---
title: "Introduction to SPRT package"
author: "Your Name"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to SPRT package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Overview

The **Sequential Probability Ratio Test (SPRT)**, proposed by Abraham Wald (1945), is a method of hypothesis testing that evaluates data **sequentially** rather than fixing the sample size in advance.  
It is widely used in **quality control, clinical trials, and agricultural research** where early stopping can save both time and resources.

---

# Theoretical Background

We test simple hypotheses:

\[
H_0: \theta = \theta_0 \quad \text{vs.} \quad H_1: \theta = \theta_1
\]

After \(n\) observations \((X_1, X_2, \ldots, X_n)\), the **likelihood ratio** is:

\[
\Lambda_n = \prod_{i=1}^n \frac{f(X_i; \theta_1)}{f(X_i; \theta_0)}
\]

or equivalently,

\[
\log \Lambda_n = \sum_{i=1}^n \log \left( \frac{f(X_i; \theta_1)}{f(X_i; \theta_0)} \right).
\]

---

## Derivation of Decision Boundaries

To control Type I error (\(\alpha\)) and Type II error (\(\beta\)), Wald proposed comparing the likelihood ratio \(\Lambda_n\) with two thresholds.

- Accept \(H_0\) if \(\Lambda_n \leq B\)  
- Accept \(H_1\) if \(\Lambda_n \geq A\)  
- Continue sampling otherwise

where  

\[
A = \frac{1-\beta}{\alpha}, 
\qquad 
B = \frac{\beta}{1-\alpha}.
\]

### Why these thresholds?

1. **Type I error control**: Probability of wrongly rejecting \(H_0\) should not exceed \(\alpha\).  
   This sets the *upper boundary* \(A\).

2. **Type II error control**: Probability of wrongly rejecting \(H_1\) should not exceed \(\beta\).  
   This sets the *lower boundary* \(B\).

Thus, the SPRT is designed so that:

\[
P(\text{Reject } H_0 | H_0 \text{ true}) \leq \alpha, \qquad
P(\text{Reject } H_1 | H_1 \text{ true}) \leq \beta.
\]

This guarantees the desired error rates in the **sequential** framework.

---

# Example 1: Binomial Data

Suppose we want to test whether the probability of success is \(p_0 = 0.1\) vs \(p_1 = 0.3\).

```{r, message=FALSE}
library(SPRT)

# Simulated binary outcomes (1 = success, 0 = failure)
x <- c(0,0,1,0,1,1,1,0,0,1,0,0)

# Run SPRT
res <- sprt(x, alpha = 0.05, beta = 0.1, p0 = 0.1, p1 = 0.3)

# Print results
res

# Plot sequential test path
sprt_plot(res)


# Observations from a Normal distribution
x1 <- c(52, 55, 58, 63, 66, 70, 74)

result1 <- sprt(
  x1,
  alpha = 0.05,
  beta = 0.1,
  p0 = 50,
  p1 = 65,
  dist = "normal",
  sigma = 10
)

result1
sprt_plot(result1)
# Yields from a fertilizer trial (kg/plot)
yield <- c(47, 50, 52, 49, 58, 61, 63, 54, 57)

fert_test <- sprt(
  yield,
  alpha = 0.05,
  beta = 0.1,
  p0 = 45,
  p1 = 55,
  dist = "normal",
  sigma = 8
)

fert_test
sprt_plot(fert_test)