My R package 'powerlmm' has now been update to version 0.2.0. It contains several improvements, and new features.

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Non-randomized comparisons are common in RCTs. In this post I show some examples of confounding and collider bias, using treatment adherence as an example. I present a small simulation study that show that common regression models used in clinical psychology, makes little sense, and that Bayesian instrumental variable regression can be easily fit using the R package brms.

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In this post I compare the performance of Amazon EC2 instances vs my HP workstation and my MacBook Pro, when doing Monte Carlo simulations.

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Over the summer I've been working on finishing my new R package 'powerlmm', which is now almost complete. It provides flexible power calculations for typical two- and three-level longitudinal linear mixed models, with unbalanced treatment groups and cluster sizes, as well as with missing data and random slopes at both the subject and cluster-level.

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This post explains how Cohen's d relates to the proportion of overlap between two normal distributions, and why I use a different measure then Cohen in my Cohen's d visualization.

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This visualization illustrates the appropriate tests to use when your research hypothesis is that two treatments are equally effective, or that a new treatment is no worse than the current gold standard.

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The visualization illustrates a Bayesian two-sample t test

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I've added more options to my NHST visualization

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My p-curve visualization has been updated with the option to log the x-axis.

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This visualization shows how the t-distribution and the Gaussian (normal) distribution approaches each other when sample size increases.

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