quartets: Datasets to Help Teach Statistics

In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>, D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.

Version:	0.1.1
Depends:	R (≥ 2.10)
Published:	2023-04-13
DOI:	10.32614/CRAN.package.quartets
Author:	Lucy D'Agostino McGowan [aut, cre]
Maintainer:	Lucy D'Agostino McGowan <lucydagostino at gmail.com>
BugReports:	https://github.com/r-causal/quartets/issues
License:	MIT + file LICENSE
URL:	https://github.com/r-causal/quartets, https://r-causal.github.io/quartets/
NeedsCompilation:	no
Citation:	quartets citation info
Materials:	README, NEWS
CRAN checks:	quartets results

Documentation:

Downloads:

Linking:

Please use the canonical form https://CRAN.R-project.org/package=quartets to link to this page.