```{r setup, include = FALSE}
library(radiant)
knitr::opts_chunk$set(
  comment = NA,
  cache = FALSE,
  message = FALSE,
  warning = FALSE,
  dev = "svg"
)
```

Below is the output from Radiant used to evaluate if the average price in a sample of diamonds is greater than \$1000.

```{r fig.width = 7, fig.height = 7}
data(diamonds, package = "radiant.data", envir = environment())
result <- single_mean(
  diamonds, var = "price", comp_value = 1000, 
  alternative = "greater", conf_lev = 0.95
)
summary(result)
plot(result)
```

### A few other rmarkdown features

* You can create
* bullet lists

1. And numbered
2. lists

You can even include math:

$$f(\alpha, \beta) \propto x^{\alpha-1}(1-x)^{\beta-1}$$

See Rstudio's [rmarkdown](https://rmarkdown.rstudio.com/) site for more information.
