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Addiction Research & Theory Volume 31, 2023 - Issue 3
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Commentaries

Reply to Billieux and Fournier (2022): collaborative shortcut to ontological diversity

Veli-Matti Karhulahtia Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, FinlandCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-3709-5341View further author information
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Matúš Adamkoviča Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, Finland;b Department of Psychology, University of Presov, Presov, SlovakiaORCID Iconhttps://orcid.org/0000-0002-9648-9108View further author information
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Jukka Vahloa Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, Finland;c Centre for Collaborative Research, University of Turku, Turku, FinlandORCID Iconhttps://orcid.org/0000-0001-5835-5945View further author information
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Marcel Martončika Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, Finland;d Institute of Social Sciences, CSPS SAS, Košice, SlovakiaORCID Iconhttps://orcid.org/0000-0003-4869-6900View further author information
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Matti Munukkaa Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, FinlandORCID Iconhttps://orcid.org/0000-0003-3101-466XView further author information
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Raine Koskimaaa Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, FinlandORCID Iconhttps://orcid.org/0000-0002-1492-4074View further author information
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Mikaela von Bonsdorffa Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, Finland;e Folkhälsan Research Center, Helsinki, FinlandORCID Iconhttps://orcid.org/0000-0001-8530-5230View further author information
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Pages 174-177 | Received 03 Dec 2022, Accepted 15 Dec 2022, Published online: 28 Dec 2022

Figures & data

Figure 1. Visualization of the networks. A = Original data, ggmModSelect estimator, tuning = 0; B = Original data, EBICglasso estimator, tuning = .25; C = Dichotomized data, IsingFit estimator, tuning =.25; D = Non-paranormal transformation, ggmModSelect estimator, tuning = 0; E = Non-paranormal transformation, EBICglasso estimator, tuning =.25.

Figure 1. Visualization of the networks. A = Original data, ggmModSelect estimator, tuning = 0; B = Original data, EBICglasso estimator, tuning = .25; C = Dichotomized data, IsingFit estimator, tuning =.25; D = Non-paranormal transformation, ggmModSelect estimator, tuning = 0; E = Non-paranormal transformation, EBICglasso estimator, tuning =.25.

Data availability statement

The data and materials used in this study are openly available in OSF: https://osf.io/v4cqd/.

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