For example, we want to do a type III ANOVA, so we fit a linear model with categorical predictors and use the car::Anova function:

some_linear_model <- lm(mpg ~ as.factor(cyl)*as.factor(am), data=mtcars) 
some_anova <- car::Anova(some_linear_model, type = "III")

Then, we use report() and report_table() to output the results:

report::report(some_anova)
report::report_table(some_anova)

The effect sizes using repor_tablet() are correct, but the effect sizes using report() don't match up with the correct effects:

• The main effect of (Intercept) is statistically significant and large (F(1, 26) =
  171.10, p < .001; Eta2 (partial) = 0.41, 95% CI [0.15, 1.00])
• The main effect of as.factor(cyl) is statistically significant and large (F(2, 26)
  = 9.12, p < .001; Eta2 (partial) = 0.20, 95% CI [0.02, 1.00])
• The main effect of as.factor(am) is statistically significant and medium (F(1, 26)
  = 6.35, p = 0.018; Eta2 (partial) = 0.10, 95% CI [0.00, 1.00])
• The interaction between as.factor(cyl) and as.factor(am) is statistically not
  significant and large (F(2, 26) = 1.38, p = 0.269; Eta2 (partial) = 0.41, 95% CI
  [0.15, 1.00])

Compare these to the results obtained with report_table():