## Articles with the statistics tag

My R package 'powerlmm' has now been update to version 0.3.0. It adds support for a more flexible effect size specifiation.

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This post contains the slides from a talk I gave recently at Stockholm University

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

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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 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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I often get asked about how to fit different longitudinal models in lme/lmer. In this post I cover several different two-level, three-level and partially nested models.

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