mkin/docs/reference/plot.mmkin-3.png, branch v1.0.3Fitting kinetic models to chemical degradation data (also on github)https://erac.jrwb.de/mkin/atom?h=v1.0.32021-02-15T14:20:53ZImprove README, introductory vignette and some other docs2021-02-15T14:20:53ZJohannes Rankejranke@uni-bremen.de2021-02-15T13:08:13Zurn:sha1:a9427a09abdf7ce9aaeae7c7190f90c8f2e5ef52
Also bump version to 1.0.3.
Prepare for v1.0.02021-02-03T17:18:19ZJohannes Rankejranke@uni-bremen.de2021-02-03T15:41:31Zurn:sha1:f0ef23a7598e5d19648ae4edc2b74e0fba27a41c
- 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
Update online docs for release version2020-10-15T10:53:23ZJohannes Rankejranke@uni-bremen.de2020-10-15T10:53:23Zurn:sha1:5b1179f0daff9077e1370065f9b9f03a2d0e0450Include random effects in starting parameters2020-04-15T17:00:06ZJohannes Rankejranke@uni-bremen.de2020-04-15T16:13:04Zurn:sha1:42171ba55222383a0d47e5aacd46a972819e7812
- mean_degparms() now optionally returns starting values for fixed and
random effects, which makes it possible to obtain acceptable fits
also in more difficult cases (with more parameters)
- Fix the anova method, as it is currently not enough to inherit from
lme: https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761
- Show fit information, and per default also errmin information
in plot.nlme.mmkin()
- Examples for nlme.mmkin: Decrease tolerance and increase the number of
iterations in the PNLS step in order to be able to fit FOMC-SFO and
DFOP-SFO
Additional algorithm "d_c", more tests, docs2019-06-04T13:09:28ZJohannes Rankejranke@uni-bremen.de2019-06-04T13:09:28Zurn:sha1:95178837d3f91e84837628446b5fd468179af2b9
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.
Improve error model fitting2019-05-03T17:14:15ZJohannes Rankejranke@uni-bremen.de2019-05-03T17:14:15Zurn:sha1:1ef7008be2a72a0847064ad9c2ddcfa16b055482
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
Better initials for error model parameters2019-05-02T11:17:05ZJohannes Rankejranke@uni-bremen.de2019-05-02T11:17:05Zurn:sha1:70591022c07f0e8fb4dd67789b7c8d78af8ebc18
- Also make it possible to specify initial values for error model
parameters.
- Run tests
- Rebuild docs
Adapt tests, vignettes and examples2019-04-10T08:17:35ZJohannes Rankejranke@uni-bremen.de2019-04-10T08:17:35Zurn:sha1:194659fcaccdd1ee37851725b8c72e99daa3a8cf
- Write the NEWS
- Static documentation rebuilt by pkgdown
- Adapt mkinerrmin
- Fix (hopefully all) remaining problems in mkinfit
Static documentation rebuilt by pkgdown 1.1.02018-06-05T23:24:15ZJohannes Rankejranke@uni-bremen.de2018-06-05T23:24:15Zurn:sha1:39b2c2c762b03922a2648b1b226d0f308600cc87Another rebuild of the static docs2018-03-09T22:14:39ZJohannes Rankejranke@uni-bremen.de2018-03-09T22:14:39Zurn:sha1:0b677308e3ffbf742880f05cc54da469f3ecc854
Now with rbenchmark installed, to get results for the compiled_models
vignette