Programming syntax, a template model module and tools to help
    maintain a modular and open-source health economic model's project
    documentation website.  These elements are the foundation for a
    prototype software framework to support replicable and transferable
    health economic models. The software framework is extended by other R
    libraries.  For detailed documentation about the framework and how to
    use it visit <https://www.ready4-dev.com/>. For a background to the
    methodological issues that the framework is attempting to help solve,
    see Hamilton et al. (2024) <doi:10.48550/arXiv.2310.14138>.

A framework for unit testing for realistic minimalists,
    where we distinguish between expected, acceptable, current, fallback,
    ideal, or regressive behaviour. It can also be used for monitoring
    third-party software projects for changes.

Flux (mass per unit time) and Load (mass) are computed from timeseries estimates of analyte concentration and discharge. Concentration timeseries are computed from regression between surrogate and user-provided analyte. Uncertainty in calculations is estimated using bootstrap resampling. Code for the processing of acoustic backscatter from horizontally profiling acoustic Doppler current profilers is provided. All methods detailed in Livsey et al (2020) <doi:10.1007/s12237-020-00734-z>, Livsey et al (2023) <doi:10.1029/2022WR033982>, and references therein.

Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.