R Observatory: Metrics for bayesTLS version 1.0.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

bayesTLS

1.0.0Package record

Joint Bayesian 4PL Models for Thermal Load Sensitivity

0packages depend

958downloads / year

59.0%test coverage

13/13flavors OK

Overview

About

Maintained by Daniel W. A. NobleCRAN page ↗GitHub ↗Issues ↗

Fits joint Bayesian four-parameter logistic (4PL) models to thermal-tolerance proportion data, extracts the classical thermal load sensitivity quantities (z, CTmax at 1 hour, T_crit) with full posterior uncertainty, and predicts heat-injury accumulation and survival under fluctuating temperature regimes with optional Sharpe-Schoolfield repair. Models are fitted with 'Stan' via the 'brms' package. Implements the framework described in Noble, Arnold, Nakagawa and Pottier (in preparation).

Changes in 1.0.0

First release.

bayesTLS fits joint Bayesian four-parameter logistic (4PL) models to

thermal-tolerance data and derives the classical thermal death time / thermal

load sensitivity quantities from the posterior, so that every downstream

quantity carries full uncertainty and is mutually consistent within a draw.

Model fitting

standardize_data() maps a raw thermal-tolerance dataset (binomial counts or continuous proportions) onto the columns the model expects, and records the duration unit and centring used.
fit_4pl() fits the joint 4PL with brms, in either the *midpoint* parameterisation or the *direct* CTmax/z parameterisation (ctmax = ~ ..., z = ~ ...), with moderators allowed on any sub-parameter.

Show all changes in 1.0.0Hide the rest of the changes in 1.0.0

make_4pl_formula() and make_4pl_priors() expose the underlying brms formula and default priors for inspection or customisation.

Deriving thermal load sensitivity

tls() derives z, CTmax and T_crit per moderator group from any fitted 4PL — including hand-written brms models — by evaluating the sub-parameters on a moderator x temperature grid with brms::posterior_linpred().
tls_z(), tls_ctmax() and tls_tcrit() return the individual quantities; derive_tdt_curve() and derive_tdt_landscape() give the TDT curve and landscape.

Prediction under fluctuating temperatures

predict_heat_injury() accumulates heat injury over an arbitrary temperature trace, with optional Sharpe-Schoolfield repair (repair_rate_schoolfield()).
predict_survival_curves() propagates that injury to survival.
make_temperature_scenarios() builds fluctuating-temperature scenarios.

Two-stage comparison

ts_stage1(), ts_stage2(), ts_curve() and ts_ci() implement the conventional two-stage TDT workflow, for direct comparison against the joint model.

Plotting and diagnostics

plot_tdt_curve(), plot_tdt_landscape(), plot_heat_injury(), plot_survival_curves(), plot_temperature_scenarios() and friends, all on a shared theme_tdt().
diagnose_tdt_fit() and bayes_R2_tls() for fit checking.

Data

Four publicly available example datasets spanning lethal and sub-lethal

endpoints: aphid_tdt, dsuzukii, snowgum_psii and zebrafish_o2.

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("bayesTLS", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("daniel1noble/bayesTLS")

Development version from daniel1noble/bayesTLS. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-bayestls

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-bayesTLS

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-bayesTLS

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 2.5 min · r-oldrel-macos-x86_64

Status changes

OK13 OK2026-09-08
ERROR12 OK, 1 ERROR2026-09-07
OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK7 OK2026-07-22

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 1.0.0

READMEYes · 1,849 wordsVignettesNoNEWSYes · 67% structured · mentions 1.0.0Citation fileYes · cites a publication

Cites a JSS article

Help pages: 63 (16 internal)includes a package overview page
Examples that run: 43%20 run in a plain check · 27 never run
Exported functions with examples: 100%
Documented parameters: 91%
Return-value docs: 100%
Help pages citing references: 0%

How these are measured

Downloads

958

CRAN downloads in the past year

Rank #11,619 · ~3/day · ~80/mo

Releases R versions

Daily download trend is not available in this view yet.

36730 days

92590 days

9581 year

Compare downloads with other packages →

Also on19 r2u25 autocran

How downloads are counted

Repository

daniel1noble/bayestls ↗Account: daniel1noble

3Stars

0Forks

1Open issues

0Open PRs

0Releases

711Commits

4Commit identities

711 commits · Last activity 2026-10-19 · 0% stars, 30d · Default branch at 1.1.0, ahead of CRAN 1.0.0 (checked 2026-10-18)

Responsiveness

How these are sampled

PRs merged (all time): 100%
Median days to close, 50 most recently updated closed issues: 24
Median days to close, 50 most recently updated closed PRs: 0

Stars over time

2026-07-22 · 12026-08-06 · 3

How these are measured

PracticesBeta

8 development-tooling and community-health practices detected across 5 families in the upstream repository

Checks run against github.com/daniel1noble/bayestls on 2026-10-18.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux

Docs source (1)

GitHub Pages site last deployed 2026-10-16

Coverage (1)

Coverage reporting

Reproducibility and dev environment (2)

data-raw/Makefile

Show all practicesHide 1 practice

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

How this is detected·Detection ruleset v3 (2026-09-26)

AI coding toolsBeta

This repository shows signs of AI coding tools.

Claude Codefirst seen 2026-04-21
Seen in the repository: CLAUDE.md
Named in .Rbuildignore: .claude, CLAUDE.md
Named in .gitignore: .claude
Pull request it wrote: opened by a project member
Commits: at least 16 crediting it in the message
GitHub Copilotfirst seen 2026-07-18
Pull request: opened by its own account
Commits: 2 by its own account

Models named in its commits

Claude Opus 4.814 commits · Jun to Jul 2026
Claude Opus 4.71 commit · May 2026
Claude Sonnet 4.61 commit · May 2026

Counts are commits, not lines of code. Commits by a tool's accounts count once across all of its accounts. Commits crediting it are the largest count among its credit lines, a minimum.How this is detected·Detection ruleset v4 (updated 2026-09-27)

Dependencies

Declared dependencies

14 external dependencies (excludes base and recommended)

Installing bayesTLS adds 73 packages (35 with compiled code, 78 MB of source tarballs). Heaviest: BH 14 MB, stringi 11 MB, ggplot2 6.0 MB.

Depends (1)

R >= 4.1.0

Imports (10)

brms dplyr ggplot2MASSmethodspatchwork posteriorstatstibbleutils

LinkingTo (0)

none

Suggests (8)

cmdstanr glmmTMB here pkgload readxl testthat (>= 3.0.0)tidybayes tidyr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (4)

Daniel W. A. Noble

Author, Maintainer since 1.0.0 (2026)

Pieter A. Arnold

Author

Shinichi Nakagawa

Author

Patrice Pottier

Author

Releases (1)

1.0.0Latest
record
2026-07-21 · current release
R
R 4.6.0 released · 2026-04-24

Package metadata

First published: 2026-07-21
Total releases: 1 / 1 yrs
License: CC BY 4.0
Additional repositories: stan-dev.r-universe.dev
Minimum R: ≥ 4.1.0
Bundled data: 21 KB / 4 files
Download size: 248 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("bayesTLS")

The package provides a citation file that builds its entry when R runs it. Run citation() to see it.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for bayesTLS version 1.0.0 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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