freqTLS: Frequentist Inference for Thermal Load Sensitivity Models

freqTLS is a maximum-likelihood / profile-likelihood complement to the Bayesian bayesTLS package. It fits single-stage four-parameter logistic (4PL) thermal-load-sensitivity (thermal death-time) models via Template Model Builder (TMB), parameterised directly in CTmax and z (thermal sensitivity), so that both quantities can be profiled. It returns prior-free Wald, profile-likelihood, or parametric-bootstrap confidence intervals for binomial and beta-binomial survival counts and the experimental Beta continuous-proportion family. Formula shape effects, limited random intercepts, and deterministic heat-injury prediction are also experimental; censored-time, hurdle-productivity, posterior, and fitted repair models remain outside freqTLS.

Experimental software

Use freqTLS at your own risk. Results and APIs may be incorrect or change. Users are responsible for checking their data, design, model specification, convergence, identifiability, diagnostics, and interpretation. Important analyses should be independently refitted and cross-checked with the Bayesian sister package bayesTLS (source repository). Agreement is a cross-check, not proof of correctness; shared data or model errors can make both packages agree.

Credit and origins

The thermal-load-sensitivity modelling framework and the direct mapping from the 4PL midpoint slope to z and CTmax are due to Noble, Arnold and Pottier (the bayesTLS package). freqTLS contributes the TMB maximum-likelihood likelihood and the profile-likelihood machinery. Engineering patterns are adapted from drmTMB (GPL-3) with attribution in the relevant source files.

See also

Useful links:

• https://github.com/itchyshin/freqTLS

• https://itchyshin.github.io/freqTLS/

• Report bugs at https://github.com/itchyshin/freqTLS/issues

Author

Maintainer: Shinichi Nakagawa itchyshin@gmail.com (ORCID) [copyright holder]

Authors:

• Shinichi Nakagawa itchyshin@gmail.com (ORCID) [copyright holder]

• Pieter A. Arnold (ORCID) (co-author of the bayesTLS framework)

• Patrice Pottier (ORCID) (co-author of the bayesTLS framework)

• Daniel W. A. Noble (ORCID) (senior author of the bayesTLS thermal-load-sensitivity framework)