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Depends on inline >= 0.16.2 (including the bug fixes from
eddelbuettel/inline#18), which provides 'moveDLL' to store the DLL for a
compiled function in a safe place in case the argument 'dll_dir' is
specified in the call to 'mkinmod'.
Huge thanks to Dirk @eddelbuettel for his review and support
for the work on the inline package.
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With automatic reloading in mkinfit and mkinpredict in case the
DLL is not loaded and the original DLL path has been cleaned up.
Depends on jranke/inline@974bdea04fcedfafaab231e6f359c88270b56cb9
See inline#13
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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 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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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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Preparing for symbolic solutions for more than one compound
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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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Use lazy = TRUE in the pd target for generating pkgdown documentation
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to check if the link to the pfm package is correctly generated by
pkgdown after preparing the pfm package docs accordingly
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Static documentation rebuilt by pkgdown
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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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Address winbuilder check problems, update check log, update of static docs
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Static documentation rebuilt by pkgdown
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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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by plotting squared residuals against predicted values, and
showing the variance function used in the fitted error model.
Rebuild docs
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Static documentation rebuilt by pkgdown
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- Fix the respective error in the code
- Static documentation rebuilt by pkgdown
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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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from vignettes/mkin.Rmd
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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- 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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which was accidentally overwritten by pkgdown -> roxygen
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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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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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