Publications by Achim Zeileis
Color vision deficiency emulation fixed in colorspace 2.1-0
The color vision deficiency emulation provided by R package colorspace was inaccurate for some highly-saturated colors due to a bug that was fixed in version 2.1-0. The (typically small) differences are illustrated for a range of palettes. Background Functions for emulating color vision deficiencies have been part of the R package colorspace for se...
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Coloring in R’s blind spot
New arXiv working paper on the new color palette functions palette.colors() and hcl.colors() in base R since version 4.0.0. Citation Achim Zeileis, Paul Murrell (2023). “Coloring in R’s Blind Spot.” arXiv.org E-Print Archive arXiv:2303.04918 [stat.CO]. doi:10.48550/arXiv.2303.04918 Abstract Prior to version 4.0.0 R had a poor default color pa...
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Machine learning of a 2022 FIFA World Cup multiverse
Probabilistic forecasts for the 2022 FIFA World Cup are obtained by using a hybrid model that combines data from three advanced statistical models through random forests. The favorite is Brazil, followed by Argentina, Netherlands, Germany, and France. The 2022 FIFA World Cup will take place in Qatar from 20 November to 18 December 2022. 32 of ...
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The Poisson distribution: From basic probability theory to regression models
Brief introduction to the Poisson distribution for modeling count data using the distributions3 package. The distribution is illustrated using the number of goals scored at the 2018 FIFA World Cup, suitable for self-study or as a classroom exercise. The Poisson distribution The classic basic probability distribution employed for modeling count da...
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distributions3 @ useR! 2022
Conference presentation about the ‘distributions3’ package for S3 probability distributions (and ‘topmodels’ for graphical model assessment) at useR! 2022: Slides, video, replication code, and vignette. Abstract (Authors: Achim Zeileis, Moritz N. Lang, Alex Hayes) The distributions3 package provides a beginner-friendly and lightweight int...
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Model-Based Causal Forests for Heterogeneous Treatment Effects
A new arXiv paper investigates which building blocks of random forests, especially causal forests and model-based forests, make them work for heterogeneous treatment effect estimation, both in randomized trials and observational studies. Citation Susanne Dandl, Torsten Hothorn, Heidi Seibold, Erik Sverdrup, Stefan Wager, Achim Zeileis (2022). “...
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Probabilistic forecasting for the UEFA Women’s Euro 2022
Using a consensus model based on quoted bookmakers’ odds winning probabilities for all competing teams in the UEFA Women’s Euro are obtained: The favorite is Spain, followed by host England, France, and the Netherlands as the defending champion. Football fans throughout Europe and the world anticipate the UEFA Women’s Euro 2022 that wil...
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Welcome to the redesigned web page
Redesigned the personal web page using a responsive jekyll design with a fresh theme and better-structured content. New web page After half a decade the web page received a reboot with a new theme as well as more and better-structured content. Thanks Thanks to the help of Patrik Keller and Reto Stauffer and the powerful Feeling Responsive theme b...
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GLMM trees published in BRM
Generalized linear mixed-effects model trees, especially for detecting treatment-subgroup interactions in clustered data. Accompanied by the R package glmertree, combining partykit::glmtree and lme4::glmer. Citation Marjolein Fokkema, Niels Smits, Achim Zeileis, Torsten Hothorn, Henk Kelderman (2018). “Detecting Treatment-Subgroup Interactions ...
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BAMLSS paper published in JCGS
Bayesian additive models for location, scale, and shape (and beyond) provide a general framework for distributional regression. Accompanied by the R package bamlss. Citation Nikolaus Umlauf, Nadja Klein, Achim Zeileis (2018). “BAMLSS: Bayesian Additive Models for Location, Scale and Shape (and Beyond).” Journal of Computational and Graphical ...
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