---
title: "Introduction to CamelRatiosIndex"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to CamelRatiosIndex}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 9,
  fig.height = 6
)
```

## Overview

The **CamelRatiosIndex** package implements the multivariate-weighted indexing method proposed by Ayimah et al. (2023a, 2023b) for bank performance assessment using the CAMEL framework. The package provides:

- **`camel_index()`**: Computes composite year-on-year indices from CAMEL ratio data
- **`plot_camel_index()`**: Visualizes percentage differences across banks using ggplot2
- **Built-in data**: Example datasets from Ghanaian commercial banks (2015-2022)

This composite index is intended to offer regulators and policymakers a standardised, objective for monitoring bank performance over time and across institutions. Its ability to benchmark banks against a common base year enhances early-warning capabilities, enabling supervisory authorities to identify emerging weaknesses individual banks as well as systemic vulnerabilities within the industry.

## The CAMEL Framework

CAMEL is an internationally recognized framework for evaluating bank performance, comprising five dimensions:

| Dimension | Ratio | Direction |
|-----------|-------|-----------|
| **C**apital Adequacy | Ca | Higher = better |
| **A**sset Quality | Aq | Higher = worse (inverted) |
| **M**anagement Efficiency | Me | Higher = worse (inverted) |
| **E**arnings | Eq | Higher = better |
| **L**iquidity | Lm | Higher = worse (inverted) |

## Installation

```r
# Install from GitHub (development version)
# install.packages("remotes")
remotes::install_github("YOUR-USERNAME/CamelRatiosIndex")
```

## Quick Start

### Computing the CAMEL Index

```{r}
library(CamelRatiosIndex)

# Load built-in example data
data("camel_2015")
data("camel_2022")

# Compute the index
result <- camel_index(camel_2015, camel_2022)

# View the main output
result$index_table
```

### Accessing Detailed Results

```{r}
# Laspeyres-type indices (base year weights)
result$mw_lasp

# Paasche-type indices (current year weights)
result$mw_pash

# Communality weights from base year factor analysis
result$weights_base

# Eigenvalues
result$eigenvalues_base
```

### Visualizing Results

```{r}
# Basic plot
plot_camel_index(result)

# Highlight specific banks
plot_camel_index(result, highlight_banks = c("Absa", "Ecobank", "GCB"))

# Custom styling
plot_camel_index(
  result,
  title = "Ghanaian Banks: 2015 vs 2022",
  subtitle = "CAMEL Index Performance",
  theme_fn = ggplot2::theme_bw
)
```

## Data Format

### Data Frame Input

When using data frames, the first column must be the bank identifier, followed by the five CAMEL ratios:

```{r}
# Example structure
head(camel_2015)
```

### Matrix Input

For matrices, supply bank names separately:

```{r}
base_mat <- as.matrix(camel_2015[, -1])
curr_mat <- as.matrix(camel_2022[, -1])
banks <- camel_2015$Bank

result2 <- camel_index(base_mat, curr_mat, bank_names = banks)
```

## Understanding the Output

The `camel_index()` function returns a rich object with multiple components:

```{r}
# Print overview
print(result)

# Detailed summary
summary(result)
```

### Key Metrics

- **I_mw**: Composite index (base = 100). Values > 100 indicate improvement; < 100 indicate decline.
- **PD**: Percentage difference from base year. Positive = improvement.
- **mw_lasp**: Laspeyres-type index using base year communality weights.
- **mw_pash**: Paasche-type index using current year communality weights.
- **weights_base/current**: Communality values from robust factor analysis, representing the proportion of variance explained by each CAMEL ratio.

## Methodology

The index computation follows these steps:

1. **Transform ratios**: Invert Aq, Me, and Lm so that higher values always indicate better performance.
2. **Compute correlations**: Build correlation matrices for base and current years.
3. **Extract eigenvalues**: Determine the number of factors (eigenvalues > 1).
4. **Robust factor analysis**: Use [robustfa::FaCov()] with OGK covariance estimation.
5. **Extract communalities**: Use communalities as weights for each CAMEL ratio.
6. **Compute indices**: Calculate Laspeyres and Paasche indices, then average them.
7. **Scale to base 100**: Final composite index with percentage difference.

## References

Ayimah, J. C., Mettle, F. O., Nortey, E. N., & Minkah, R. (2023a). A Robust Multivariate Weighting Technique for Computing a Measure for Inflation. *African Journal of Technical Education and Management*, 3(1), 1-15. Retrieved from	https://ajtem.com/index.php/ajtem/article/view/53. 

Ayimah, J.C. (2023b). Computing Multivariate-Weighted Consumer Price Index: An Application Manual in R. B P International. DOI: 10.9734/bpi/mono/978-81-19315-32-1.
DOI:http://dx.doi.org/10.9734/bpi/mono/978-81-19315-32-1
