---
title: "Official Statistics Workflow with DPrivStats"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Official Statistics Workflow}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

```{r setup}
library(DPrivStats)
set.seed(11)
```

This vignette sketches an official-statistics workflow: combining multiple
DP tabulations under a single budget, applying post-processing constraints,
and comparing composition rules.

## Combining releases under one budget

```{r}
budget <- new_privacy_budget(epsilon = 4.0, delta = 1e-6, composition = "rdp")

if (can_spend(budget, 1.5)) {
  budget <- spend(budget, 1.5, "mean income by region table")
}

if (can_spend(budget, 1.5)) {
  budget <- spend(budget, 1.5, "education histogram")
}
budget
```

## Post-processing constraints

DP histograms can contain negative noisy counts; truncating at zero and
normalizing are pure post-processing steps that preserve DP:

```{r}
data(example_microdata)
h <- dp_histogram(example_microdata$age, epsilon = 1.0,
                  breaks = seq(10, 90, by = 10), normalize = TRUE)
h$estimate # already non-negative by construction
```

## Composition comparison

For a fixed workflow of small releases, RDP is typically much tighter than
basic composition:

```{r}
eps_seq <- c(1.5, 1.5, 1.0)
compare_composition(eps_seq, delta = 1e-6)
```

## Disclosure risk intuition

The privacy loss random variable of a Laplace release is exponential; its
tail probabilities quantify the chance of large losses:

```{r}
laplace_plr_tail(c(0, 3, 6), epsilon = 1.0)
```