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kgoldfeld.github.io | ||
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www.rdatagen.net
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| | | | | Simulation can be super helpful for estimating power or sample size requirements when the study design is complex. This approach has some advantages over an analytic one (i.e.one based on a formula), particularly the flexibility it affords in setting up the specific assumptions in the planned study, such as time trends, patterns of missingness, or effects of different levels of clustering. A downside is certainly the complexity of writing the code as well as the computation time, which can be a bit painful. My goal here is to show that at least writing the code need not be overwhelming. | |
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austinrochford.com
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| | | | | Splines are a powerful tool when modeling nonlinear relationships. This post shows how to include splines in a Bayesian model in Python using pymc3. In addition, we will show how to use a second splin | |
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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) . | |
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www.airconditionednation.com
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| | | SINGAPORE'S MYSTIFYING POLITICAL SUCCESSION Published in New Mandala. | ||
