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freqTLS  

Frequentist Inference for Thermal Load Sensitivity Models
View on CRAN: Click here


Download and install freqTLS package within the R console
Install from CRAN:
install.packages("freqTLS")

Install from Github:
library("remotes")
install_github("cran/freqTLS")

Install by package version:
library("remotes")
install_version("freqTLS", "0.1.0")



Attach the package and use:
library("freqTLS")
Maintained by
Shinichi Nakagawa
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-07-21
Latest Update: 2026-07-21
Description:
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.
How to cite:
Shinichi Nakagawa (2026). freqTLS: Frequentist Inference for Thermal Load Sensitivity Models. R package version 0.1.0, https://cran.r-project.org/web/packages/freqTLS. Accessed 24 Oct. 2026.
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