\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \]
For a project I am working on, we are using Stan to fit generalized random effects location-scale models to a bunch of count data.

\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \def\bm{\mathbf} \def\bs{\boldsymbol} \]
For a project I am working on, we are using Stan to fit generalized random effects location-scale models to a bunch of count data.

In this post, we will sketch out what we think is a promising and pragmatic method for examining selective reporting while also accounting for effect size dependency. The method is to use a cluster-level bootstrap, which involves re-sampling clusters of observations to approximate the sampling distribution of an estimator. To illustrate this technique, we will demonstrate how to bootstrap a Vevea-Hedges selection model.

UPDATED November 21, 2020. Thanks to Allen O’Brien for pointing out a bug in the codefolding code, which led to the last code chunk defaulting to hidden rather than open.

As I’ve discussed in previous posts, meta-analyses in psychology, education, and other areas often include studies that contribute multiple, statistically dependent effect size estimates. I’m interested in methods for meta-analyzing and meta-regressing effect sizes from data structures like this, and studying this sort of thing often entails conducting Monte Carlo simulations.

2020-05-03 This post describes an implementation of code folding for an older version of the Academic Theme. It does not work with Academic 4.+. See my updated instructions to get it working with newer versions of Academic.

At AERA this past weekend, one of the recurring themes was how software availability (and its usability and default features) influences how people conduct meta-analyses. That got me thinking about the R packages that I’ve developed, how to understand the extent to which people are using them, how they’re being used, and so on.

About one year ago, the nlme package introduced a feature that allowed the user to specify a fixed value for the residual variance in linear mixed effect models fitted with lme().

In today’s Quant Methods colloquium, I gave an introduction to the logic and purposes of Monte Carlo simulation studies, with examples written in R.
Here are the slides from my presentation.

I have recently been working to ensure that my clubSandwich package works correctly on fitted lme and gls models from the nlme package, which is one of the main R packages for fitting hierarchical linear models.

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