lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data

MOTIVATION: Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can model complicated covariance structures and detect non-linear effects of both categorical and continuous cov...

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Detalles Bibliográficos
Autores principales: Timonen, Juho, Mannerström, Henrik, Vehtari, Aki, Lähdesmäki, Harri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317115/
https://www.ncbi.nlm.nih.gov/pubmed/33471072
http://dx.doi.org/10.1093/bioinformatics/btab021

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317115/
https://www.ncbi.nlm.nih.gov/pubmed/33471072
http://dx.doi.org/10.1093/bioinformatics/btab021

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