grouped_generics.md
In broomExtra: Enhancements for 'broom' and 'easystats' Package Families

`grouped_tidy()` works

Code
  list(lmer_df, lm_df)
Output
  [[1]]
  # A tibble: 8 x 9
    sex   effect   group    term            estimate std.error statistic conf.low
    <fct> <chr>    <chr>    <chr>              <dbl>     <dbl>     <dbl>    <dbl>
  1 F     fixed    <NA>     (Intercept)       623.     161.         3.88   209.  
  2 F     fixed    <NA>     age                -4.34     2.61      -1.66   -11.1 
  3 F     ran_pars status   sd__(Intercept)   169.      NA         NA       NA   
  4 F     ran_pars Residual sd__Observation   415.      NA         NA       NA   
  5 M     fixed    <NA>     (Intercept)       553.      62.5        8.84   392.  
  6 M     fixed    <NA>     age                -3.60     0.696     -5.17    -5.39
  7 M     ran_pars status   sd__(Intercept)    79.8     NA         NA       NA   
  8 M     ran_pars Residual sd__Observation   355.      NA         NA       NA   
    conf.high
        <dbl>
  1   1037.  
  2      2.38
  3     NA   
  4     NA   
  5    714.  
  6     -1.80
  7     NA   
  8     NA

  [[2]]
  # A tibble: 4 x 8
    sex   term        estimate std.error statistic  p.value conf.low conf.high
    <fct> <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
  1 F     (Intercept)   668.     110.         6.08 3.09e- 8   379.     957.   
  2 F     age            -6.06     2.61      -2.32 2.26e- 2   -12.9      0.815
  3 M     (Intercept)   547.      27.2       20.1  4.68e-84   477.     617.   
  4 M     age            -3.80     0.704     -5.40 7.38e- 8    -5.61    -1.98

`grouped_glance()` works

Code
  list(lmer_df, lm_df)
Output
  [[1]]
  # A tibble: 2 x 8
    sex    nobs sigma  logLik    AIC    BIC REMLcrit df.residual
    <fct> <int> <dbl>   <dbl>  <dbl>  <dbl>    <dbl>       <int>
  1 F        89  415.   -656.  1321.  1331.    1313.          85
  2 M      2754  355. -20079. 40165. 40189.   40157.        2750

  [[2]]
  # A tibble: 2 x 13
    sex   r.squared adj.r.squared sigma statistic      p.value    df  logLik
    <fct>     <dbl>         <dbl> <dbl>     <dbl>        <dbl> <dbl>   <dbl>
  1 F        0.0583        0.0475  431.      5.39 0.0226           1   -665.
  2 M        0.0105        0.0101  359.     29.1  0.0000000738     1 -20112.
       AIC    BIC   deviance df.residual  nobs
     <dbl>  <dbl>      <dbl>       <int> <int>
  1  1336.  1344.  16139909.          87    89
  2 40231. 40249. 355441032.        2752  2754