Bayesian Repeated-Measures ANOVA: An Updated Methodology Implemented in JASP

This post is a teaser for van den Bergh, D., Wagenmakers, E., & Aust, F. (2022). Bayesian Repeated-Measures ANOVA: An Updated Methodology Implemented in JASP. Preprint available on PsyArXiv: https://psyarxiv.com/fb8zn/ In JASP 0.16.3 we changed the default Bayesian repeated-measures ANOVA. It is important to understand this change as it may affect the results, bringing them more in line with the…

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Generalized Linear Models (GLM) in JASP

It took a while, but finally, the frequentist Generalized Linear Model (GLM) has become available in JASP, as part of the Regression module! In this blog post, we give you a quick introduction to the idea behind GLM and the full functionality of this new JASP sub-module. We also show you how you can conduct a binomial regression analysis using…

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Introducing JASP 0.16.3: Quality Control, GLMs, Bayesian State Space Models, Improvements to Bayesian ANOVA, and More

We are happy to announce that JASP 0.16.3 has been released and is now available on our download page. JASP 0.16.3 contains the following new features and improvements: The new module Quality Control has been added which you can use to investigate if a manufactured product adheres to a defined set of quality criteria. The newly added module for Bayesian…

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Teaching The Problem of Points with JASP

The field of statistics and probability theory was born around 1654, in a famous correspondence between Blaise Pascal and Pierre de Fermat. These two French mathematicians concerned themselves with a problem in gambling: suppose two players A and B are engaged in a match – for concreteness, suppose they are repeatedly tossing a fair coin. Whenever the coin lands heads,…

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Bayesian Modeling for Cognitive Science: A Workshop in Amsterdam

We are delighted to announce that registration is now open for the annual Amsterdam workshop on probabilistic modelling for cognitive science. The tenth instalment of this workshop takes place August 22–26, 2022. Program In this workshop, plenary lectures provide the theoretical background of Bayesian statistics, and practical computer exercises teach participants how to use the popular JAGS and Stan programs…

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Theory and Practice of Bayesian Hypothesis Testing: A Hybrid JASP Workshop in Amsterdam

The JASP Team is excited to announce that registration for our annual two-day hybrid Amsterdam workshop is now open. The workshop will be happening both on-site and via Zoom and it takes place on August 29–30, 2022. Program In this two-day workshop, plenary lectures provide the theoretical background of Bayesian statistics and Bayesian hypothesis testing; in addition, practical exercises demonstrate…

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Meta-Analysis of Prediction Model Performance

It is highly recommended to evaluate the performance of prediction models across different study populations, settings, or locations since good performance is essential for proper decision making regarding patients’ health (Debray et al., 2015). When multiple estimates of prediction model performance are available (e.g. from the published literature), meta-analysis may help to obtain a summary estimate and investigate the presence…

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How to Predict with Machine Learning Models in JASP: Classification

This blog post will demonstrate how a machine learning model trained in JASP can be used to generate predictions for new data. The procedure we follow is standardized for all the supervised machine learning analyses in JASP, so the demonstration here generalizes to all of them. Please note that we use the latest version of JASP (version 0.16.2). For our…

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Introducing JASP 0.16.2: Performance Improvements, Apple Silicon, and Bug Fixes

JASP has been updated! This is a maintenance release that contains the following improvements: Performance improvements on both Mac and Windows resulting from upgrades to our development framework (Qt 6.2, R 4.1.3) There is a new build for Apple Silicon (e.g., M1 iMacs and Macbooks) that makes use of Apple’s new chipset resulting in a significant speed up. The installation…

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Latent Growth Curve Modeling (LGCM) in JASP

‘How can we model the form of change in an outcome as time passes by?’, ‘Which statistical technique helps us to describe individual growth trajectory’s over time?’, ‘Can individual differences in an initial state and in change over time be analyzed?’ These questions are of importance to researchers who examine developmental, longitudinal, or consecutive measurements across multiple occasions. What solves…

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Introducing JASP 0.16.1: French Translation, Cochrane Meta-Analysis, Decision Trees, and More.

We are happy to announce that JASP 0.16.1 has been released and is now available on our download page. JASP 0.16.1 contains the following new features and improvements: JASP is now available in French (incomplete). The Machine Learning module has been expanded so that you can now perform (1) Support Vector Machine regression and classification analyses, and (2) Decision Tree…

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