Passing `exclude` argument of emmc functions in `emmeans` clashes with .update.options

[banfai]

If the exclude argument of an emmc function is passed to emmeans (via ...), then it clashes with the exclude argument of .update.options():

library(emmeans)

emmeans(lm(Petal.Length ~ Species, data = iris), pairwise ~ Species, exclude = "setosa")
#> Error in .update.options(result, options, ..., exclude = "adjust"): formal argument "exclude" matched by multiple actual arguments

Expected behavior can be achieved by passing a shortened form (e.g. exclud), but this reduces readability:

emmeans(lm(Petal.Length ~ Species, data = iris), pairwise ~ Species, exclud = "setosa")
#> $emmeans
#>  Species    emmean     SE  df lower.CL upper.CL
#>  setosa       1.46 0.0609 147     1.34     1.58
#>  versicolor   4.26 0.0609 147     4.14     4.38
#>  virginica    5.55 0.0609 147     5.43     5.67
#> 
#> Confidence level used: 0.95 
#> 
#> $contrasts
#>  contrast               estimate     SE  df t.ratio p.value
#>  versicolor - virginica    -1.29 0.0861 147 -15.012  <.0001

It also doesn't work passing it in options:

emmeans(lm(Petal.Length ~ Species, data = iris), pairwise ~ Species, options = list(exclude = "setosa"))
#> Argument 'exclude' was ignored. Valid choices are:
#> adjust, alpha, avgd.over, bias.adjust, by.vars, calc, cross.adjust, delta, df, initMesg, estName, estType, famSize, frequentist, infer, inv.lbl, level, methDesc, nesting, null, predict.type, pri.vars, side, sigma, tran, tran.mult, tran.offset, tran2, type, is.new.rg, submodel, model.info, roles, grid, levels, matlevs, linfct, bhat, nbasis, V, dffun, dfargs, misc, post.beta
#> $emmeans
#> [...]
#> 
#> $contrasts
#>  contrast               estimate     SE  df t.ratio p.value
#>  setosa - versicolor       -2.80 0.0861 147 -32.510  <.0001
#>  setosa - virginica        -4.09 0.0861 147 -47.521  <.0001
#>  versicolor - virginica    -1.29 0.0861 147 -15.012  <.0001
#> 
#> P value adjustment: tukey method for comparing a family of 3 estimates

I know that it is discouraged to use emmeans to obtain contrasts directly, and it indeed does work using it separately, but it seems to be easy to fix to work directly (see PR)

contrast(emmeans(lm(Petal.Length ~ Species, data = iris), ~ Species), method = "pairwise", exclude = "setosa")
#> contrast               estimate     SE  df t.ratio p.value
#> versicolor - virginica    -1.29 0.0861 147 -15.012  <.0001

[rvlenth]

PR has been merged.