MOD_summary improvements about kirkegaard HOT 1 CLOSED

> #fit two models with iris data, one with normal and one with standardized data
> fit1 = lm("Sepal.Length ~ Sepal.Width + Petal.Length", iris)
> fit2 = lm("Sepal.Length ~ Sepal.Width + Petal.Length", iris %>% df_standardize())
Skipped Species because it is a factor.
> #then summarize the two models
> MOD_summary(fit1, standardize = F) #unstd. data, don't std. betas
  |============================================================================================================================| 100%
Model coefficients
             Beta    SE CI.lower CI.upper
Sepal.Width  0.60 0.069     0.46     0.73
Petal.Length 0.47 0.017     0.44     0.51


Model meta-data
       outcome   N   R2 R2-adj. R2-cv
1 Sepal.Length 150 0.84    0.84  0.82


Etas from analysis of variance
              eta eta.part
Sepal.Width  0.28     0.58
Petal.Length 0.91     0.92
> MOD_summary(fit1, standardize = T) #unstd. data, then std. betas
  |============================================================================================================================| 100%
Model coefficients
             Beta    SE CI.lower CI.upper
Sepal.Width  0.31 0.036     0.24     0.39
Petal.Length 1.01 0.036     0.93     1.08


Model meta-data
       outcome   N   R2 R2-adj. R2-cv
1 Sepal.Length 150 0.84    0.84  0.82


Etas from analysis of variance
              eta eta.part
Sepal.Width  0.28     0.58
Petal.Length 0.91     0.92