R PACKAGE psycholing

We're putting together an R package with functions for performing standard data processing tasks for many common experimental designs in psycholinguistics and cognitive psychology and for coding and interpreting the results for linear mixed-effects models.

Here's how you can grab and use the current development version of the package:

  1. Install the devtools package if you haven't already. It's available on CRAN.
  2. In R, type devtools::install_github('sfraundorf/psycholing') to both download and install the package in one step.
  3. That's it! You now have the psycholing package installed. You can load it up with library(psycholing) and use it just like any other R package.

You can also check out the GitHub repository for the package.


By popular demand, here's a Web-based version of Scott's course in linear mixed-effect models, updated for the fall 2020 term. Disclaimer: These lecture notes are from a previous term, so there's a possibility they no longer reflect current practice in the field.

  1. Introduction to multi-level / mixed effects models
  2. Descriptive statistics in R
  3. Data processing in R
  4. Fixed effects
  5. Fixed-effect interactions and outliers
  6. Model comparison
  7. Random intercepts
  8. Level-2 variables
  9. Random slopes
  10. Crossed random effects
  11. Centering and other transformations
  12. Contrast coding for variables with two categories & "Main effects" vs. "simple effects"
  13. Post-hoc comparisons
  14. Contrast coding for variables with more than two categories & orthogonality
  15. Logit models for categorical outcomes
  16. Empirical logit and Poisson models
  17. Dealing with missing data
  18. Longitudinal and time-series data: Growth curve analysis
  19. Longitudinal and time-series data: Autocorrelation
  20. Crossed-lagged designs and effect size
  21. Statistical power
  22. Signal detection theory