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.
PoolPrev() to fail with pools bigger than about 300,
with tests to capture this behavior in the future (on pools of 1000).PoolPrev() and
HierPoolPrev().CheckInputData() allows users to test the formatting of their
input data prior to estimating prevalence, checking things including class of
input variables, whether input columns exist, whether columns/rows have
incorrect or missing values, among other checks.PrepareClusterData() allows users to test the hierarchy/clustered
sampling scheme prior to estimating prevalence, to ensure data is formatted
correctly according to the assumptions underlying PoolTestR. This function tests
whether any values in the hierarchy/sampling columns are missing, tests the
nesting of clustered variables, and tests whether each location has a unique
identifier. # PoolTestR v0.2.0 (Release date: 2024-12-05) 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:getPrevalence() to return the posterior median as the point estimate (instead of the posterior mean) for Bayesian models with with PoolRegBayes()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 detailsPoolRegBayes(), HierPoolPrev(), and PoolPrev()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 )