mkin/docs/articles/web_only/FOCUS_Z_files, branch C_analyticalFitting kinetic models to chemical degradation data (also on github)https://cgit.jrwb.de/mkin/atom?h=C_analytical2020-10-15T10:53:23ZUpdate online docs for release version2020-10-15T10:53:23ZJohannes Rankejranke@uni-bremen.de2020-10-15T10:53:23Zurn:sha1:5b1179f0daff9077e1370065f9b9f03a2d0e0450Add docs generated using released version 0.9.522020-05-27T05:12:51ZJohannes Rankejranke@uni-bremen.de2020-05-27T05:12:51Zurn:sha1:b5ee48a86e4b1d4c05aaadb80b44954e2e994ebcUpdate vignettes and docs2020-05-12T14:34:00ZJohannes Rankejranke@uni-bremen.de2020-05-12T14:34:00Zurn:sha1:6211f3ef4995657798686d8d4ab43ed9406e8a08Fix bug in yesterdays release, add methods for BIC2019-11-01T11:19:03ZJohannes Rankejranke@uni-bremen.de2019-11-01T11:19:03Zurn:sha1:bbb74d4f60033899cd5cdd36aa70f157bc52209eAdaptations for gmkin2019-07-09T07:10:02ZJohannes Rankejranke@uni-bremen.de2019-07-08T16:12:21Zurn:sha1:e0bef15657df1d6cade99cc3f6d8b07fa35792fe
Address winbuilder check problems, update check log, update of static docs
Static documentation rebuilt by pkgdown2019-07-05T13:57:24ZJohannes Rankejranke@uni-bremen.de2019-07-05T13:57:24Zurn:sha1:4596667b19f032232ceb8f3f762aaad5d69c15beAdditional 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.
Fix hessian calculations2019-05-07T06:43:12ZJohannes Rankejranke@uni-bremen.de2019-05-07T06:43:12Zurn:sha1:c322a8102a399cbb1fe38c4c4ca4485cea8bc4e8
Static documentation rebuilt by pkgdown
After the OLS step, use OLS parameter estimates2019-05-07T06:12:27ZJohannes Rankejranke@uni-bremen.de2019-05-07T06:12:27Zurn:sha1:8a3475c59f3d91ce5ce7d980d6de09360617e7fe
- Fix the respective error in the code
- Static documentation rebuilt by pkgdown
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