template.qmd

---
title: "Our great idea"
author:
- name: Marie Curie
  affiliations:
    - name: University of Paris
      department: Department of Radiation
      address: Somewhere
      city: Paris
      country: France
      postal-code: PX2039
  email: mcurie.notreal@gmail.com
  corresponding: true
- name: Genghis Khan
  affiliations:
    - name: Monash University
      department: Department of Econometrics & Business Statistics
      city: Clayton VIC
      country: Australia
      postal-code: 3800
- name: Monique Ash
  email: Monique.Ash@monash.edu
abstract: |
  A brief summary of our ideas
keywords: [blah, blah]
bibliography: references.bib
wpnumber: no/yr
jelcodes: C10,C14,C22
blind: false
cover: true
linestretch: 1.5
format:
  wp-pdf:
    keep-tex: true
filters:
  - blindable-div
---

# Introduction

In a famous paper, @BC64 introduced a family of transformations \dots

```{r}
#| label: fig-density
#| fig-cap: Simulated data from a N(0,1) distribution.
library(tidyverse)
set.seed(2022-12-20)
df <- tibble(x = rnorm(200))
df |>
  ggplot(aes(x=x)) +
  geom_density(bw = "sj") +
  geom_rug()
```

@fig-density shows a kernel density estimate of simulated data from a N(0,1) distribution. The sample variance is given by
$$
  s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i-\bar{x})^2 = `r round(sd(df$x), 2)`.
$$ {#eq-s2}
Note that @eq-s2 is an unbiased estimate of the variance, but it is not the maximum likelihood estimate [@Rice2007, p.269].

New paragraph.

## Subsection header

::: {.blindable}

# Acknowledgement {.unnumbered}

We would like to thank our pet goldfish...

:::