Introducing JASP 0.16 – Predict with ML Models, Neural Networks, New Techniques for Meta-Analysis, and More.

We are happy to announce that JASP 0.16 has been released and is now available on our download page. JASP 0.16 contains the following new features and improvements:

  • The Meta-Analysis module now includes popular adjustment techniques for publication bias and small study effects: (1) PET-PEESE

  • and (2) WAAP-WLS.

  • Another addition to the Meta-Analysis module is Prediction Model Performance. It is an implementation of the metamisc package which provides analysis of diagnosis and prognosis research studies.

  • The Machine Learning module has been expanded so that you can now (1) perform Neural Network regression and classification analyses,

  • (2) save your models as .jaspML files,

  • (3) and load .jaspML files to apply to new data in the new Prediction analysis.

  • Inspired by the work of Zoltan Dienes, General Bayesian Tests has been added to the Summary Statistics module. The General Bayesian Tests allows one to test a hypothesis about a parameter for various likelihood functions. It has implemented the bayesplay package.

These are just a few highlights — JASP 0.16 contains much more. For a complete list of features, improvement and bug fixes view our release notes.

About the author

Dana Sleiffer

Dana is a Research Master student in Psychology at the University of Amsterdam.