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 degradation compartments are involved. It provides stable functionality for kinetic evaluations according to the FOCUS guidance (see below for details). In addition, it provides functionality to do hierarchical kinetics based on nonlinear mixed-effects models.
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 various helpful tools have been developed as detailed in 'Credits and historical remarks' below. This package aims to provide a one stop solution for degradation kinetics, addressing modellers that are willing to, or even prefer to work with R.
Documentation of the development version is found in the 'dev' subdirectory. In the articles section of this documentation, you can also find demonstrations of the application of nonlinear hierarchical models, also known as nonlinear mixed-effects models, to more complex data, including transformation products and covariates.
including equilibrium reactions and using the single first-order reversible
binding (SFORB) model, which will automatically create two state variables
for the observed variable.
is performed either using the analytical solution for the case of
parent only degradation or some simple models involving a single transformation
product, , 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
deSolve package (default is
summary of an
mkinfit object 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".
function. A two-component error model similar to the one proposed by
Rocke and Lorenzato
can be selected using the argument
error_model = "tc".
so their estimators can more reasonably be expected to follow
a normal distribution.
saemix package as a backend. Analytical
solutions suitable for use with this package have been implemented for parent
only models and the most important models including one metabolite (SFO-SFO
and DFOP-SFO). Fitting other models with
saem.mmkin, while it makes use
of the compiled ODE models that mkin provides, has longer run times (from a couple
of minutes to more than an hour).
The autogeneration of C code was
inspired by the
ccSolve package. Thanks
to Karline Soetaert for her work on that.
There is a graphical user interface that may be useful. Please refer to its documentation page for installation instructions and a manual. It only supports evaluations using (generalised) nonlinear regression, but not simultaneous fits using nonlinear mixed-effects models.
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
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
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
Finally, there is KineticEval, which contains some further development of the scripts used for KinGUII.
Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.
Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.
Funding was received from the Umweltbundesamt in the course of the projects
Thanks to everyone involved for collaboration and support!
Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for her interest and support for using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.
Contributions are welcome!
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