earthUI

Interactive GUI for Enhanced Adaptive Regression Through Hinges (EARTH) models.

earthUI provides a Shiny-based graphical interface for the earth package, making it easy to build, explore, and export multivariate adaptive regression spline models without writing code.

Installation

# Install remotes if needed
install.packages("remotes")

# Install earthUI from GitHub
remotes::install_github("wcraytor/earthUI")

Optional: Report Export

To export reports (HTML, PDF, or Word), install the Quarto CLI and the R package:

install.packages("quarto")

For PDF reports, a LaTeX distribution is also required:

install.packages("tinytex")
tinytex::install_tinytex()

Quick Start

library(earthUI)
launch()

This opens an interactive Shiny application where you can:

1. Import data from CSV or Excel files
2. Configure variables — select target, predictors, flag categoricals, and designate special column types (contract date, DOM, concessions, latitude/longitude, living area, lot size, age, etc.)
3. Set model parameters — degree, interaction constraints, advanced tuning
4. View results — coefficients, variable importance, partial dependence, diagnostics
5. Export reports — publication-quality HTML, PDF, or Word reports via Quarto

Appraisal Mode

For real estate appraisal workflows, earthUI provides:

• RCA adjustments — regression-based comparable adjustments with CQA scoring and subject value interpolation
• Sales Comparison Grid — multi-sheet Excel workbook with formulas for Net Sale Price, grouped adjustments (location, site, age), residual feature breakdowns, and adjusted sale price. Protected sheets with unlocked input cells for appraiser entry.

Demo Data

earthUI includes a demo appraisal dataset (Appraisal_1.csv) with residential sales data. Access it with:

demo_file <- system.file("extdata", "Appraisal_1.csv", package = "earthUI")
df <- import_data(demo_file)

Programmatic Use

All analytical functions are available independently of the Shiny app:

library(earthUI)

# Load the demo dataset
demo_file <- system.file("extdata", "Appraisal_1.csv", package = "earthUI")
df <- import_data(demo_file)
cats <- detect_categoricals(df)

# Fit a model
result <- fit_earth(df, target = "sale_price",
                    predictors = c("living_sqft", "lot_size", "age"))

# Examine results
format_summary(result)
format_variable_importance(result)

# Plot
plot_variable_importance(result)
plot_contribution(result, 1)

License

AGPL-3