mkin/docs/reference/IORE.solution.html, 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:sha1:5b1179f0daff9077e1370065f9b9f03a2d0e0450Update vignettes and docs2020-05-12T14:34:00ZJohannes Rankejranke@uni-bremen.de2020-05-12T14:34:00Zurn:sha1:6211f3ef4995657798686d8d4ab43ed9406e8a08Documentation improvements, rebuild static docs2020-05-12T06:36:31ZJohannes Rankejranke@uni-bremen.de2020-05-12T06:07:07Zurn:sha1:9ac853c7ceece333099021974025d07e75be2b33Merge parent solutions to one file2020-05-07T07:13:20ZJohannes Rankejranke@uni-bremen.de2020-05-07T07:10:33Zurn:sha1:67c8163487e776e9a378c9dfcd39c74f6e6bc507
- Combine R/*.solution.R into R/parent_solutions.R
- Create a roxygen family of related functions
- Reduce redundancy by using the inherit tag
- Change the parent.0 argument to parent_0 to match
the corresponding parameter name used in mkinfit
Static documentation rebuilt by pkgdown2020-05-07T06:59:29ZJohannes Rankejranke@uni-bremen.de2020-05-07T06:59:29Zurn:sha1:8bdb4cd437a9d4663e542f95869e8692aa38dadbStatic documentation rebuilt by pkgdown2019-10-25T00:10:08ZJohannes Rankejranke@uni-bremen.de2019-10-25T00:10:08Zurn:sha1:af2e1540cdad2fd00bb6216a38a754ff748629adCorrect and rebuild docs2019-09-18T11:03:50ZJohannes Rankejranke@uni-bremen.de2019-09-18T11:03:50Zurn:sha1:e05656d57668688b971c28e32b4cfd4d3eac4662
- Reconcile docs and code for max_twa_parent
- Correct links to docs in twa vignette
- Static documentation rebuilt by pkgdown
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.
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