PoolTestR v0.2.0 (Release date: 2024-11-XX)

Caitlin Cherryh has joined the development team and has been working on improving readability of outputs, documentation, and testing.

This update includes an option for PoolPrev() to skip the calculation of Bayesian estimates. When using bayesian = FALSE, only MLE and likelihood ratio confidence intervals will be calculated, substantially speeding up this function (perhaps x100).

This updates also removes one source of bias from prevalence estimates returned for any hierarchical models. This effects the results of HierPoolPrev() and getPrevalence() applied to models with random effects. Under the update, prevalence estimates will typically slightly increase, though the difference will not be notable if the sample size is large and there is little clustering.

Previous estimates of prevalence did not marginalise out the random effects when calculating population-level prevalence, but as of this version, random effects are marginalised out. Due to the complexity introduced by this bias-correction we now longer-support specifying nested surveys using ~(1|Layer1/Layer2) and recommend using the format ~(1|Layer1) + (1|Layer2) which should be equivalent as long as each level in Layer2 is unique — i.e. the format already required for HierPoolPrev().

Due to the complexity introduced by this bias-correction, the way of specifying priors for HierPoolPrev() has been updated. Priors for HierPoolPrev() are now directly on the real-scale (logit-transformed) parameters, rather than prevalence directly. We have also updated the default priors for PoolRegBayes() for regression parameters, as we believe the previous priors were too diffuse (normal(0,100)). The defaults for the centered predictors are now student(6,0,1.5).

HierPoolPrev() now has functionality to return estimate of intracluster correlation coefficients (ICC) at one or more levels of clustering.

HierPoolPrev() and PoolPrev() now have custom output classes (inheriting from tibble, the previous class for these outputs). This has allowed us (Caitlin) to add pretty-print functions these outputs which are much more human readable. Saving the output with write.csv() or similar will still return a detailed, machine-readable output.

Both PoolPrev() and HierPoolPrev() have been updated to improve point estimates. New default function values have been added for both PoolPrev() and HierPoolPrev(). The default for the new robust parameter is robust = TRUE, which means the point estimate of prevalence is the posterior median. In both functions, the default value for all.negative.pools = 'zero', meaning when all pools are negative, the point estimate and the. lower bound for the interval will be 0.

PoolTestR v0.1.3 (Release date: 2022-07-XX)

This is patch to fix a bug affecting PoolPrev(). The bug affected the maximum likelihood estimates (MLE) and likelihood ratio confidence intervals (LR-CIs) of prevalence when the default Jeffrey’s prior was being used. The bug would usually make the MLE and LR-CIs much closer to the Bayesian estimates than they should have been. As both sets of estimates are valid, the results will still have been approximately correct.

This patch also includes an option, replicate.poolscreen (default to FALSE), for PoolPrev(). This options changes the way the likelihood ratio confidence intervals are calculated. With replicate.poolscreen = TRUE, PoolPrev will more closely reproduce the results produced by Poolscreen. We believe that our implementation of these intervals is more correct so would recommend that users continue to use the default (replicate.poolscreen = FALSE), but this option may be helpful for those who are trying to compare results across the two programs.

PoolTestR v0.1.2 (Release date: 2021-07-XX)

We have published a paper about PoolTestR in Environmental Modelling and Software now available at https://doi.org/10.1016/j.envsoft.2021.105158. If you find this package useful, please let us know and/or cite our paper!

A couple bug fixes:

• Corrections to the Jeffrey’s prior in PoolPrev
• Improved numerical stability of hierarchical models – previous implementation was causing initialisation of MCMC to fail in some edge cases

A few improvements:

• Allow user to specify level of confidence intervals and credible intervals (default level is 0.95, for 95% intervals)
• Option for getPrevalence() to return the posterior median as the point estimate (instead of the posterior mean) for Bayesian models with with PoolRegBayes()
• Implement PoolRegBayes() with a logit link function as custom family in brms. This allows for better post-processing for the results of PoolRegBayes() – e.g. simulating from the model, leave-one-out cross-validation, posterior predictive checks. See brms for details
• Allow users to pass more control variables to MCMC sampling routines across PoolRegBayes(), HierPoolPrev(), and PoolPrev()
• Allows users to specify the scale parameter for the half-Cauchy hyper-prior or the standard deviations of the random effect terms in HierPoolPrev(). Also reduced the default value of this hyperprior from 25 (very diffuse) to 2 (weakly informative). This is now very comparable to the equivalent default hyper-prior for brms models including those fit using PoolRegBayes() (i.e. a half t distribution three degrees of freedom )

PoolTestR v0.1.1 (Release date: 2021-02-13)

Minor patch so that the package works across more platforms (namely solaris)

PoolTestR v0.1.0 (Release date: 2021-02-08)

This is our first official release! Please see the github site (https://github.com/AngusMcLure/PoolTestR#pooltestr) for a basic crash course on using the package. An upcoming (open access) journal article will go into further detail. A preprint can be accessed at https://arxiv.org/abs/2012.05405. I’ll post a link to the article when published.