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

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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```

## Installation

```{r eval=FALSE}
# Install from GitHub
# install.packages("remotes")
remotes::install_github("cttir/hyperspectR")
```

## Loading the Package

```{r setup}
library(hyperspectR)
```

## Creating and Inspecting a Cube

hyperspectR provides synthetic data generators for testing and demonstration.
`hs_example_cube()` creates a 30x30 pixel tissue scene with 61 spectral bands
spanning 430-910 nm:

```{r}
cube <- hs_example_cube()
print(cube)
```

Check the dimensions:

```{r}
dim(cube)
```

The summary provides per-band statistics:

```{r}
s <- summary(cube)
s$wavelength_range
s$data_range
```

## Subsetting

You can subset cubes spatially and spectrally while preserving the class:

```{r}
sub <- cube[1:15, 1:15, 1:30]
dim(sub)
```

## Visualization

### RGB Composite

```{r}
ggplot2::autoplot(cube, type = "rgb")
```

### Single Band Image

```{r}
hs_plot_image(cube, wavelength = 550)
```

### Mean Spectrum

```{r}
hs_plot_spectra(cube, pixels = "mean", show_sd = TRUE)
```

## Computing Tissue Indices

The package provides a research hemoglobin fraction estimate and dimensionless band ratios. These outputs are not validated clinical measurements:

```{r}
sto2 <- hs_sto2(cube)
hs_plot_index(sto2, title = "Fitted Hb fraction (%)", palette = "sto2")
```

### Clinical Panel Display

```{r fig.width=10, fig.height=4}
hs_plot_clinical(cube, indices = c("sto2", "npi", "thi"))
```

## Interactive Exploration

Launch the Shiny app for interactive analysis:

```{r eval=FALSE}
hs_run_app(cube)
```

## Next Steps

- Read real data with `hs_read_envi()` or `hs_read_cube()`
- Preprocess with `hs_smooth()`, `hs_snv()`, `hs_msc()`
- Perform PCA with `hs_pca()` or MNF with `hs_mnf()`
- Classify with `hs_sam()` or fit chromophores with `hs_beer_lambert()`

# Citation

To cite hyperspectR in publications, please use:

```bibtex
@Manual{,
  title = {hyperspectR: Hyperspectral Imaging Analysis for Biomedical Applications},
  author = {R. Heller and V. Forstmeier},
  year = {2026},
  note = {R package version 0.1.0},
  url = {https://github.com/cttir/hyperspectR},
}
```