Age | Commit message (Collapse) | Author | Files | Lines |
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This also adds the first benchmark results obtained on my laptop system
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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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Convergence is faster with this version (@ecomets mentioned that there
was a bugfix lately that could lead to faster convergence). However,
if I use too many iterations (i.e. 10 000 as in the last version of
this vignette), I get an error in solving omega.teta during later
iterations, apparently due to overparameterisation of the DFOP model
in this case.
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Adapt to the corrected data and unify control parameters for saemix and
nlmixr with saem. Update docs
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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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Evaluations with nlme, saemix and nlmixr are included
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In residual plots, use xlab and xlim if appropriate
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Also bump version to 1.0.3.
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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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By depending on parallel instead of importing it, functions to set up
and stop a cluster are always available when mkin is loaded.
The use of multicore processing in mmkin on Windows is now documented in
the help file, which brings mkin closer to a version 1.0 #9.
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instead of the orange danger color.
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Also, use .rmd extension instead of .Rmd for vignettes.
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also for deSolve and eigenvalue based solutions. This noticeably increases
performance for these methods, see test.log and benchmark vignette.
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This revealed that transforming rates is necessary for fitting
the analytical solution of the SFO-SFO model to the FOCUS D dataset.
Benchmarks show that fitting coupled models with deSolve got a bit
slower through the latest changes
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- Added a section with platform specific notes on getting compiled
models to work to the compiled models article
- Don't return empty SFORB parameter list from endpoints() if there is no
SFORB model
- Avoid warnings when using standardized = TRUE in plot.mmkin()
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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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- 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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- Rebuild static documentation
- Adapt test to new approach to two component error model
where the model is inadequate
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