The R package mkin provides calculation routines for the analysis of chemical degradation data, including multicompartment kinetics as needed for modelling the formation and decline of transformation products, or if several compartments are involved.
You can install the latest released version from CRAN from within R:
In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and helpful tools have been developed as detailed in ‘Credits and historical remarks’ below.
mkinmod, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.
mkinpredictis performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the
deSolvepackage (default is
compiled_models. The autogeneration of C code was inspired by the
ccSolvepackage. Thanks to Karline Soetaert for her work on that.
transform_odeparmsso their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric log-ratio transformation that is now used for the formation fractions.
mkinfitobject is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.
error_model = "obs".
error_model = "tc".
There is a graphical user interface that may be useful. Please refer to its documentation page for installation instructions and a manual.
mkin would not be possible without the underlying software stack consisting of, among others, R and the package deSolve. In previous version,
mkin was also using the functionality of the FME package. Please refer to the package page on CRAN for the full list of imported and suggested R packages. Also, Debian Linux, the vim editor and the Nvim-R plugin have been invaluable in its development.
mkin could not have been written without me being introduced to regulatory fate modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories Ltd (formerly RCC Ltd).
mkin greatly profits from and largely follows the work done by the FOCUS Degradation Kinetics Workgroup, as detailed in their guidance document from 2006, slightly updated in 2011 and in 2014.
Also, it was inspired by the first version of KinGUI developed by BayerCropScience, which is based on the MatLab runtime environment.
In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on
mkin, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the
Somewhat in parallel, Syngenta has sponsored the development of an
mkin and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the CAKE website, where you can also find a zip archive of the R scripts derived from
mkin, published under the GPL license.
Finally, there is KineticEval, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.
|Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data Environments 6 (12) 124 doi:10.3390/environments6120124|
|Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data Environmental Sciences Europe 30 17 doi:10.1186/s12302-018-0145-1|