This page contains Shiny apps produced by the consortium partners in the QHELP project.
There is also a presentation by Fabian Dablander available.
For educational purposes, the source code is shown with the apps.
More apps and presentations have been developed by students in the QHELP seminars. |
You may also want to look at the Shiny apps produced in the predecessor project, TquanT. |
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Linear Regression:A learnr tutorial.
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LexOPS finds characteristics of words from large corpora to be used in lexical studies.
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Validating BLIM-simulated Data: This app does a series of BLIM simulations and shows the dependence of the Discrepancy Index from the noise. The app was inspired by a student app from the 2019 seminar.
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Conditional Testing
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Properties of Relations: This app allows you to define an
arbitrary binary relation on a set of five items and shows you whether certain
properties are fulfilled.
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Modeling PISA Data by Clusters: An illustration of statistical
learning and visualisation techniques using real-world data (PISA).
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Distributions: An illustration of the effect
of violations of assumptions for the F distribution.
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QQplot: This Shiny app is meant to let you play around with a few different distributions (Normal, Skew Normal, Cauchy, Skew Cauchy), and check what the effect of different variables (scale, location and shape) on their qq-plot is.
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Modeling the 2018 PISA Dataset based on PCA and Clustering: A series of interactive tutorials introducing principle component analysis, clustering, linear modelling and cross-validation for large datasets.
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Learning Paths: A revision and extension
of an app from the TquanT
project.
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Reliability: How would your house look like if the
carpenter building the windows is not fully reliable?
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n-Neighbourhood: Illustrating neighbourhoods
in knowledge structures (inspired by the Fringe app from the TquanT project).
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Tutorial on Knowledge Space Theory: A learnr tutorial
including several R Shiny Apps developed within the QHELP and TquanT.
projects.
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Similarity Assessment: An app for automatic grading of assignments.
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R & Shiny: An R refresher and a Shiny introduction, together with exercises.
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