mkin/docs/reference/schaefer07_complex_case.html, branch stackoverflow_71340826 Fitting kinetic models to chemical degradation data (also on github) https://cgit.jrwb.de/mkin/atom?h=stackoverflow_71340826 2022-03-02T17:03:54Z Make tests more platform independent, update docs 2022-03-02T17:03:54Z Johannes Ranke jranke@uni-bremen.de 2022-03-02T17:03:54Z urn:sha1:2bb59c88d49b193f278916ad9cc4de83c0de9604 Improve README, introductory vignette and some other docs 2021-02-15T14:20:53Z Johannes Ranke jranke@uni-bremen.de 2021-02-15T13:08:13Z urn:sha1:a9427a09abdf7ce9aaeae7c7190f90c8f2e5ef52 Also bump version to 1.0.3. Prepare for v1.0.0 2021-02-03T17:18:19Z Johannes Ranke jranke@uni-bremen.de 2021-02-03T15:41:31Z urn: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 Add docs generated using released version 0.9.52 2020-05-27T05:12:51Z Johannes Ranke jranke@uni-bremen.de 2020-05-27T05:12:51Z urn:sha1:b5ee48a86e4b1d4c05aaadb80b44954e2e994ebc Update vignettes and docs 2020-05-12T14:34:00Z Johannes Ranke jranke@uni-bremen.de 2020-05-12T14:34:00Z urn:sha1:6211f3ef4995657798686d8d4ab43ed9406e8a08 Fix bug in yesterdays release, add methods for BIC 2019-11-01T11:19:03Z Johannes Ranke jranke@uni-bremen.de 2019-11-01T11:19:03Z urn:sha1:bbb74d4f60033899cd5cdd36aa70f157bc52209e Correct and rebuild docs 2019-09-18T11:03:50Z Johannes Ranke jranke@uni-bremen.de 2019-09-18T11:03:50Z urn: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 pkgdown 2019-07-05T13:57:24Z Johannes Ranke jranke@uni-bremen.de 2019-07-05T13:57:24Z urn:sha1:4596667b19f032232ceb8f3f762aaad5d69c15be Additional algorithm "d_c", more tests, docs 2019-06-04T13:09:28Z Johannes Ranke jranke@uni-bremen.de 2019-06-04T13:09:28Z urn: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 fitting 2019-05-03T17:14:15Z Johannes Ranke jranke@uni-bremen.de 2019-05-03T17:14:15Z urn: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