programming

An update on code folding with blogdown + Academic theme

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.

Simulating correlated standardized mean differences for meta-analysis

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.

Code folding with blogdown + Academic theme

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.

CRAN downloads of my packages

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.

Bug in nlme::lme with fixed sigma and REML estimation

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().

Simulation studies in R (Fall, 2016 version)

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.

Bug in nlme::getVarCov

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.

Assigning after dplyr

Hadley Wickham’s dplyr and tidyr packages completely changed the way I do data manipulation/munging in R. These packages make it possible to write shorter, faster, more legible, easier-to-intepret code to accomplish the sorts of manipulations that you have to do with practically any real-world data analysis.

Update: parallel R on the TACC

I have learned from Mr. Yaakoub El Khamra that he and the good folks at TACC have made some modifications to TACC’s custom MPI implementation and R build in order to correct bugs in Rmpi and snow that were causing crashes.

Running R in parallel on the TACC

UPDATE (4/8/2014): I have learned from Mr. Yaakoub El Khamra that he and the good folks at TACC have made some modifications to TACC’s custom MPI implementation and R build in order to correct bugs in Rmpi and snow that were causing crashes.