Wording of table headers

In my neverending attempt to understand the terminology and concepts behind "predictions", I have drafted a vignette that shows the technical differences between the different "marginalization" options (argument estimate), as well as their meaning. The basic distinction is data-grid based (or condtitional) predictions, and empirical (or marginal) predictions for those options that average over the sample. See in particular: https://easystats.github.io/modelbased/articles/technical_marginalization.html#summary

Now my question is, since we try to distinguish between the different estimate options and do reflect this in the title: should we be even clearer, adding "conditional" and "marginal" as term in the header?

Note: the second option, the emmeans-default estimate = "typical", prints "Estimated Marginal Means", although these are probably not strictly marginal, so we would/should maybe change this.

We could simply have "conditional predictions" as header for the first two options, and "marginal predictions" or "marginal (counterfactual) predictions" for the latter two options? @DominiqueMakowski any thoughts?

library(modelbased)
data(penguins)
set.seed(123)
d <- penguins
d$weights <- abs(rnorm(nrow(d), 1, 0.2))
model <- lm(body_mass ~ species + sex + bill_len, data = d, weights = weights)

data-grid based, conditional predictions

estimate_means(model, "sex", estimate = "specific")
#> Model-based Predictions
#> 
#> sex    |    Mean |    SE |             95% CI | t(328)
#> ------------------------------------------------------
#> female | 3611.50 | 59.73 | [3494.00, 3729.01] |  60.46
#> male   | 4148.88 | 38.84 | [4072.48, 4225.28] | 106.83
#> 
#> Variable predicted: body_mass
#> Predictors modulated: sex
#> Predictors controlled: species (Adelie), bill_len (44)

estimate_means(model, "sex", estimate = "typical")
#> Estimated Marginal Means
#> 
#> sex    |    Mean |    SE |             95% CI | t(328)
#> ------------------------------------------------------
#> female | 3873.98 | 25.06 | [3824.69, 3923.27] | 154.61
#> male   | 4411.36 | 32.43 | [4347.56, 4475.15] | 136.03
#> 
#> Variable predicted: body_mass
#> Predictors modulated: sex
#> Predictors averaged: species, bill_len (44)

empirical, marginal predictions

estimate_means(model, "sex", estimate = "average")
#> Average Predictions
#> 
#> sex    |    Mean |    SE |             95% CI | t(328)
#> ------------------------------------------------------
#> female | 3868.92 | 23.72 | [3822.27, 3915.58] | 163.13
#> male   | 4545.49 | 23.74 | [4498.79, 4592.19] | 191.49
#> 
#> Variable predicted: body_mass
#> Predictors modulated: sex

estimate_means(model, "sex", estimate = "population")
#> Average Counterfactual Predictions
#> 
#> sex    |    Mean |    SE |             95% CI | t(328)
#> ------------------------------------------------------
#> female | 3939.15 | 27.46 | [3885.12, 3993.17] | 143.44
#> male   | 4476.52 | 27.38 | [4422.66, 4530.38] | 163.50
#> 
#> Variable predicted: body_mass
#> Predictors modulated: sex
#> Predictors averaged: species, bill_len (44)

^{Created on 2026-08-13 with reprex v2.1.1}

Start with the estimate_means examples in the issue and the linked technical marginalization vignette, comparing the current headers for each estimate option. Decide which conditional and marginal terminology the project should use, then update the relevant output wording and verify all four example outputs are consistent.