Package: VeraCrop 0.1.0

Elham Elahifard

VeraCrop: Yield Gap Analysis Using Comparative Performance Analysis

Implements automated variable-type detection, preprocessing, encoding, scaling, model diagnostics, variable selection, and yield gap computation for agricultural comparative performance analysis (CPA). The comparative performance analysis approach is described in de Bie (2004) <doi:10.1016/j.scienta.2003.11.017>. For an overview of yield gap assessment methodologies, see Kamkar et al. (2025) <doi:10.1016/j.agsy.2025.104392>.

Authors:Abolfazl Derakhshan [aut, cph], Elham Elahifard [aut, cre], Aghajan Bahadori [ctb], Shaban Zarei [ctb], Hossein Norouzi [ctb]

VeraCrop_0.1.0.tar.gz
VeraCrop_0.1.0.zip(r-4.7-any)VeraCrop_0.1.0.zip(r-4.6-any)
VeraCrop_0.1.0.tar.gz(r-4.7-any)VeraCrop_0.1.0.tar.gz(r-4.6-any)
VeraCrop_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
VeraCrop/json (API)

# Install 'VeraCrop' in R:

install.packages('VeraCrop', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

sugarcane1 - Sugarcane Yield Trial Data
wheat1 - Wheat Yield Trial Data
wheat2 - Wheat Yield Trial Data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 42 exports 17 dependencies

Last updated from:e1dcd96a1f. Checks:6 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	194
source / vignettes	OK	270
linux-release	OK	180
windows-devel	OK	109
windows-release	OK	145
wasm-release	OK	141

Exports:add_missing_indicators adjust_intercept back_transform_coefficients back_transform_data bootstrap_cpa calculate_minmax_effects calculate_opt_values calculate_relative_importance check_assumptions check_data_ready detect_variable_types encode_variables filter_variables group_high_cardinality handle_missing_values missing_summary model_diagnostics pareto_analysis plot_all_graphs plot_contribution_shares plot_minmax_effects plot_observed_vs_predicted plot_relative_importance plot_yield_gap_distribution prep_yield_gap preprocessing_report remove_constant_vars remove_highly_correlated remove_near_zero_variance remove_non_predictors save_field_level_excel save_results_excel scale_new_data scale_variables select_vars_aic select_vars_ftest summary_scaling trim_whitespace validate_model_with_cv validate_preprocessing yield_gap_analysis yield_gap_components

Dependencies:cli cpp11 farver ggplot2 glue gtable isoband labeling lifecycle R6 RColorBrewer rlang S7 scales vctrs viridisLite withr

Getting Started with VeraCrop

Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-09-28

Help page	Topics
Add Missing Indicators	add_missing_indicators
Adjust Intercept After Back-Transformation	adjust_intercept
Back-transform Coefficients to Original Scale	back_transform_coefficients
Back-transform Data	back_transform_data
Bootstrap Confidence Intervals for the CPA Yield Gap Decomposition	bootstrap_cpa
Calculate Min-Max Effects of Variables on Yield	calculate_minmax_effects
Calculate Optimal Values for Selected Variables	calculate_opt_values
Calculate Relative Importance of Variables	calculate_relative_importance
Check Linear Regression Assumptions	check_assumptions
Quick Check for Data Readiness	check_data_ready
Detect variable types	detect_variable_types
Encode variables for statistical models	encode_variables
Apply All Filtering Steps	filter_variables
Group levels of high-cardinality categorical variables	group_high_cardinality
Handle Missing Values	handle_missing_values
Quick Missing Value Summary	missing_summary
Quick Model Summary with Assumption Checks	model_diagnostics
Pareto Analysis of Yield Gap Contributions	pareto_analysis
Generate All Yield Gap Plots	plot_all_graphs
Plot Variable Contributions to Yield Gap	plot_contribution_shares
Plot Min-Max Effects of Variables on Yield	plot_minmax_effects
Plot Observed vs. Predicted Yield	plot_observed_vs_predicted
Plot Relative Importance of Variables	plot_relative_importance
Plot Distribution of Yield Gap Across Fields	plot_yield_gap_distribution
Main Preprocessing Pipeline for Yield Gap Analysis	prep_yield_gap
Create a Preprocessing Report	preprocessing_report
Remove Constant Variables	remove_constant_vars
Remove Highly Correlated Variables	remove_highly_correlated
Remove Near Zero Variance Variables	remove_near_zero_variance
Remove Non-Predictor Variables	remove_non_predictors
Save Field-Level Yield Gap Analysis to Excel	save_field_level_excel
Save Yield Gap Analysis Results to Excel	save_results_excel
Scale New Data Using Existing Parameters	scale_new_data
Scale Continuous Variables	scale_variables
Variable Selection via AIC-Based Stepwise Regression	select_vars_aic
Forward Stepwise Variable Selection Using F-test	select_vars_ftest
Sugarcane Yield Trial Data	sugarcane1
Get Scaling Summary	summary_scaling
Trim leading/trailing whitespace from character columns	trim_whitespace
k-Fold Cross-Validation for Yield Gap Model	validate_model_with_cv
Validate Preprocessing Results	validate_preprocessing
Wheat Yield Trial Data (Clean)	wheat1
Wheat Yield Trial Data (Small, High-Dimensional, Real-World Messy)	wheat2
Complete Yield Gap Analysis Using CPA Method	yield_gap_analysis
Calculate Yield Gap Components from CPA Table	yield_gap_components

Package: VeraCrop 0.1.0

VeraCrop: Yield Gap Analysis Using Comparative Performance Analysis

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)