Age | Commit message (Collapse) | Author | Files | Lines |
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with the custom lsoda call, avoiding repeated getNativeSymbolInfo calls.
It's just that we should not be calling foreign functions from different
packages, because the may change without notice. Using
getNativeSymbolInfo for "call_lsoda" avoids the CRAN note, and a similar
call could probably be used for "unlock_solver", avoiding the NOTE in
checks for cran, but we should not do this in a CRAN package.
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mkinfit: Keep model names stored in mkinmod objects, avoiding their loss in gmkin
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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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- D24_2014 dataset on aerobic soil degradation of 2,4-D from the EU
assessment as mkindsg object with metadata
- f_time_norm_focus() to do time-step normalisation using the FOCUS
method
- focus_soil_moisture data with default moisture contents at pF1,
pF 2 and pF 2.5 for USDA soil types from FOCUS GW guidance
- Dataset generation scripts in inst/dataset_generation
- Depend on R >= 2.15.1 in order to facilitate the use of
utils::globalVariables()
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Also, use logit transformation for g and for solitary formation
fractions, addressing #10.
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Do not give starting values for random effects in nlme.mmkin.
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- Reduce significant digits for the objective function output in
mkinfit(..., quiet = FALSE) as R and R-devel gave different output on my
system
- Add makefile target 'devtest' for testing with R-devel, in order
to fix problems showing up with R-devel on Travis
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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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This revealed a bug in the data returned in mkinfit$data in the case
of the d_3 algorithm, which also affected the residual plot - the
data from the direct fitting was not returned even if this was
the better method.
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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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This increases performance up to a factor of five!
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As we set the tolerance for ode() appropriately
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This increases the performance in the complete test suite
by about 20 secs from 120 to around 100 secs.
I tried improving merge speed by using data.table on another
branch, but this did not give a noticeable performance gain.
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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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The likelihood ratio test method is lrtest, in addition,
methods for update and residuals were added.
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The confint method can do profile likelihood based confidence intervals!
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in the hope that this makes plotting cross-platform also for this error
model
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mkinfit objects now include an ll() function to calculate the
log-likelihood. Part of the code was refactored, hopefully making it
easier to read and maintain. IRLS is currently the default algorithm for
the error model "obs", for no particular reason. This may be subject
to change when I get around to investigate.
Slow tests are now in a separate subdirectory and will probably
only be run by my own Makefile target.
Formatting of test logs is improved.
Roundtripping error model parameters works with a precision of 10% when
we use lots of replicates in the synthetic data (see slow tests). This
is not new in this commit, but as I think it is reasonable this
closes #7.
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Address winbuilder check problems, update check log, update of static docs
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generated with mkin < 0.9.49.5
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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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