curveR: Calibration Curve Ecosystem for Quantitative Immunoassays

Meta-package for the curveR immunoassay calibration curve ecosystem. Attaches curveRcore and curveRfreq and provides the umbrella vignette that compares frequentist and Bayesian approaches.

Package ecosystem

Package	Role	Stan required?
curveRcore	Shared models, inverses, derivatives, preprocessing, output class	No
curveRfreq	Frequentist multi-start Levenberg-Marquardt NLS calibration	No
curveRbayes	Bayesian hierarchical calibration via Stan/HMC	Yes
curveRweights	Continuous precision weights from calibration-curve uncertainty	Yes

Installation

# Full ecosystem (Stan required for Bayesian + weights components)
remotes::install_github("immunoplex/curveR")

# Frequentist only — no Stan
remotes::install_github("immunoplex/curveRcore")
remotes::install_github("immunoplex/curveRfreq")

# Add Bayesian fitting
remotes::install_github("immunoplex/curveRbayes")

# Add precision weighting
remotes::install_github("immunoplex/curveRweights")

# Stan backend (required for curveRbayes and curveRweights)
install.packages("cmdstanr",
                 repos = c("https://stan-dev.r-universe.dev",
                           getOption("repos")))
cmdstanr::install_cmdstan()

Hub vignette

The umbrella methods-comparison vignette lives exclusively in this package because it requires all four packages simultaneously: vignette("curveR-methods-comparison", package = "curveR")

See also

Useful links:

• https://immunoplex.github.io/curveR/

• https://github.com/immunoplex/curveR

• Report bugs at https://github.com/immunoplex/curveR/issues

Author

Maintainer: Michael Scot Zens michael.s.zens@dartmouth.edu

Authors:

• Hardik Gupta hardik.gupta@dartmouth.edu

• Seamus O. Stein seamusstein@gmail.com

• Anne G. Hoen anne.g.hoen@dartmouth.edu