TquanT Shiny Apps Gallery

This site contains Shiny apps produced in the TquanT project. For educational purposes, the source code is shown with the apps.

More apps have been developed by students at the TquanT seminars. Furthermore, this site offers some Rmarkdown presentations held at the TquanT seminars by Francis Tuerlinckx.

Bayesian Statistics & Computation

Bayesian statistics - the frog example - an extended version of a student app: An introduction to Bayesian statistics

Bayesian Correlation Analysis: The "Bayesian correlation analysis" app shows how the results of Bayesian procedures vary in the light of changes regarding data and prior beliefs.

Bayesian t-Test Teaching App: The goal of the Bayesian t-test app is to provide teachers with a handy tool to show students what a Bayes factor, and more generally, what the results from a Bayesian t-test look like when data points are added in real-time.

P-values & Bayes factors: This app illustrates the relation between p values and Bayes factors for various widely-used statistical tests of hypotheses.

James-Stein estimator: The goal of this shiny app is to visualize the effect of shrinkage estimators and compare their performance to other estimators.

Sequential testing with p-values and Bayes factors This app illustrates the relation between p-values and Bayes factors for various statistical tests under sequential and block sampling procedures.

Knowledge Space Theory

The Skill mApp aims to provide a first contact with both the Knowledge Space Theory and the skill map theory.

Local independence: This app exemplifies the local independence assumption of the basic local independence model.

Parameter estimation: This app uses three procedures to estimate the parameters for a data set and a knowledge structure specified by the user.

Classical and Bayesian parameter estimation: This app demonstrates classical and Bayesian parameter estimation methods for probabilistic knowledge structures.

Identifiability: This app visualizes the trade-off between the parameters of a non-identifiable basic local independence model (BLIM).

Surmise Relations: This app demonstrates surmise relations and the corresponding knowledge spaces.

Probabilistic Knowledge Assessment: This app lets you build your own knowledge structure on a set of five items on elementary probability theory, and lets you perform a probabilistic knowledge assessment on that structure. Especially the UI is based on a students' app.

Deterministic Knowledge Assessment: This app lets you build your own knowledge structure on a set of five items on elementary probability theory, and lets you perform a deterministic knowledge assessment on that structure. It is derived from the app on probabilistic knowledge assessment.

BLIM simulation: This app lets you simulate response patterns based on a given knowledge space using the BLIM.

Properties of Knowledge Spaces: This app lets you enter a knowledge structure and shows various properties of the corresponding knowledge space.

Precedence Relation and Corresponding Quasi-Ordinal Knowledge Space: This app illustrates the one-to-one correspondence between a precedence relation among a number of problems and a quasi-ordinal knowledge space.

Fringe & Neighbourhood: This app illustrates the fringe and neighbourhood of knowledge states.

Animated Learning Paths: This app illustrates learning paths in a knowledge structure.

Validating Knowledge Structures: This app applies several validation coefficients to a selection of knowledge structures and respective data. It is based on a student app developed at the 2018 TquanT seminar in Glasgow.

Multisensory Signals

Race Model: An app introducing and illustrating the race model for multisensory signals.

TWIN: Time Window of Multisensory INtegration.

TWIN 2017: A Shiny App for visualizing, simulating and estimating the Time-Window of INtegration (TWIN) model (version 2017).

Miscellaneous

Comparing Intraclass Correlations for Schwarz Values across European Countries.

Multiverse analysis: This app shows how different choices in constructing the data leads to different analysis results.


TquanT is co-funded by the Erasmus+ Programme of the European Commission.

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