mkin/NAMESPACE, branch v0.9.49.8 Fitting kinetic models to chemical degradation data (also on github) https://cgit.jrwb.de/mkin/atom?h=v0.9.49.8 2019-11-13T20:15:35Z Calculate Akaike weights 2019-11-13T20:15:35Z Johannes Ranke jranke@uni-bremen.de 2019-11-13T20:15:35Z urn:sha1:c3700bec3a704660d3ade7a54c56b7084beb02b4 Likelihood ratio test for mmkin columns with two fits 2019-11-13T10:05:39Z Johannes Ranke jranke@uni-bremen.de 2019-11-13T10:05:39Z urn:sha1:f6385b071cee2c261da28bf98fd1599da1a30ddb Add a lack-of-fit test 2019-11-09T00:05:51Z Johannes Ranke jranke@uni-bremen.de 2019-11-09T00:05:51Z urn:sha1:20b9c584e7c43ecbb708459e531c24a1a4751e17 - Switch an example dataset in the test setup to a dataset with replicates, adapt tests - Skip the test for lrtest with an update specification as it does not only fail when pkgdown generates static help pages, but also in testthat Scaled residual plots 2019-11-04T22:48:20Z Johannes Ranke jranke@uni-bremen.de 2019-11-04T22:48:20Z urn:sha1:3410513f55b3f8b5c4331f4fb4487613d3a28208 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 Add likelihood ratio test and other methods, fixes 2019-10-31T00:59:05Z Johannes Ranke jranke@uni-bremen.de 2019-10-31T00:55:01Z urn:sha1:7091d3738e7e55acb20edb88772b228f6f5b6c98 The likelihood ratio test method is lrtest, in addition, methods for update and residuals were added. Fix checks and tests 2019-10-26T20:52:53Z Johannes Ranke jranke@uni-bremen.de 2019-10-26T20:52:53Z urn:sha1:6e5630a0df7e857697ff2ce4730a5f7f45b67377 Static documentation rebuilt by pkgdown parms and confint methods 2019-10-26T18:50:09Z Johannes Ranke jranke@uni-bremen.de 2019-10-26T18:50:09Z urn:sha1:7b7729694363515007193d1c3e29e9b76271abb3 The confint method can do profile likelihood based confidence intervals! Use roxygen for functions and methods 2019-10-25T00:03:54Z Johannes Ranke jranke@uni-bremen.de 2019-10-24T22:37:42Z urn:sha1:0a3eb0893cb4bd1b12f07a79069d1c7f5e991495 Direct error model fitting works 2019-04-04T15:21:13Z Johannes Ranke jranke@uni-bremen.de 2019-04-04T13:42:23Z urn:sha1:7a1d3d031aa23fce723ac4f4c8e4bb5d64959447 - No IRLS required - Removed optimization algorithms other than Port - Removed the dependency on FME - Fitting the error model 'obs' is much faster for the FOCUS_2006_D dataset and the FOMC_SFO model (1 second versus 3.4 seconds) - Vignettes build slower. Compiled models needs 3 minutes instead of 1.5 - For other vignettes, the trend is less clear. Some fits are faster, even for error_model = "const". FOCUS_Z is faster (34.9 s versus 44.1 s) - Standard errors and confidence intervals are slightly smaller - Removed code for plotting during the fit, as I hardly ever used it - Merged the two cost functions (using transformed and untransformed parameters) into one log-likelihood function