A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of long...
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
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Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0251194 |