2020
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6. | Gronau, Quentin F; Singmann, Henrik; Wagenmakers, Eric-Jan: bridgesampling: An R Package for Estimating Normalizing Constants. In: Journal of Statistical Software, vol. 92, no. 10, 2020. @article{Gronau2020b,
title = {bridgesampling: An R Package for Estimating Normalizing Constants},
author = {Quentin F Gronau and Henrik Singmann and Eric-Jan Wagenmakers},
url = {https://www.jstatsoft.org/index.php/jss/article/view/v092i10/v92i10.pdf, published version
https://arxiv.org/pdf/1710.08162.pdf, preprint
http://arxiv.org/abs/1710.08162, on ArXiV},
doi = {10.18637/jss.v092.i10},
year = {2020},
date = {2020-02-27},
urldate = {2018-09-25},
journal = {Journal of Statistical Software},
volume = {92},
number = {10},
abstract = {Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain, as they usually involve high-dimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng & Wong, 1996; Meng & Schilling, 2002) to estimate normalizing constants in a generic and easy-to-use fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples.},
keywords = {hierarchical-Bayesian modeling, R, Software, Statistics - Computation},
pubstate = {published},
tppubtype = {article}
}
Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain, as they usually involve high-dimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng & Wong, 1996; Meng & Schilling, 2002) to estimate normalizing constants in a generic and easy-to-use fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples. |
2019
|
5. | Singmann, Henrik; Kellen, David: An introduction to linear mixed modeling in experimental psychology. In: New Methods in Cognitive Psychology, pp. 4–31, Psychology Press, 2019. @incollection{Singmann2019,
title = {An introduction to linear mixed modeling in experimental psychology},
author = {Henrik Singmann and David Kellen},
url = {http://singmann.org/download/publications/singmann_kellen-introduction-mixed-models.pdf, preprint},
year = {2019},
date = {2019-11-11},
booktitle = {New Methods in Cognitive Psychology},
pages = {4–31},
publisher = {Psychology Press},
keywords = {mixed models, R, Statistics - Computation},
pubstate = {published},
tppubtype = {incollection}
}
|
2016
|
4. | Gauvrit, Nicolas; Singmann, Henrik; Soler-Toscano, Fernando; Zenil, Hector: Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method. In: Behavior Research Methods, vol. 48, pp. 314-329, 2016. @article{Gauvrit2016,
title = {Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method},
author = {Nicolas Gauvrit and Henrik Singmann and Fernando Soler-Toscano and Hector Zenil},
url = {http://singmann.org/download/publications/Gauvrit-et-al.-2016-Algorithmic-complexity-for-psychology-a-user-frie.pdf, published article
http://singmann.org/download/publications/submitted/2015_gauvrit_et_al_complexity_final.pdf, final manuscript
http://arxiv.org/abs/1409.4080, on arXiv
https://github.com/singmann/acss, code on GitHub},
year = {2016},
date = {2016-04-04},
journal = {Behavior Research Methods},
volume = {48},
pages = {314-329},
keywords = {complexity, new paradigm psychology of reasoning, R, Software},
pubstate = {published},
tppubtype = {article}
}
|
3. | Kellen, David; Singmann, Henrik: ROC Residuals in Signal-Detection Models of Recognition Memory. In: Psychonomic Bulletin & Review, vol. 23, no. 1, pp. 253-264, 2016. @article{Kellen3999,
title = {ROC Residuals in Signal-Detection Models of Recognition Memory},
author = {David Kellen and Henrik Singmann},
url = {http://singmann.org/download/publications/Kellen-and-Singmann-2016-ROC-residuals-in-signal-detection-models-of-recogn.pdf, publisher manuscript (large file)
http://singmann.org/download/publications/submitted/final_manuscript.pdf, accepted manuscript (small file)
https://osf.io/p2eq8/, supplemental materials (OSF)},
year = {2016},
date = {2016-02-01},
journal = {Psychonomic Bulletin & Review},
volume = {23},
number = {1},
pages = {253-264},
keywords = {mathematical modeling, measurement models, memory, mixed models, R, Signal detection},
pubstate = {published},
tppubtype = {article}
}
|
2015
|
2. | Klauer, Karl Christoph; Singmann, Henrik; Kellen, David: Parametric Order Constraints in Multinomial Processing Tree Models: An Extension of Knapp and Batchelder (2004). In: Journal of Mathematical Psychology, vol. 64-65, no. 1-7, 2015. @article{klauer_parametric_xxxx,
title = {Parametric Order Constraints in Multinomial Processing Tree Models: An Extension of Knapp and Batchelder (2004)},
author = {Klauer, Karl Christoph and Singmann, Henrik and Kellen, David},
url = {http://singmann.org/download/publications/2015_klauer_singmann_kellen_jmp.pdf, published article
http://singmann.org/download/publications/supplemental/2014_klauer_singmann_kellen_JMP_supp.zip, supplemental materials
http://arxiv.org/abs/1411.2571, on arXiv},
year = {2015},
date = {2015-01-01},
journal = {Journal of Mathematical Psychology},
volume = {64-65},
number = {1-7},
keywords = {mathematical modeling, measurement models, MPT models, R},
pubstate = {published},
tppubtype = {article}
}
|
2013
|
1. | Singmann, Henrik; Kellen, David: MPTinR: Analysis of multinomial processing tree models in R. In: Behavior Research Methods, vol. 45, no. 2, pp. 560–575, 2013, ISSN: 1554-3528. @article{singmann_mptinr:_2013,
title = {MPTinR: Analysis of multinomial processing tree models in R},
author = {Singmann, Henrik and Kellen, David},
url = {http://singmann.org/download/publications/Singmann%20&%20Kellen%20(2013)%20MPTinR.pdf, published article
http://CRAN.R-project.org/package=MPTinR, MPTinR on CRAN},
issn = {1554-3528},
year = {2013},
date = {2013-01-01},
journal = {Behavior Research Methods},
volume = {45},
number = {2},
pages = {560--575},
keywords = {Fisher information, mathematical modeling, measurement models, MPT models, R, Software},
pubstate = {published},
tppubtype = {article}
}
|