Andreas M Brandmaier
researcher
Facts
6 properties- Instance of
- human
- Occupation
- researcher
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en Andreas Brandmaier
en Andreas M Brandmaier
🔖 Identifiers · 8
- ORCID iD
- 0000-0001-8765-6982
- VIAF cluster ID
- 316491530
- GND ID
- 1025794966
- OpenAlex ID
- A5020756502as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- DDB person (GND) ID
- 1025794966
- Deutsche Biographie (GND) ID
- 1025794966
- ResearcherID
- E-9096-2014
- WorldCat Entities ID
- E39PBJhxgqk98BQ6kgwdh983cP
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 11
A Practical Guide to Variable Selection in Structural Equation Modeling by Using Regularized Multiple-Indicators, Multiple-Causes Models,
Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED),
A strong dependency between changes in fluid and crystallized abilities in human cognitive aging,
<b>pdc</b>: An<i>R</i>Package for Complexity-Based Clustering of Time Series,
Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data,
Machine-Learning Approaches to Structural Equation Modeling,
Meta-analysis of generalized additive models in neuroimaging studies,
Reproducible Research in R: A Tutorial on How to Do the Same Thing More Than Once
+3 more
← Authored by 11
A Practical Guide to Variable Selection in Structural Equation Modeling by Using Regularized Multiple-Indicators, Multiple-Causes Models,
Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED),
A strong dependency between changes in fluid and crystallized abilities in human cognitive aging,
<b>pdc</b>: An<i>R</i>Package for Complexity-Based Clustering of Time Series,
Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data,
Machine-Learning Approaches to Structural Equation Modeling,
Meta-analysis of generalized additive models in neuroimaging studies,
Reproducible Research in R: A Tutorial on How to Do the Same Thing More Than Once
+3 more
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