Bootstrap-Calibrated Goodness-of-Fit Test for Simplex Regression // Implements the bootstrap-calibrated local-influence goodness-of-fit test for simplex regression models with constant or varying dispersion, following the local influence approach of Zhu and Zhang (2004) doi:10.1093/biomet/91.3.579 and the simplex regression model of Barndorff-Nielsen and Jorgensen (1991) doi:10.1016/0047-259X(91)90008-P. The test statistic aggregates individual local-influence measures under case-weight perturbation. Because the first-order asymptotic normal calibration is severely liberal in finite samples, a parametric bootstrap calibration is provided that restores accurate size control and delivers high power against omitted covariates, neglected dispersion, and distributional misspecification. Plotting functions reproduce the figures and tables of the companion methodological paper. Computational kernels are implemented in 'C++' via 'Rcpp' and 'RcppArmadillo' for speed, and two real datasets are bundled.