Introducing JASP 0.17: Syntax Mode, Keyboard Navigation, Acceptance Sampling and More!

We are happy to announce that JASP 0.17 has been released and is now available on our download page page. JASP 0.17 contains the following new features and improvements:

  • JASP now has syntax mode. This allows users to view the functions that were used to perform an analysis. These functions can be edited to change settings in the analysis; doing so will directly impact the options shown in the GUI. This means that syntax and GUI work in tandem. In addition, the code can be emailed and copy-pasted in the JASP R console to reproduce the original analysis. This is a first version of syntax mode that we will expand on in the next few versions. Specifically, syntax mode is currently not yet available for the JASP add-on modules. Also, syntax mode currently works inside JASP only; more work is needed before the code will work correctly in R Studio.


  • Keyboard functionality has been implemented. This includes implementation of the ALT-key, which allows users to navigate the major UI elements, such as analyses, the modules and menus. To use, press the “ALT” key (”Option” key on Mac), which will display tags on top of elements that indicate how to select each of them with the keyboard. In addition, tab-orders within analyses have been fixed.


  • The acceptance sampling module has been added. This module allows users to create and analyze lot sampling plans. Features include the generation of OC curves, AOQ/ATI/ASN curves, plan summaries, and plan assessments against specific constraints. Currently, the module supports the creation of single stage attributes and variable sampling plans. It also supports the analysis of single and multi-stage attribute plans, and single-stage variable plans. Finally, users can use data from variable measurements to decide whether or not to accept a lot.


  • The JAGS module has been expanded with Customizable Inference. This is useful when a model contains large parameter vectors. For example, in an hierarchical model where an ability parameter is estimated for every participant, customizable inference allows you to explore the posterior distribution of ability for (a subset of) the participants.

  • Bayesian networks have been added to the Network Analysis module. This addition allows users to quantify evidence for the absence of connections between nodes, model-average across network structure, and seamlessly update knowledge as the data accumulate. A tutorial on the implemented Bayesian networks is available at (Huth et al., 2023).
  • Bayesian Penalized Meta-Analysis is a new analysis added to the Meta-Analysis module. It allows users to estimate lasso and ridge penalized (3-level) meta-regression models. See Van Lissa, van Erp & Clapper (2021) for more details.
  • Plots can now be saved in .pptx, .pdf, and .eps format.
  • In Descriptives, we have now added confidence intervals for the sample mean, standard deviation, and variance.

These are just a few highlights — JASP 0.17 contains much more. For a complete list of features, improvements and bug fixes view our release notes.



Huth, K., de Ron, J., Goudriaan, A., Luigjes, J., Mohammadi, R, van Holst, R.,Wagenmakers, E.-J., & Marsman, M. (2023). Bayesian analysis of cross-sectional networks: A tutorial in R and JASP. Manuscript submitted for publication.

Van Lissa, C. J., van Erp, S., & Clapper, E. (2021). Selecting relevant moderators with Bayesian regularized meta-regression.