This package is considered a duplicate. The official version of this package is found at:https://agrobioestat.r-universe.dev/leafareaR

Package: leafareaR 0.0.1

Joao Everthon da Silva Ribeiro

leafareaR: Leaf Area Modeling, Evaluation, and Prediction

Tools for leaf area estimation based on leaf length, leaf width, and observed leaf area. The package supports data validation, predictor generation, descriptive statistics, exploratory graphics, scatterplot matrices, linear models, nonlinear models, mixed models, model evaluation, ranking, equation generation, prediction, export of results and plots, and an interactive 'shiny' application. Methods implemented in the package are aligned with non-destructive allometric workflows described by Ribeiro et al. (2024) <doi:10.1016/j.sajb.2024.07.006>, Ribeiro et al. (2023) <doi:10.1590/1807-1929/agriambi.v27n3p209-215>, and Ribeiro et al. (2025) <doi:10.1590/0103-8478cr20230550>.

Authors:Joao Everthon da Silva Ribeiro [aut, cre], Ester dos Santos Coelho [aut], Toshik Iarley da Silva [aut]

leafareaR_0.0.1.tar.gz
leafareaR_0.0.1.tar.gz(r-4.7-any)leafareaR_0.0.1.tar.gz(r-4.6-any)
leafareaR_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
leafareaR/json (API)

# Install 'leafareaR' in R:

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

Bug tracker:https://github.com/agrobioestat/leafarear/issues

Datasets:

leafarea_sample - Example dataset for leaf area modeling

On CRAN:

2.70 score 57 exports 57 dependencies

Last updated from:6f87a92b53. Checks:4 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	153
source / vignettes	OK	199
linux-release-x86_64	OK	152
wasm-release	OK	158

Exports:la_abs_bias_metric la_add_equation_to_results la_bias la_build_equation la_ccc la_create_derived la_d la_descriptive_default la_descriptive_stats la_evaluate_linear_models la_evaluate_mixed_models la_evaluate_model la_evaluate_nonlinear_models la_extract_coefficients la_feature_display_names la_feature_labels la_fit_linear_models la_fit_mixed_models la_fit_nonlinear_models la_input_overview la_linear_fitted_values la_linear_formulas la_list_derived la_mae la_mape la_matrixplot la_matrixplot_default la_metric_table la_mixed_coefficients la_mixed_fitted_values la_mixed_formulas la_mse la_nonlinear_coefficients la_nonlinear_fitted_values la_nonlinear_specs la_nse la_plot_observed_predicted la_plot_residual_histogram la_plot_residual_qq la_plot_residuals la_plot_scatter la_plot_scatter_set la_predict_from_results la_predict_linear_model la_predict_mixed_model la_predict_model la_predict_nonlinear_model la_predict_top_ranked la_r la_r_squared la_rank_models la_rank_models_by_metrics la_rank_models_weighted la_rmse la_top_models la_validate_input run_leafareaR_app

Dependencies:base64enc boot bslib cachem cli commonmark cpp11 digest farver fastmap fontawesome fs ggplot2 glue gtable htmltools httpuv insight isoband jquerylib jsonlite labeling later lattice lifecycle lme4 lmerTest magrittr MASS Matrix memoise mime minqa MuMIn nlme nloptr numDeriv otel promises R6 rappdirs rbibutils RColorBrewer Rcpp RcppEigen Rdpack reformulas rlang S7 sass scales shiny sourcetools vctrs viridisLite withr xtable

Getting started with leafareaR

Rendered fromleafareaR-introduction.Rmdusingknitr::rmarkdown

Last update: 2026-07-06
Started: 2026-07-06

Help page	Topics
Calculate absolute bias	la_abs_bias_metric
Add equation information to a results table	la_add_equation_to_results
Calculate prediction bias	la_bias
Build a readable equation from a fitted model	la_build_equation
Calculate Lin's concordance correlation coefficient	la_ccc
Create derived leaf parameters	la_create_derived
Calculate Willmott's index of agreement	la_d
Summarize the default leaf area variables	la_descriptive_default
Calculate descriptive statistics for selected variables	la_descriptive_stats
Evaluate all linear models from a 'la_fit_linear_models()' object	la_evaluate_linear_models
Evaluate all mixed models from a 'la_fit_mixed_models()' object	la_evaluate_mixed_models
Evaluate a single fitted model	la_evaluate_model
Evaluate all nonlinear models from a 'la_fit_nonlinear_models()' object	la_evaluate_nonlinear_models
Extract model coefficients	la_extract_coefficients
Display labels for leaf variables	la_feature_display_names
Display labels for leaf variables	la_feature_labels
Fit candidate linear models for leaf area estimation	la_fit_linear_models
Fit candidate linear mixed-effects models for leaf area estimation	la_fit_mixed_models
Fit multiple nonlinear models	la_fit_nonlinear_models
Summarize a validated leaf area dataset	la_input_overview
Extract fitted values from linear model results	la_linear_fitted_values
List default linear model formulas	la_linear_formulas
List available derived variables	la_list_derived
Calculate mean absolute error	la_mae
Calculate mean absolute percentage error	la_mape
Create a matrix plot for selected variables	la_matrixplot
Create a default matrix plot for leaf variables	la_matrixplot_default
Calculate a standard metric table from observed and predicted values	la_metric_table
Extract coefficients from a mixed model	la_mixed_coefficients
Extract fitted values from mixed-model results	la_mixed_fitted_values
List default mixed-model formulas	la_mixed_formulas
Calculate mean squared error	la_mse
Return coefficients from a selected nonlinear model	la_nonlinear_coefficients
Extract observed, fitted values and residuals for a selected nonlinear model	la_nonlinear_fitted_values
Default nonlinear model specifications	la_nonlinear_specs
Calculate Nash-Sutcliffe efficiency	la_nse
Observed versus predicted leaf area plot	la_plot_observed_predicted
Histogram of residuals	la_plot_residual_histogram
QQ plot of residuals	la_plot_residual_qq
Residuals versus fitted values plot	la_plot_residuals
Scatter plot between two selected variables	la_plot_scatter
Scatter plots for multiple selected predictors against leaf area	la_plot_scatter_set
Predict using one selected model from a fit object	la_predict_from_results
Predict from a linear model	la_predict_linear_model
Predict from a mixed model	la_predict_mixed_model
Predict from a fitted model	la_predict_model
Predict from a nonlinear model	la_predict_nonlinear_model
Predict from the top-ranked model	la_predict_top_ranked
Calculate Pearson correlation coefficient	la_r
Calculate coefficient of determination	la_r_squared
Rank models using a simple metric priority rule	la_rank_models
Rank models by average metric positions	la_rank_models_by_metrics
Rank models using a weighted score	la_rank_models_weighted
Calculate root mean squared error	la_rmse
Select the top models from a ranking table	la_top_models
Validate and standardize input data for leaf area analysis	la_validate_input
Example dataset for leaf area modeling	leafarea_sample
Launch the built-in Shiny application	run_leafareaR_app

Package: leafareaR 0.0.1

leafareaR: Leaf Area Modeling, Evaluation, and Prediction

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Readme and manuals

Help Manual

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