Fix warnings in examples (#325)

* update authors

* language

* language

* warnings in examples, authorship, version bump, cran comments

* remove spelling

Author: rempsyc

.gitignore

@@ -50,3 +50,4 @@ revdep/library.noindex
 revdep/data.sqlite
 /doc/
 /Meta/
+revdep/

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: report
 Title: Automated Reporting of Results and Statistical Models
-Version: 0.5.5.3
+Version: 0.5.5.4
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
@@ -51,7 +51,7 @@ Depends:
     R (>= 3.6)
 Imports:
     bayestestR (>= 0.13.0),
-    effectsize (>= 0.8.2),
+    effectsize (> 0.8.2),
     insight (>= 0.18.8),
     parameters (>= 0.20.0),
     performance (>= 0.9.2),

R/cite_easystats.R

@@ -277,7 +277,8 @@ cite_easystats <- function(packages = "easystats",
           )
         ),
         report = sprintf(
-          paste("Makowski, D., L\u00fcdecke, D., Ben-Shachar, M. S., Patil, I., & Wiernik, B. M. (2022).",
+          paste("Makowski, D., L\u00fcdecke, D., Ben-Shachar, M. S., Patil, I.,",
+                "Wiernik, B. M., & Th\U00E9riault, R. (2022).",
                 "report: Automated reporting of results and statistical models (%s) [R package].",
                 "https://CRAN.R-project.org/package=report (Original work published 2021)"),
           installed_packages["report"]

R/report.R

@@ -85,7 +85,7 @@
 #'
 #' library(report)
 #'
-#' model <- t.test(mpg ~ am, data = mtcars)
+#' model <- t.test(mtcars$mpg ~ mtcars$am)
 #' r <- report(model)
 #'
 #' # Text

R/report.bayesfactor_models.R

@@ -23,7 +23,6 @@
 #'
 #'   r <- report(BFmodels)
 #'   r
-#'   as.data.frame(r)
 #'
 #'   # Bayes factor - inclusion
 #'   inc_bf <- bayesfactor_inclusion(BFmodels, prior_odds = c(1, 2, 3), match_models = TRUE)

R/report.brmsfit.R

@@ -14,7 +14,7 @@
 #' \dontrun{
 #' if (require("brms")) {
 #'   model <- suppressWarnings(brm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 300))
-#'   r <- report(model)
+#'   r <- report(model, verbose = FALSE)
 #'   r
 #'   summary(r)
 #'   as.data.frame(r)

R/report.htest.R

@@ -17,7 +17,7 @@
 #' report(t.test(iris$Sepal.Width, iris$Sepal.Length))
 #' report(t.test(iris$Sepal.Width, iris$Sepal.Length, var.equal = TRUE))
 #' report(t.test(mtcars$mpg ~ mtcars$vs))
-#' report(t.test(mtcars$mpg, mtcars$vs, paired = TRUE))
+#' report(t.test(mtcars$mpg, mtcars$vs, paired = TRUE), verbose = FALSE)
 #' report(t.test(iris$Sepal.Width, mu = 1))
 #'
 #' # Correlations

R/report.lavaan.R

@@ -25,8 +25,8 @@
 #'   # summary(as.data.frame(r))
 #'
 #'   # Specific reports
-#'   report_table(model)
-#'   report_performance(model)
+#'   suppressWarnings(report_table(model))
+#'   suppressWarnings(report_performance(model))
 #' }
 #' }
 #' @return An object of class [report()].

R/report.stanreg.R

@@ -17,7 +17,6 @@
 #'   r
 #'   summary(r)
 #'   as.data.frame(r)
-#'   summary(as.data.frame(r))
 #' }
 #' }
 #' @return An object of class [report()].

R/report_effectsize.R

@@ -31,7 +31,7 @@
 #'
 #' # Bayesian models
 #' if (require("rstanarm")) {
-#'   model <- stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600)
+#'   model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
 #'   report_effectsize(model, effectsize_method = "basic")
 #' }
 #' }

R/report_info.R

@@ -32,7 +32,7 @@
 #'
 #' # Bayesian models
 #' if (require("rstanarm")) {
-#'   model <- stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 300)
+#'   model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 300))
 #'   report_info(model)
 #' }
 #' }

R/report_performance.R

@@ -39,7 +39,7 @@
 #'                  dem60 =~ y1 + y2 + y3
 #'                  dem60 ~ ind60 "
 #'   model <- lavaan::sem(structure, data = PoliticalDemocracy)
-#'   report_performance(model)
+#'   suppressWarnings(report_performance(model))
 #' }
 #' }
 #' @export

