### Install soilquality using pak

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Installs the development version of the soilquality package from GitHub using the pak package manager. Ensure pak is installed first.

```r
if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}

pak::pak("ccarbajal16/soilquality")
```

--------------------------------

### Install soilquality using devtools

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Installs the development version of the soilquality package from GitHub using the devtools package. Ensure devtools is installed first.

```r
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}

devtools::install_github("ccarbajal16/soilquality")
```

--------------------------------

### Launch Interactive Soil Quality Application

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Starts the interactive Shiny application for the soilquality package, providing a graphical user interface for analysis.

```r
# Start Shiny app for GUI-based analysis
run_sqi_app()
```

--------------------------------

### Basic Soil Quality Index (SQI) Calculation

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Loads example soil data, computes SQI using automatic property detection, and prints a summary of the results. Visualizes the SQI distribution and generates a comprehensive report.

```r
library(soilquality)

# Load example data
data(soil_ucayali)

# Compute SQI with automatic property detection
result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = c("Sand", "Silt", "Clay", "pH", "OM", "P", "K")
)

# View results summary
print(result)

# Visualize SQI distribution
plot(result, type = "distribution")

# Create comprehensive report
plot_sqi_report(result)
```

--------------------------------

### Interactive Tools

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Launch the interactive Shiny application for a user-friendly experience of SQI calculation and analysis.

```APIDOC
## Interactive Tools

### `run_sqi_app()`

**Description**: Launch interactive Shiny application with file upload, property selection, scoring configuration, AHP input, results display, and CSV download.
```

--------------------------------

### Interactive AHP Matrix Creation and SQI Calculation

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Creates an Analytic Hierarchy Process (AHP) matrix interactively to define indicator importance, then uses the computed weights for SQI calculation.

```r
# Create AHP matrix interactively
indicators <- c("pH", "OM", "P", "K")
ahp_matrix <- create_ahp_matrix(indicators, mode = "interactive")

# Use computed AHP weights in SQI calculation
result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = ahp_matrix$indicators,
  pairwise_matrix = ahp_matrix$weights
)
```

--------------------------------

### Access Function Documentation

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Access detailed documentation for any function within the soilquality package using the '?' operator or the help() function.

```r
?function_name
```

```r
help(function_name)
```

--------------------------------

### Browse Vignettes

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Open the vignettes for the soilquality package in your web browser to explore its features and usage.

```r
browseVignettes("soilquality")
```

--------------------------------

### Report Issues

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

If you encounter any problems or have suggestions, report them on the package's GitHub issues page.

```r
https://github.com/ccarbajal16/soilquality/issues
```

--------------------------------

### Scoring Constructors (User-facing)

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

User-friendly constructors for defining various scoring rules for soil properties.

```APIDOC
## Scoring Constructors (User-facing)

### `higher_better()`

**Description**: Constructor for higher-is-better scoring rules.

### `lower_better()`

**Description**: Constructor for lower-is-better scoring rules.

### `optimum_range()`

**Description**: Constructor for optimal range scoring with tolerance.

### `threshold_scoring()`

**Description**: Constructor for custom threshold-based scoring.

### `print.scoring_rule()`

**Description**: S3 print method for scoring rule objects.
```

--------------------------------

### Property Sets and Standard Rules

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Access pre-defined soil property sets and automatically generate standard scoring rules.

```APIDOC
## Property Sets and Standard Rules

### `soil_property_sets`

**Description**: Pre-defined collections: basic (pH, OM, P, K), standard (9 properties), comprehensive (14 properties), physical, chemical, fertility.

### `standard_scoring_rules()`

**Description**: Automatic scoring rule assignment based on property name patterns.
```

--------------------------------

### Cite the soilquality Package

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Use this R code to generate the citation for the soilquality package, which can be included in research publications.

```r
citation("soilquality")
```

--------------------------------

### SQI Calculation with Pre-defined Property Sets

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Computes the Soil Quality Index (SQI) using a pre-defined set of soil properties, such as the 'standard' set available in the package.

```r
# Use standard property set
result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = soil_property_sets$standard
)
```

