---
title: "Final project: <Your title>"
author: "<Your name>"
format:
  html:
    toc: true
    embed-resources: true
execute:
  echo: true
  warning: false
  message: false
---

## Before you start

This template is provided to help you produce a **fully reproducible** final project.

This `.qmd` is the file you submit for grading, so keep your full scientific narrative (analysis + interpretation) in this single document.

Rules:

- This `.qmd` is your submission artifact; presentation slides are separate and do not replace it.
- Do **not** use `setwd()` and do **not** use absolute paths.
- Your `.qmd` must render without errors from a fresh R session.
- If your data is not built-in, your code should download it from a stable public source (OSF/Zenodo/DOI) or use an instructor-approved reproducible access arrangement.

## 0) Setup

```{r}
library(tidyverse)

# Add packages you use (examples):
# library(lme4)
# library(metafor)
# library(lavaan)
# library(emmeans)
# library(performance)
```

## 1) Research question and hypotheses

Write:

- Research question(s)
- Hypotheses (confirmatory and/or exploratory)
- Why this dataset is appropriate (design/structure)

*Your text here.*

## 2) Data access and preprocessing

### Data access

Explain how the data is accessed reproducibly.

- If built-in: state the package and dataset name.
- If public: include the URL/DOI and download code.

```{r}
# Example pattern (public CSV):
# url <- "https://..."
# df <- readr::read_csv(url)
```

### Preprocessing steps

Describe and implement:

- missing data handling
- transformations (if any)
- variable coding decisions

```{r}
# Your preprocessing code here
```

## 3) Descriptives and visualization

```{r}
# Example:
# df |> summary()
# ggplot(df, aes(x = ..., y = ...)) + geom_point()
```

## 4) Main analysis (advanced methods)

Implement at least **two** advanced components.

### Component 1

Describe what you did and why.

```{r}
# Your model/code here
```

### Component 2

Describe what you did and why.

```{r}
# Your model/code here
```

## 5) Diagnostics / robustness / bias checks

Include checks appropriate for your methods:

- model diagnostics / influence
- bootstrap/sensitivity checks
- convergence/singularity checks (for mixed models)
- publication bias checks (for meta-analysis)

```{r}
# Your checks here
```

## 6) Results: tables + figures

Create clean, publication-style outputs:

```{r}
# Your table(s) and figure(s) here
```

## 7) Discussion

Write:

- main conclusions
- limitations
- what you would do next

*Your text here.*

## 8) Reproducibility checklist (fill in)

- [ ] My `.qmd` renders without errors from a fresh session.
- [ ] I used no absolute paths and no `setwd()`.
- [ ] My data is accessible (built-in or downloaded from a stable public source).
- [ ] I stated and justified my modeling choices.
- [ ] I included checks appropriate for my chosen methods.

## 9) AI assistance disclosure (if applicable)

If you used Generative AI tools, include:

- what you used it for (e.g., “debugging `lmer()` convergence warnings”)
- what you changed manually

*Your disclosure here (or state “No AI tools used”).*

## Session info (optional)

```{r}
# Uncomment if you want to include:
# sessionInfo()
```
