Easily compute planned contrast analyses (pairwise comparisons similar to t-tests but more powerful when more than 2 groups), and format in publication-ready format. Supports only three groups for the moment. In this particular case, the confidence intervals are bootstraped on the Robust Cohen's d.

## Arguments

- response
The dependent variable.

- group
The group for the comparison.

- covariates
The desired covariates in the model.

- data
The data frame.

- bootstraps
The number of bootstraps to use for the confidence interval

## Details

Statistical power is lower with the standard *t* test
compared than it is with the planned contrast version for two
reasons: a) the sample size is smaller with the *t* test,
because only the cases in the two groups are selected; and b)
in the planned contrast the error term is smaller than it is
with the standard *t* test because it is based on all the cases
(source).

## Examples

```
# Basic example
nice_contrasts(
data = mtcars,
response = "mpg",
group = "cyl",
bootstraps = 200
)
#> Dependent Variable Comparison df t p dR CI_lower
#> 1 mpg 4 - 8 29 8.904534 8.568209e-10 3.031774 2.0731878
#> 2 mpg 6 - 8 29 3.111825 4.152209e-03 1.245144 0.6934405
#> 3 mpg 4 - 6 29 4.441099 1.194696e-04 1.786630 0.9840528
#> CI_upper
#> 1 5.229091
#> 2 2.728547
#> 3 3.415750
if (FALSE) {
nice_contrasts(
data = mtcars,
response = "disp",
group = "gear"
)
# Multiple dependent variables
nice_contrasts(
data = mtcars,
response = c("mpg", "disp", "hp"),
group = "cyl"
)
# Adding covariates
nice_contrasts(
data = mtcars,
response = "mpg",
group = "cyl",
covariates = c("disp", "hp")
)
}
```