Published May 28, 2025
| Version 0.92
Software documentation
Open
Gender and age matter! Identifying important predictors for subjective well-being using machine learning methods
Authors/Creators
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1.
Leibniz Institute of Ecological Urban and Regional Development
Description
Software to reproduce the results from the manuscript: Samartzidis, L., Quatrosi, M. & von Dulong, A. Gender and Age Matter! Identifying Important Predictors for Subjective Well-being Using Machine Learning Methods. Soc Indic Res (2025). https://doi.org/10.1007/s11205-025-03643-5
Files
wellbeing_ml_zenodo.zip
Files
(570.3 MB)
| Name | Size | Download all |
|---|---|---|
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md5:95ab456024e4ada4d4d9a22a393a37f7
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570.3 MB | Preview Download |
Additional details
Related works
- Is supplement to
- Journal article: 10.1007/s11205-025-03643-5 (DOI)
Dates
- Created
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2023-12-28
Software
- Programming language
- R