Frequentist Inference for Thermal Load Sensitivity Models // A maximum-likelihood implementation of the thermal-load- sensitivity framework for thermal death-time modelling introduced by Noble, Arnold and Pottier in the 'bayesTLS' package, providing the frequentist counterpart to that Bayesian workflow. The modelling idea and the four-parameter logistic parameterisation are theirs; 'freqTLS' contributes a 'Template Model Builder' ('TMB') likelihood whose midpoint is written directly in terms of critical thermal maximum ('CTmax') and thermal sensitivity (z) so both headline quantities are estimable, and reports uncertainty through a unified trio of frequentist intervals -- Wald (delta), profile-likelihood, and bootstrap -- for binomial and beta-binomial survival counts and beta-distributed proportions. Column and formula interfaces support fixed and grouped designs plus limited independent random intercepts; prediction includes survival curves and deterministic heat-injury scenarios. Equivalence claims are restricted to the matched relative-threshold, constant-shape 'bayesTLS' configuration.