title = {{Multi-State Models for Panel Data: The msm Package for R}},
volume = {38},
year = {2011}
}
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archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Team, R Development Core and {R Development Core Team}, R},
doi = {10.1007/978-3-540-74686-7},
eprint = {arXiv:1011.1669v3},
isbn = {3{\_}900051{\_}00{\_}3},
issn = {3-900051-07-0},
journal = {R Foundation for Statistical Computing},
pmid = {16106260},
title = {{R: A Language and Environment for Statistical Computing}},
year = {2016}
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@book{rasmussen2006,
abstract = {Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.},
archivePrefix = {arXiv},
arxivId = {026218253X},
author = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
booktitle = {International Journal of Neural Systems},
doi = {10.1142/S0129065704001899},
eprint = {026218253X},
isbn = {026218253X},
issn = {0129-0657},
pmid = {15112367},
title = {{Gaussian Processes for Machine Learning}},
year = {2006}
}
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year = {2017}
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@book{Agresti2003,
author = {Agresti, Alan},
editor = {{John Wiley {\&} Sons}},
title = {{Categorical data analysis}},
year = {2003}
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@article{brms2018,
author = {B{\"{u}}rkner, Paul},
journal = {The R Journal},
number = {1},
pages = {395--411},
title = {{Advanced Bayesian Multilevel Modeling with the R Package brms}},
volume = {10},
year = {2018}
}
@article{brms2017,
author = {B{\"{u}}rkner, Paul},
journal = {Journal Of Statistical Software},
number = {1},
pages = {1--28},
title = {{brms: An R Package for Bayesian Multilevel Models Using Stan}},
volume = {80},
year = {2017}
}
@article{Steyerberg2008,
author = {Steyerberg, Ewout W and Mushkudiani, Nino and Perel, Pablo and Butcher, Isabella and Lu, Juan and McHugh, Gillian S and Murray, Gordon D and Marmarou, Anthony and Roberts, Ian and Habbema, J. Dik F and Maas, Andrew I. R},
doi = {10.1371/journal.pmed.0050165},
editor = {Singer, Mervyn},
journal = {PLoS Medicine},
month = {aug},
number = {8},
pages = {e165},
title = {{Predicting Outcome after Traumatic Brain Injury: Development and International Validation of Prognostic Scores Based on Admission Characteristics}},
