# Resources

## Article tutorials

- Multiple Methods for Visualizing Human Language: A Tutorial for Social and Behavioural Scientists

  OSF preprint

- The text package tutorial

  Full text on PsycNET.

  OSF preprint.

  Tutorial code

- The Language-Based Assessment Model (L-BAM) Library

  OSF preprint

  Tutorial code

## Selected published articles advancing the text package

- Nilsson, A. H., Runge, J. M., Ganesan, A. V., Lövenstierne, C. V. N., Soni, N., & Kjell, O. N. (2025).
  Automatic implicit motive codings are at least as accurate as humans’ and 99% faster.
  Journal of Personality and Social Psychology.
  Article link

- Gu, Z., Kjell, K., Schwartz, H. A., & Kjell, O. (2025).
  Natural language response formats for assessing depression and worry with large language models: A sequential evaluation with model pre-registration.
  Assessment, 10731911251364022.
  Article link

- Nilsson, A. H., Eichstaedt, J. C., Lomas, T., Schwartz, A., & Kjell, O. (2024).
  The Cantril Ladder elicits thoughts about power and wealth.
  Scientific Reports, 14(1), 2642.
  Article link

## Blog posts and tutorials

- Implicit Motives Tutorial

- Installing and Managing Python Environments with `reticulate`

- Creating a Singularity Container to Run HuggingFace Transformers Models in R

- How to Best Manage Computationally Heavy Analyses

- HuggingFace Language Models Are Downloaded in `.cache`

- HuggingFace Transformers in R: Word Embeddings Defaults and Specifications

- Pre-registration and Researcher Degrees of Freedom