{ "_id": "6a3643563efcd9bda43fcd98", "Package": "quartets", "Type": "Package", "Title": "Datasets to Help Teach Statistics", "Version": "0.1.3", "Authors@R": "c(\nperson(\"Lucy\", \"D'Agostino McGowan\", , \"lucydagostino@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-6983-2759\"))\n)", "Description": "In the spirit of Anscombe's quartet, this package includes\ndatasets that demonstrate the importance of visualizing your\ndata, the importance of not relying on statistical summary\nmeasures alone, and why additional assumptions about the data\ngenerating mechanism are needed when estimating causal effects.\nThe package includes \"Anscombe's Quartet\" (Anscombe 1973)\n, D'Agostino McGowan &\nBarrett (2023) \"Causal Quartet\"\n, \"Datasaurus Dozen\"\n(Matejka & Fitzmaurice 2017), \"Interaction Triptych\" (Rohrer &\nArslan 2021) , \"Rashomon\nQuartet\" (Biecek et al. 2023) ,\nand Gelman \"Variation and Heterogeneity Causal Quartets\"\n(Gelman et al. 2023) .", "License": "MIT + file LICENSE", "URL": "https://github.com/r-causal/quartets,\nhttps://r-causal.github.io/quartets/", "BugReports": "https://github.com/r-causal/quartets/issues", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.3", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Repository": "https://r-causal.r-universe.dev", "Date/Publication": "2026-03-22 14:51:00 UTC", "RemoteUrl": "https://github.com/r-causal/quartets", "RemoteRef": "HEAD", "RemoteSha": "a6d67ece2483cd6633ced4b5ece0abd74c46d540", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-20 07:35:06 UTC", "User": "root" }, "Author": "Lucy D'Agostino McGowan [aut, cre] (ORCID:\n)", "Maintainer": "Lucy D'Agostino McGowan ", "MD5sum": "7413aed14a510bd276910e0fac42c007", "_user": "r-causal", "_type": "src", "_file": "quartets_0.1.3.tar.gz", "_fileid": "babb444a15da43f0c195f5a7d723af859bc7302db18bb2fb0868d10379c16dd2", "_filesize": 2519252, "_sha256": 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"fields": [ "dataset", "x", "y" ], "rows": 11, "table": true, "tojson": true }, { "name": "anscombe_outlier", "title": "Anscombe's Quartet Outlier Data", "object": "anscombe_outlier", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "dataset", "x", "y" ], "rows": 11, "table": true, "tojson": true }, { "name": "anscombe_quartet", "title": "Anscombe's Quartet Data", "object": "anscombe_quartet", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "dataset", "x", "y" ], "rows": 44, "table": true, "tojson": true }, { "name": "causal_collider", "title": "Collider Data", "object": "causal_collider", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "exposure", "outcome", "covariate" ], "rows": 100, "table": true, "tojson": true }, { "name": "causal_collider_time", "title": "Time-varying Causal Quartet Data", "object": "causal_collider_time", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "exposure_baseline", "outcome_baseline", "covariate_baseline", "exposure_followup", "outcome_followup", "covariate_followup" ], "rows": 100, "table": true, "tojson": true }, { "name": "causal_confounding", "title": "Confounder Data", "object": "causal_confounding", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "covariate", "exposure", "outcome" ], "rows": 100, "table": true, "tojson": true }, { "name": "causal_confounding_time", "title": "Time-varying Causal Quartet Data", "object": "causal_confounding_time", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "covariate_baseline", "exposure_baseline", "outcome_baseline", "covariate_followup", "exposure_followup", "outcome_followup" ], "rows": 100, "table": true, "tojson": true }, { "name": "causal_m_bias", "title": "M-Bias Data", "object": "causal_m_bias", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "u1", "u2", "covariate", "exposure", "outcome" ], "rows": 100, "table": true, "tojson": true }, { "name": "causal_m_bias_time", "title": "Time-varying Causal Quartet Data", 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