model_parameters(): show both raw and standardized coefficients

Ala https://gist.github.com/bwiernik/55571550aa45d57ddaf7cd680c3211c3

library(dplyr, warn.conflicts = FALSE)
library(parameters)

mod <- lm(mpg ~ disp + wt,  data = mtcars)

# Both
left_join(
  parameters(mod),
  select(
    parameters(mod, standardize = "refit"), 
    Parameter, "Std. Coefficient" = Coefficient, 
    "Std._CI_low" = CI_low, "Std._CI_high" = CI_high
  ),
  by = "Parameter"
)
#> Parameter   | Coefficient |       SE |         95% CI | t(29) |      p | Std. Coefficient |    Std. 95% CI
#> ----------------------------------------------------------------------------------------------------------
#> (Intercept) |       34.96 |     2.16 | [30.53, 39.39] | 16.15 | < .001 |         1.85e-17 | [-0.17,  0.17]
#> disp        |       -0.02 | 9.19e-03 | [-0.04,  0.00] | -1.93 | 0.064  |            -0.36 | [-0.75,  0.02]
#> wt          |       -3.35 |     1.16 | [-5.73, -0.97] | -2.88 | 0.007  |            -0.54 | [-0.93, -0.16]
#> 
#> Uncertainty intervals (equal-tailed) and p values (two-tailed) computed using a
#>   Wald t-distribution approximation.

I'm thinking an argument like add_standardized. FALSE (default) to replace raw coefficients with standardized (if any), TRUE to add the retain the raw results and add standardized coefficients and CI (if any).

Start with the parameters() entry point and its existing standardize handling. Trace how raw coefficients, standardized coefficients, and confidence intervals are assembled; done means an option can retain raw results while adding standardized results and intervals, without changing the default behavior.