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r-causal · Dec 6, 2023 · b812b77 · b812b77
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -2,7 +2,7 @@
 ^\.Rproj\.user$
 ^LICENSE\.md$
 ^data-raw$
-^README\.Rmd$
+^README\.qmd$
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: quartets
 Type: Package
 Title: Datasets to Help Teach Statistics
-Version: 0.1.2
+Version: 0.1.3
 Authors@R: c(
     person("Lucy", "D'Agostino McGowan", , "[email protected]", 
     role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6983-2759"))
@@ -11,7 +11,7 @@ Description: In the spirit of Anscombe's quartet, this package includes datasets
    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>, 
+   D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.1080/26939169.2023.2276446>, 
    "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>.
 License: MIT + file LICENSE

diff --git a/NEWS.md b/NEWS.md
@@ -9,3 +9,7 @@
 # quartets 0.1.2
 
 * Add an article demonstrating how to use the datasets with a real-world example
+
+# quartets 0.1.3
+
+* Update citation for Causal Quartet
diff --git a/R/causal_collider.R b/R/causal_collider.R
@@ -5,7 +5,7 @@
 #' Y ~ X + N(0,1) (outcome)
 #' Z ~ 0.45X + 0.77Y + N(0, 1) (measured factor: collider)
 #'
-#' @references D'Agostino McGowan L, Barrett M (2023). Causal inference is not a statistical problem. Preprint arXiv:2304.02683v1.
+#' @references Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446
 #' @format A dataframe with 100 rows and 3 variables:
 #'
 #' * `exposure`: exposure

diff --git a/R/causal_confounding.R b/R/causal_confounding.R
@@ -4,7 +4,7 @@
 #' Z ~ N(0, 1) (measured factor: confounder)
 #' X ~ Z + N(0,1) (exposure)
 #' Y ~ 0.5X + Z + N(0, 1) (outcome)
-#' @references D'Agostino McGowan L, Barrett M (2023). Causal inference is not a statistical problem. Preprint arXiv:2304.02683v1.
+#' @references Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446
 #' @format A dataframe with 100 rows and 3:
 #'
 #' * `covariate`: a known factor (confounder)

diff --git a/R/causal_m_bias.R b/R/causal_m_bias.R
@@ -7,8 +7,7 @@
 #' X ~ U1 + N(0, 1) (exposure)
 #' Y ~ X + U2 + N(0, 1) (outcome)
 #'
-#' @references D'Agostino McGowan L, Barrett M (2023). Causal inference is not a statistical problem. Preprint arXiv:2304.02683v1.
-#'
+#' @references Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446
 #' @format A dataframe with 100 rows and 5 variables:
 #'
 #' * `u1`: an unknown factor

diff --git a/R/causal_mediator.R b/R/causal_mediator.R
@@ -5,7 +5,7 @@
 #' Z ~ X + N(0,1) (measured factor: mediator)
 #' Y ~ Z + N(0, 1) (outcome)
 #'
-#' @references D'Agostino McGowan L, Barrett M (2023). Causal inference is not a statistical problem. Preprint arXiv:2304.02683v1.
+#' @references Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446
 #'
 #' @format A dataframe with 100 rows and 3 variables:
 #'

diff --git a/R/causal_quartet.R b/R/causal_quartet.R
@@ -7,7 +7,7 @@
 #'   * (3) A mediator
 #'   * (4) M-bias
 #'
-#' @references D'Agostino McGowan L, Barrett M (2023). Causal inference is not a statistical problem. Preprint arXiv:2304.02683v1.
+#' @references Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446
 #'
 #' @format A dataframe with 400 rows and 6 variables:
 #'

diff --git a/R/causal_quartet_time.R b/R/causal_quartet_time.R
@@ -30,7 +30,7 @@
 #' ## occur.
 #' lm(outcome_followup ~ exposure_baseline + covariate_baseline,
 #'    data = causal_collider_time)
-#' @references D'Agostino McGowan L, Barrett M (2023). Causal inference is not a statistical problem. Preprint arXiv:2304.02683v1.
+#' @references Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446
 #'
 #' @format `causal_collider_time`: A dataframe with 100 rows and 7 variables:
 #'

diff --git a/README.md b/README.md
@@ -131,12 +131,12 @@ causal_quartet |>
   knitr::kable()
 ```
 
-| Data generating mechanism | Y \~ X | Y \~ X + Z | Correlation of X and Z |
-|:--------------------------|-------:|-----------:|-----------------------:|
-| \(1\) Collider            |      1 |       0.55 |                    0.7 |
-| \(2\) Confounder          |      1 |       0.50 |                    0.7 |
-| \(3\) Mediator            |      1 |       0.00 |                    0.7 |
-| \(4\) M-Bias              |      1 |       0.88 |                    0.7 |
+| Data generating mechanism | Y ~ X | Y ~ X + Z | Correlation of X and Z |
+|:--------------------------|------:|----------:|-----------------------:|
+| \(1\) Collider            |     1 |      0.55 |                    0.7 |
+| \(2\) Confounder          |     1 |      0.50 |                    0.7 |
+| \(3\) Mediator            |     1 |      0.00 |                    0.7 |
+| \(4\) M-Bias              |     1 |      0.88 |                    0.7 |
 
 ## Datasaurus Dozen
 
@@ -389,8 +389,9 @@ Statistician. 27 (1): 17–21. doi:10.1080/00031305.1973.10478966. JSTOR
 Biecek P, Baniecki H, Krzyziński M, Cook D (2023). Performance is not
 enough: the story of Rashomon’s quartet. Preprint arXiv:2302.13356v2.
 
-D’Agostino McGowan L, Barrett M (2023). Causal inference is not a
-statistical problem. Preprint arXiv:2304.02683v1.
+Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal
+inference is not just a statistics problem, Journal of Statistics and
+Data Science Education, DOI: 10.1080/26939169.2023.2276446
 
 Davies R, Locke S, D’Agostino McGowan L (2022). *datasauRus: Datasets
 from the Datasaurus Dozen*. R package version 0.1.6,

diff --git a/README.qmd b/README.qmd
@@ -291,7 +291,7 @@ Anscombe, F. J. (1973). "Graphs in Statistical Analysis". American Statistician.
 
 Biecek P, Baniecki H, Krzyziński M, Cook D (2023). Performance is not enough: the story of Rashomon’s quartet. Preprint arXiv:2302.13356v2.
 
-D'Agostino McGowan L, Barrett M (2023). Causal inference is not a statistical problem. Preprint arXiv:2304.02683v1.
+Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446
 
 Davies R, Locke S, D'Agostino McGowan L (2022). _datasauRus: Datasets from the Datasaurus Dozen_. R package version 0.1.6, <https://CRAN.R-project.org/package=datasauRus>.
 

diff --git a/man/causal_collider.Rd b/man/causal_collider.Rd
diff --git a/man/causal_collider_time.Rd b/man/causal_collider_time.Rd
diff --git a/man/causal_confounding.Rd b/man/causal_confounding.Rd
diff --git a/man/causal_m_bias.Rd b/man/causal_m_bias.Rd
diff --git a/man/causal_mediator.Rd b/man/causal_mediator.Rd
diff --git a/man/causal_quartet.Rd b/man/causal_quartet.Rd