The JASP Blog

Starting out in Bayesian statistics can be daunting. Learners are confronted with new terms in abundance: prior distribution, posterior distribution, Bayes factor, marginal likelihood, credible interval – just to name a few. If you are all on your own, it can be challenging to get a good grasp of all the important concepts. At JASP, we are committed to increase…

The JASP programming team has made it easier for new code contributors to add functionality. Specifically, the team has created components in the scripting language qml that make it relatively straightforward to design your own GUI including boxes for the variables, radio buttons, tick boxes, etc. Examples are given in the first part of the JASP YouTube video available here. Associated…

The only thing worse than statistical software that does not work, is statistical software that appears to work but produces incorrect output. For this reason, verifying the computed results of statistical software is serious business – also for JASP. This is why in July of 2020 we started a systematic verification process. Beginning with the most commonly used frequentist analyses,…

We are happy that JASP 0.14 has been downloaded over 99877 times since its release on October 14th! JASP 0.14.1 adds some improvements and fixes small issues. Some of the improvements are: Several changes to the audit module Evaluate audit samples more efficiently #4411 More prior construction methods #700 UVB functionality #1149 More computing speed for RoBMA #4408 Previously the…

This is a guest post by Tom Faulkenberry (Tarleton State University). Click here to access the supplementary materials. Amid the COVID-19 pandemic, universities have needed to quickly adjust their traditional methods of instruction to allow for maximum flexibility. This means that professors have also had to think critically about how they can best deliver instruction in new formats. Ever the…

To facilitate the transition to Bayesian inference we recently created the “Learn Bayes module in JASP” (with support from a grant from the APS Fund for Teaching and Public Understanding of Psychological Science). The goal of this module is to illustrate all steps of Bayesian parameter estimation and testing. In the previous blogpost we outlined the Binomial Estimation analysis, which…

This is a guest post by Calvin Deans-Browne (UCL) and Henrik Singmann (UCL). Click here to access the supplemental materials. The Setup This article contains an introduction to the different indices measured in the signal detection theory (SDT) framework, a case study to put them into context, and how to compute them in JASP. An Introduction to SDT Indices Signal…

JASP 0.14 brings robust Bayesian meta-analysis (RoBMA). This extension of Bayesian meta-analysis allows researchers to adjust for publication bias when conducting model-averaged meta-analysis. RoBMA applies a set of twelve models simultaneously, some assuming publication bias and some assuming no publication bias. The inference will then be based most strongly on the models that predicted the data best. In other words,…

JASP 0.14 has been released and is now available on our download page. JASP 0.14 contains the following new features and improvements: Publication bias-adjusted meta-analysis. JASP version 0.14 adds frequentist selection models as well as Robust Bayesian meta-analysis to correct for publication bias. Head over to our tutorial videos and a tutorial paper to learn how to use these analyses.…

JASP 0.13.1 fixes several small issues. Specifically: Previously, our new “save as powerpoint” option for figures did not work on MacOS. Now it does. For some time, JASP was unstable on MacOS with Intel Iris GPU. This has now been fixed. Windows users with a “foreign” character in their username experienced problems. We have resolved this. A bug concerning changing…