---
title: "Quick Start"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Quick Start}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- markdownlint-disable MD024 -->

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

Get up and running with LOWESS in minutes.

## Basic Smoothing

Smooth a noisy sine wave — the kind of signal where LOWESS shines. Each
example recovers the underlying trend from 100 points of Gaussian noise.

```{r quickstart_1}
library(rfastlowess)

# 100-point noisy sine wave
set.seed(42)
x <- seq(0, 2 * pi, length.out = 100)
y <- sin(x) + rnorm(100, sd = 0.3)

model <- Lowess(fraction = 0.3, iterations = 3)
result <- fit(model, x, y)

cat(sprintf("First smoothed value: %.4f (true: %.4f)\n",
            result$y[1], sin(x[1])))
```

---

## With Confidence Intervals

```{r quickstart_2}
library(rfastlowess)
set.seed(42)
x <- seq(0, 2 * pi, length.out = 100)
y <- sin(x) + rnorm(100, sd = 0.3)

model <- Lowess(
    fraction = 0.5,
    iterations = 3,
    outputs = "diagnostics",
    intervals = intervals_opts(confidence = 0.90, prediction = 0.99)
)
result <- fit(model, x, y)

cat("Smoothed (first 5):", head(result$y, 5), "\n")
cat("CI lower (first 5):", head(result$confidence_lower, 5), "\n")
cat("CI upper (first 5):", head(result$confidence_upper, 5), "\n")
cat("R2:", result$diagnostics$r_squared, "\n")
```

---

## Handling Outliers

LOWESS can robustly handle outliers through iterative reweighting:

```{r quickstart_3}
library(rfastlowess)

x_out <- seq(1, 6, length.out = 6)
y_with_outlier <- c(2.0, 4.0, 6.0, 50.0, 10.0, 12.0)

model <- Lowess(
    fraction = 0.7,
    iterations = 5,
    robustness_method = "bisquare",
    outputs = "weights"
)
result <- fit(model, x_out, y_with_outlier)

# Outliers will have low robustness weights
for (i in seq_along(result$robustness_weights)) {
    if (result$robustness_weights[i] < 0.5)
        cat(sprintf("Point %d is likely an outlier (weight: %.3f)\n",
                    i, result$robustness_weights[i]))
}
```

---

## Streaming Mode

For datasets too large to fit in memory, stream them in fixed-size chunks with
overlap.

```{r quickstart_4}
library(rfastlowess)
set.seed(42)
x <- seq(0, 10 * pi, length.out = 5000)
y <- sin(x / pi) * exp(-x / 30) + rnorm(5000, sd = 0.15)

model <- StreamingLowess(
    fraction = 0.2,
    chunk_size = 1000,
    overlap = 100,
    merge_strategy = "weighted_average"
)

chunk_size <- 1000
for (start in seq(1, 4001, by = chunk_size)) {
    end <- min(start + chunk_size - 1, length(x))
    process_chunk(model, x[start:end], y[start:end])
}
result <- finalize(model)
cat(sprintf("Smoothed %d points in streaming mode\n", length(result$y)))
```

---

## Next Steps

| Topic | Link |
| --- | --- |
| How LOWESS works | [Concepts](concepts.html) |
| All parameters explained | `?Lowess` |
| Batch vs Streaming vs Online | [Execution Modes](adapter-choice.html) |
| Edge handling | [Boundary](boundary.html) |
| Outlier handling in depth | [Robustness](robustness.html) |

```{r session-info}
sessionInfo()
```