VeraCrop provides an end-to-end workflow for yield gap analysis using Comparative Performance Analysis (CPA): automatic variable-type detection, preprocessing (missing-value handling, encoding, scaling, filtering), regression diagnostics, variable selection with cross-validation, and yield gap decomposition with reporting.
# Install from source tarball
install.packages("VeraCrop_0.1.0.tar.gz", repos = NULL, type = "source")library(VeraCrop)
# 1. Preprocess: type detection, imputation, encoding, scaling, filtering
prep <- prep_yield_gap(my_data, response_var = "Yield")
# 2. Check regression assumptions
model <- lm(Yield ~ ., data = prep$data)
diag <- check_assumptions(model, data = prep$data, plot = FALSE)
# 3. Run the full yield gap analysis
result <- yield_gap_analysis(prep$data, response = "Yield")
result$yield_gap
# 4. Visualize and export
plot_all_graphs(result)
save_results_excel(result, output_path = "yield_gap_results.xlsx")See vignette("getting-started", package = "VeraCrop")
for a complete worked example.
| Step | Function |
|---|---|
| Variable type detection | detect_variable_types() |
| Missing value handling | handle_missing_values() |
| Encoding | encode_variables() |
| Scaling | scale_variables() |
| Filtering | remove_constant_vars(),
remove_near_zero_variance(),
remove_highly_correlated() |
| Full preprocessing pipeline | prep_yield_gap() |
| Regression diagnostics | check_assumptions() |
| Variable selection | select_vars_ftest() |
| Cross-validation | validate_model_with_cv() |
| Yield gap analysis | yield_gap_analysis() |
| Reporting | save_results_excel(),
save_field_level_excel(),
plot_all_graphs() |
MIT © Abolfazl Derakhshan