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2019-06-04Additional algorithm "d_c", more tests, docsJohannes Ranke1-75/+40
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.
2019-06-04Algorithms direct, two-, three-, fourstep, IRLSJohannes Ranke1-5/+45
All of them are working now and allow for comparison Based on SFO, DFOP and HS fits to twelve test datasets, only the combination of direct and threestep is needed to find the lowest AIC
2019-06-03Status von Samstag morgen - untestedJohannes Ranke1-0/+42
2019-05-02Prepare for CRAN releaseJohannes Ranke1-0/+1
- Skip long running tests on CRAN as well to avoid timeout on winbuilder - Don't install benchmark results in the package, they are only needed in the git repository - Don't run example in man/add_err.Rd as it takes > 10 s on winbuilder - Rebuild docs
2019-05-02Improve testsJohannes Ranke1-6/+13
- Improve control of the number of cores - Reduce the precision of the correlation matrix in the test summary output, as the exact results are platform dependent
2019-04-10Adapt tests, vignettes and examplesJohannes Ranke1-12/+5
- Write the NEWS - Static documentation rebuilt by pkgdown - Adapt mkinerrmin - Fix (hopefully all) remaining problems in mkinfit
2019-04-04Direct error model fitting worksJohannes Ranke1-0/+178
- 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

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