R/report_random.R

@@ -23,15 +23,16 @@
 #' \donttest{
 #' # Bayesian models
 #' if (require("rstanarm")) {
-#'   model <- stan_lmer(mpg ~ disp + (1 | cyl), data = mtcars, refresh = 0, iter = 1000)
+#'   model <- suppressWarnings(stan_lmer(
+#'     mpg ~ disp + (1 | cyl), data = mtcars, refresh = 0, iter = 1000))
 #'   r <- report_random(model)
 #'   r
 #'   summary(r)
 #' }
 #' }
 #' \dontrun{
 #' if (require("brms")) {
-#'   model <- brm(mpg ~ disp + (1 | cyl), data = mtcars, refresh = 0, iter = 1000)
+#'   model <- suppressWarnings(brm(mpg ~ disp + (1 | cyl), data = mtcars, refresh = 0, iter = 1000))
 #'   r <- report_random(model)
 #'   r
 #'   summary(r)

R/report_table.R

@@ -23,7 +23,7 @@
 #' report_table(iris)
 #'
 #' # h-tests
-#' report_table(t.test(mpg ~ am, data = mtcars))
+#' report_table(t.test(mtcars$mpg ~ mtcars$am))
 #'
 #' # ANOVAs
 #' report_table(aov(Sepal.Length ~ Species, data = iris))
@@ -50,7 +50,7 @@
 #'                  dem60 =~ y1 + y2 + y3
 #'                  dem60 ~ ind60 "
 #'   model <- lavaan::sem(structure, data = PoliticalDemocracy)
-#'   report_table(model)
+#'   suppressWarnings(report_table(model))
 #' }
 #' }
 #' @export

cran-comments.md

@@ -1,5 +1,5 @@
-## R CMD check results
+## R CMD check results report 0.5.6
 
-0 errors | 0 warnings | 0 note
+0 errors | 0 warnings | 1 note
 
-* Fixes failing tests in CRAN checks.
+* Change of maintainer from Dominique Makowski to Rémi Thériault.

inst/CITATION

@@ -1,8 +1,8 @@
 bibentry(bibtype = "Article",
          title = "Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption",
-         author=c(person("Dominique", "Makowski"), person("Mattan S.", "Ben-Shachar"), person("Indrajeet", "Patil"), person("Daniel", "Lüdecke")),
+         author=c(person("Dominique", "Makowski"), person("Mattan S.", "Ben-Shachar"), person("Indrajeet", "Patil"), person("Daniel", "Lüdecke"), person("Rémi", "Thériault")),
          year = 2021,
          journal = "CRAN",
          url = "https://github.com/easystats/report",
-         	textVersion = paste("Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN.", "Available from https://github.com/easystats/report.", "doi: .",  sep=" "),
+         	textVersion = paste("Makowski, D., Ben-Shachar, M.S., Patil, I., Lüdecke, D., & Thériault, R. (2020). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN.", "Available from https://github.com/easystats/report.", "doi: .",  sep=" "),
           mheader = "To cite in publications use:")

inst/easystats_bib.bib

@@ -201,7 +201,7 @@ @software{performancePackage
 @software{reportPackage,
   title = {{{report}}: Automated Reporting of Results and Statistical Models},
   shorttitle = {{{report}}},
-  author = {Makowski, Dominique and L\"udecke, Daniel and Ben-Shachar, Mattan S. and Patil, Indrajeet and Wiernik, Brenton M.},
+  author = {Makowski, Dominique and L\"udecke, Daniel and Ben-Shachar, Mattan S. and Patil, Indrajeet and Wiernik, Brenton M. and Thériault, Rémi},
   date = {2022-02-22},
   origdate = {2021-04-15},
   url = {https://CRAN.R-project.org/package=report},

inst/easystats_bib.yaml

@@ -569,6 +569,8 @@ references:
       given: Indrajeet
     - family: Wiernik
       given: Brenton M.
+    - family: Thériault
+      given: Rémi
   citation-key: reportPackage
   genre: R package
   issued:

man/report-package.Rd

@@ -86,7 +86,7 @@ report.estimate_means <- report.estimate_contrasts
 #' \dontrun{
 #' library(rstanarm)
 #' model <- stan_glm(Sepal.Width ~ Species * Petal.Width, data = iris)
-#' report(estimate_slopes(model))
+#' suppressWarnings(report(estimate_slopes(model)))
 #' }
 #' @rdname report.estimate_contrasts
 #' @export

man/report.Rd

@@ -9,7 +9,7 @@
       of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
       (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
       (location = 19.20, scale = 5.40) distributions. The model's explanatory power
-      is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.78).  Within this
       model:
       
         - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
@@ -38,7 +38,7 @@
       chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
       (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
       (location = , scale = ) distributions. The model's explanatory power is
-      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.78).  Within this
       model:
       
         - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
@@ -67,7 +67,7 @@
       chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
       (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
       (location = , scale = ) distributions. The model's explanatory power is
-      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.78).  Within this
       model:
       
         - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
@@ -96,7 +96,7 @@
       chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
       (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
       (location = 0.00, scale = 5.40) distributions. The model's explanatory power is
-      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.78).  Within this
       model:
       
         - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%

man/report.bayesfactor_models.Rd

@@ -4,6 +4,7 @@ if (requiet("brms")) {
     # so worth checking only locally
     skip_on_ci()
 
+    set.seed(333)
     model <- suppressWarnings(brm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 300, seed = 333))
     r <- report(model, verbose = FALSE)