--------------------------------

### SQI Computation Functions

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Core functions for computing the Soil Quality Index (SQI) using different workflows.

```APIDOC
## SQI Computation Functions

### `compute_sqi()`

**Description**: File-based SQI computation with CSV input/output.

### `compute_sqi_df()`

**Description**: In-memory SQI computation with data frame input.

### `compute_sqi_properties()`

**Description**: Enhanced SQI computation with property selection and constructor-based scoring.
```

--------------------------------

### Visualization Functions

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Functions for generating various plots and reports to visualize SQI results and indicator performance.

```APIDOC
## Visualization Functions

### `plot.sqi_result()`

**Description**: S3 plot method supporting five plot types:
  - "distribution": Histogram of SQI values with mean line
  - "indicators": Boxplots of indicator scores
  - "weights": Bar chart of AHP weights with CR annotation
  - "scree": Variance explained by principal components
  - "biplot": PCA biplot of observations and variables

### `plot_sqi_report()`

**Description**: Generate comprehensive 2x2 multi-panel visualization report.
```

--------------------------------

### AHP Weighting Functions

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Functions for calculating indicator weights using the Analytic Hierarchy Process (AHP) and managing AHP matrices.

```APIDOC
## AHP Weighting Functions

### `ahp_weights()`

**Description**: Calculate indicator weights from pairwise comparison matrix using eigenvalue decomposition.

### `create_ahp_matrix()`

**Description**: Interactive or programmatic AHP matrix creation with validation.

### `ratio_to_saaty()`

**Description**: Convert importance ratios to Saaty scale (1-9).

### `print.ahp_matrix()`

**Description**: S3 print method for AHP matrix objects.
```

--------------------------------

### Scoring Functions (Low-level)

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Low-level functions for normalizing and scoring individual soil properties based on different rules.

```APIDOC
## Scoring Functions (Low-level)

### `score_higher_better()`

**Description**: Normalize properties where higher values indicate better quality.

### `score_lower_better()`

**Description**: Normalize properties where lower values indicate better quality.

### `score_optimum()`

**Description**: Normalize properties with optimal range using linear or quadratic penalty.

### `score_threshold()`

**Description**: Custom piecewise linear scoring based on threshold-score pairs.

### `score_indicators()`

**Description**: Apply scoring functions to multiple indicators.
```

--------------------------------

### SQI Calculation with Custom Scoring Rules

Source: https://github.com/ccarbajal16/soilquality/blob/master/README.md

Computes SQI using custom scoring rules defined for specific soil properties, including optimal ranges, higher-is-better, lower-is-better, and threshold-based scoring.

```r
# Define custom scoring for specific properties
custom_rules <- list(
  pH = optimum_range(optimal = 6.5, tolerance = 1),
  OM = higher_better(),
  BD = lower_better(),
  P = threshold_scoring(
    thresholds = c(0, 10, 20, 50),
    scores = c(0, 0.5, 0.8, 1.0)
  )
)

result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = c("pH", "OM", "BD", "P"),
  scoring_rules = custom_rules
)
```

--------------------------------

### Data Handling Functions

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Functions for reading CSV files, standardizing numeric data, and safe type conversion.

```APIDOC
## Data Handling Functions

### `read_soil_csv()`

**Description**: Import soil data from CSV files with encoding handling.

### `standardize_numeric()`

**Description**: Z-score standardization of numeric columns.

### `to_numeric()`

**Description**: Safe type conversion with NA for failures.
```

--------------------------------

### PCA and MDS Selection

Source: https://github.com/ccarbajal16/soilquality/blob/master/NEWS.md

Function to perform Principal Component Analysis and select a Minimum Data Set (MDS) based on specified thresholds.

```APIDOC
## PCA and MDS Selection

### `pca_select_mds()`

**Description**: Perform PCA and select minimum data set based on variance and loading thresholds.
```

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