts I've really learned at Zipfian has been Bayesian inference using PyMC. PyMC is currently my favorite library of any kind in any language. I dramatically italicized "learned" because I had been taught... | |
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bambinos.github.io
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erikbern.com
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| | | | | I made a New Year's resolution: every plot I make during 2018 will contain uncertainty estimates. Nine months in and I have learned a lot, so I put together a summary of some of the most useful methods. | |
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indrajeetpatil.github.io
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| | | Extension of ggplot2, ggstatsplot creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) . | ||
