---
title: "Upcoming Features in corrselect 2.1.0"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Upcoming Features in corrselect 2.1.0}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

# Upcoming Features in `corrselect` 2.1.0

This vignette introduces features that will be available in version 2.1.0 of the `corrselect` package. These enhancements aim to provide more flexibility and alternative strategies for variable subset selection.

## Spectral Method (Prototype)

A new selection strategy based on spectral clustering is currently in development. This approach performs a normalized spectral clustering on the correlation matrix to identify sets of weakly correlated variables.

### Rationale

Unlike local or exhaustive search algorithms, spectral clustering provides a global approximation that can rapidly identify candidate subsets with minimal internal association.

### Overview of Steps

The algorithm follows these steps:

1. **Similarity matrix** from absolute correlations: \( S_{ij} = 1 - |r_{ij}| \)
2. **Degree vector**: \( D_i = \sum_j S_{ij} \)
3. **Normalized Laplacian**: \( L = I - D^{-1/2} S D^{-1/2} \)
4. **Eigen decomposition** of \( L \)
5. **K-means clustering** in the reduced eigenvector space
6. **Validation** of each cluster based on correlation threshold and forced variables

### Basic Example

```{r spectral-prototype, eval=FALSE}
set.seed(1)
mat <- matrix(rnorm(100), ncol = 10)
colnames(mat) <- paste0("V", 1:10)
cmat <- cor(mat)

res <- MatSelect(cmat, threshold = 0.5, method = "spectral")
res
```

### Customizing the Number of Clusters

You can pass an integer `k` to override the default number of clusters:

```{r spectral-k-param, eval=FALSE}
res <- MatSelect(cmat, threshold = 0.5, method = "spectral", k = 4)
```

Note that this method is still under testing and might change before release.

## Availability

This feature will be available in version **2.1.0**. If you're interested in testing it early, you can install the development version from GitHub:

```r
# install.packages("devtools")
devtools::install_github("gcol33/corrselect")
```

I welcome feedback and suggestions via GitHub issues or direct contact.