---
title: "Get Started with rbbnp"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Get Started with rbbnp}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  fig.align = "center",
  out.width = "80%",
  warning = FALSE,
  message = FALSE
)
library(rbbnp)
library(ggplot2)
```

## Overview

The **rbbnp** package implements a novel bias-bound approach to nonparametric inference developed by [Schennach (2020)](https://doi.org/10.1093/restud/rdz065). The key insight is that while we cannot consistently estimate pointwise bias, we can estimate an upper bound on bias magnitude, enabling valid confidence intervals with optimal bandwidths.

### The Problem

Traditional nonparametric inference faces a dilemma:

- **Optimal bandwidths** minimize mean squared error but introduce non-negligible bias
- **Undersmoothing** reduces bias but produces inefficient, wider confidence intervals

### The Solution

The bias-bound approach constructs confidence intervals that explicitly account for potential bias:

$$CI = [\hat{f} - \bar{b} - z_{\alpha/2}\hat{\sigma}, \quad \hat{f} + \bar{b} + z_{\alpha/2}\hat{\sigma}]$$

where $\bar{b}$ is the estimated bias bound.

## Installation

```{r eval=FALSE}
# Install from CRAN
install.packages("rbbnp")

# Or install development version from GitHub
# install.packages("devtools")
devtools::install_github("xinyu-daidai/rbbnp-dev")
```

## Quick Start

### Density Estimation

```{r density-quick}
# Generate sample data
X <- gen_sample_data(size = 500, dgp = "2_fold_uniform", seed = 123456)

# Estimate density with bias-aware confidence intervals
fit <- biasBound_density(X, h = 0.1, kernel.fun = "Schennach2004")

# View summary
fit
```

```{r density-plot}
# Visualize results
plot(fit)
```

The plot shows (the legend labels each element):

- **Estimate**: the estimated density $\hat{f}(x)$
- **Bias bound**: the range where $E[\hat{f}]$ may lie
- **95% CI**: the confidence interval

### Conditional Expectation (Regression)

```{r regression-quick}
# Generate regression data: Y = -X^2 + 3X + noise
Y <- -X^2 + 3*X + rnorm(500) * X

# Estimate conditional expectation
fit_reg <- biasBound_condExpectation(Y, X, h = 0.1, kernel.fun = "Schennach2004")

# Visualize
plot(fit_reg)
```

## Working with Results

Both functions return S3 objects with standard methods:
```{r methods-demo}
# Extract parameters
coef(fit)

# Get confidence intervals
head(confint(fit))

# For regression: get fitted values
head(fitted(fit_reg))
```

## Next Steps

- **[Density Estimation](density-estimation.html)**: Detailed guide to density estimation
- **[Regression](regression.html)**: Conditional expectation estimation
- **[Theory](theory.html)**: Mathematical background

## References

Schennach, S. M. (2020). A Bias Bound Approach to Non-parametric Inference. *The Review of Economic Studies*, 87(5), 2439-2472. [doi:10.1093/restud/rdz065](https://doi.org/10.1093/restud/rdz065)