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This even a bit faster.
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The vignette illustrates pH dependent degradation (covariate modelling)
with some detail for the parent compound.
Also, a bug in the saem method of the illparms function was fixed, which
prevented to find ill-defined parameters in cases where e.g. slopes of
covariate models have a negative estimate.
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Update platform dependent test plots
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Thanks to Sebastian Meyer for spotting it.
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llquant argument, improved legend text, tests
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Save winbuilder/CRAN check time...
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To ensure that tests on different platforms work on the same data, the
mixed modelling test data previosly generated in
tests/testthat/setup_script.R were generated once using the script in
inst/dataset/generation/ds_mixed.R, and are now distributed with the
package.
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Due to an oversight, error.init was not really passed to saemix_model in
saem.mmkin. The new initial values were reverted to c(1, 1), in order to
avoid changing the test results. Initial values for error model
parameters are now shown in parplot.multistart.
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For the case of mkin transformations. This gives faster convergence,
and appears to avoid problems with numeric ODE solutions
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That parhist name was not the brightest idea, as it does
not show histograms.
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Also skip long-running test with saem using the analytical from mkin
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Also, add a method for gathering convergence information
and a method for gathering information on ill-defined parameters
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Thanks to Tomas Kalibera for his analysis of the problem on the
r-package-devel mailing list and for the suggestion on how to
fix it. See the current benchmark vignette for the new data
on mkin 1.1.1 with R 4.2.1, with unprecedented performance.
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Documentation and test results were updated, the latter with regard
to the previous work on plot.mixed.mmkin.
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I am postponing my attempts to get the nlmixr interface to CRAN, given
some problems with nlmixr using R-devel under Windows, see
https://github.com/nlmixrdevelopment/nlmixr/issues/596
and
https://github.com/r-hub/rhub/issues/512,
which is fixed by the removal of nlmixr from the testsuite.
For the tests to be more platform independent, the biphasic mixed
effects models test dataset was defined in a way that fitting
should be more robust (less ill-defined).
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After the merge, run make test and accept the new snapshot for the mixed
model fit for an nlme object
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