The R package **mkin** provides calculation routines for the analysis of
chemical degradation data, including **m**ulticompartment **kin**etics 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:

```
install.packages("mkin")
```

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.

For a start, have a look a the code examples provided for
`plot.mkinfit`

and
`plot.mmkin`

, and
at the package vignettes
`FOCUS L`

and
`FOCUS D`

.

The HTML documentation is available at jrwb.de, at github and at R-Forge.

- Highly flexible model specification using
`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. - As of version 0.9-39, fitting of several models to several datasets, optionally in
parallel, is supported, see for example
`plot.mmkin`

. - Model solution (forward modelling) in the function
`mkinpredict`

is 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`deSolve`

package (default is`lsoda`

). - If a C compiler is installed, the kinetic models are compiled from automatically
generated C code, see
vignette
`compiled_models`

. The autogeneration of C code was inspired by the`ccSolve`

package. Thanks to Karline Soetaert for her work on that. - By default, kinetic rate constants and kinetic formation fractions are
transformed internally using
`transform_odeparms`

so 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 RenĂ© Lehmann for the nice cooperation on this, especially the isometric logration transformation that is now used for the formation fractions. - A side effect of this is that when parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which can not occur in a single experiment with a single defined radiolabel position.
- The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.
- Summary and plotting functions. The
`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. - The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.
- Iteratively reweighted least squares fitting is implemented in a similar way
as in KinGUII and CAKE (see below). Simply add the argument
`reweight.method = "obs"`

to your call to`mkinfit`

and a separate variance componenent for each of the observed variables will be optimised in a second stage after the primary optimisation algorithm has converged. - Iterative reweighting is also possible using the two-component error model
for analytical data of
Rocke and Lorenzato
using the argument
`reweight.method = "tc"`

. - When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.

There is a graphical user interface that I consider useful for real work. Please refer to its documentation page for installation instructions and a manual.

There is a ChangeLog, for the latest CRAN release and one for the github master branch.

`mkin`

would not be possible without the underlying software stack consisting
of R and the packages deSolve
and FME, to say the least.

It 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.

The companion package kinfit (now deprecated) was started in 2008 and first published on CRAN on 01 May 2010.

The first `mkin`

code was
published on 11 May 2010 and the
first CRAN version
on 18 May 2010.

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, amongst 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 `FME`

package.

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.

Contributions are welcome! Your mkin fork is just a mouse click away... The master branch on github should always be in good shape, I implement new features in separate branches now. If you prefer subversion, project members for the r-forge project are welcome as well. Generally, the source code of the latest CRAN version should be available there. You can also browse the source code at cgit.jrwb.de/mkin.

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