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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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After the merge, run make test and accept the new snapshot for the mixed
model fit for an nlme object
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- All degradation models are specified as ODE models. This appears to be
fast enough
- Error models are being translated to nlmixr as close to the mkin error
model as possible. When using the 'saem' backend, it appears not to be
possible to use the same error model for more than one observed variable
- No support yet for models with parallel formation of metabolites, where
the ilr transformation is used in mkin per default
- There is a bug in nlmixr which appears to be triggered if the data are
not balanced, see nlmixrdevelopment/nlmixr#530
- There is a print and a plot method, the summary method is not finished
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In residual plots, use xlab and xlim if appropriate
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- fit_with_errors for saem()
- test_log_parms for mean_degparms() and saem()
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Also bump version to 1.0.3.
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Run make testcheck to regenerate logs with merge conflicts
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mkinfit: Keep model names stored in mkinmod objects, avoiding their loss in gmkin
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Also after the upgrade from buster to bullseye of my local system, some
test results for saemix have changed.
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The errors in the example code were in the \dontrun sections, so they
were not caught by CRAN checks. In addition, the static help files
generated with pkgdown were cached, so I noticed the errors only
after completely regenerating the documentation for version 1.0.0.
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- Improve authorship and copyright information
- Prepare pkgdown config
- Remove dependence on saemix as we need the development version which
is not ready for CRAN
- Temporarily remove saemix interface to check code coverage of the rest
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Vignette FOCUS_L failed as I had introduced a bug in the handling of
warnings.
Current vdiffr only runs visual tests if R < 4.1.0, skipping r-devel for now,
see https://github.com/r-lib/vdiffr/commit/630a29d013361fd63fea242f531e2db6aef37919
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Also, use more intelligent starting values for the variance of the
random effects for saemix. While this does not appear to speed up
the convergence, it shows where this variance is greatly reduced
by using mixed-effects models as opposed to the separate independent
fits.
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This is about twice as fast as deSolve compiled in the case of FOCUS D
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Still in preparation for analytical solutions of coupled models
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Preparing for symbolic solutions for more than one compound
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Static documentation rebuilt by pkgdown
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- Reconcile docs and code for max_twa_parent
- Correct links to docs in twa vignette
- Static documentation rebuilt by pkgdown
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The new algorithm tries direct optimization of the likelihood, as well
as a three step procedure. In this way, we consistently get the
model with the highest likelihood for SFO, DFOP and HS for all 12
new test datasets.
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Now we have a three stage fitting process for
nonconstant error models:
- Unweighted least squares
- Only optimize the error model
- Optimize both
Static documentation rebuilt by pkgdown
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- Skip long running tests on CRAN as well to avoid timeout on winbuilder
- Don't install benchmark results in the package, they are only needed
in the git repository
- Don't run example in man/add_err.Rd as it takes > 10 s on winbuilder
- Rebuild docs
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- Also make it possible to specify initial values for error model
parameters.
- Run tests
- Rebuild docs
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- Write the NEWS
- Static documentation rebuilt by pkgdown
- Adapt mkinerrmin
- Fix (hopefully all) remaining problems in mkinfit
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- No IRLS required
- Removed optimization algorithms other than Port
- Removed the dependency on FME
- Fitting the error model 'obs' is much faster for the FOCUS_2006_D
dataset and the FOMC_SFO model (1 second versus 3.4 seconds)
- Vignettes build slower. Compiled models needs 3 minutes instead of 1.5
- For other vignettes, the trend is less clear. Some fits are faster,
even for error_model = "const". FOCUS_Z is faster (34.9 s versus
44.1 s)
- Standard errors and confidence intervals are slightly smaller
- Removed code for plotting during the fit, as I hardly ever used it
- Merged the two cost functions (using transformed and untransformed
parameters) into one log-likelihood function
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Also:
- Change rounding in print.nafta
- Add dots argument to nafta()
- Use cores=1 in examples
- Restrict N in IORE model to values > 0
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Static documentation rebuilt by pkgdown
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to avoid NOTES about long execution times.
Static documentation rebuilt by pkgdown with run_dont_run = TRUE
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Also improve the irls fitting of the error model and add a test
for FOCUS_2006_C where the second component of the error model is zero
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Rename 'sigma_rl' to 'sigma_twocomp' as the Rocke and Lorenzato model assumes lognormal distribution for large y.
Rebuild static documentation.
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