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.


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 maintained at the R-Forge project site.


  • 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 = "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.
  • 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.


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

Credits and historical remarks

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 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

Help topics

Main functions

Essential functionality

  • mkinmod
    Function to set up a kinetic model with one or more state variables
  • mkinfit
    Fit a kinetic model to data with one or more state variables
  • mmkin
    Fit one or more kinetic models with one or more state variables to one or more datasets

Show results

Functions working on mkinfit objects

  • plot.mkinfit(plot_sep)
    Plot the observed data and the fitted model of an mkinfit object
  • summary.mkinfit(print.summary.mkinfit)
    Summary method for class "mkinfit"
  • mkinresplot
    Function to plot residuals stored in an mkin object
  • mkinparplot
    Function to plot the confidence intervals obtained using
  • endpoints
    Function to calculate endpoints for further use from kinetic models fitted with mkinfit
  • mkinerrmin
    Calculate the minimum error to assume in order to pass the variance test

Work with mmkin objects

Functions working with aggregated results

  • [.mmkin
    Subsetting method for mmkin objects
  • plot.mmkin
    Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object

Datasets and known results

Helper functions

  • mkin_wide_to_long
    Convert a dataframe with observations over time into long format
  • mkin_long_to_wide
    Convert a dataframe from long to wide format
  • mkinsub
    Function to set up a kinetic submodel for one state variable
  • mkinds
    A dataset class for mkin
  • print.mkinds
    Print mkinds objects
  • print.mkinmod
    Print mkinmod objects
  • mkinpredict
    Produce predictions from a kinetic model using specific parameters
  • transform_odeparms(backtransform_odeparms)
    Functions to transform and backtransform kinetic parameters for fitting
  • ilr(invilr)
    Function to perform isometric log-ratio transformation
  • geometric_mean
    Calculate the geometric mean

Analytical solutions

Parent only model solutions

Deprecated functions

Functions that have been superseeded

  • mkinplot
    Plot the observed data and the fitted model of an mkinfit object


  • add_err
    Add normally distributed errors to simulated kinetic degradation data



  • Depends: minpack.lm, rootSolve, inline, parallel
  • Imports: stats, graphics, methods, FME, deSolve, R6
  • Suggests: knitr, testthat, microbenchmark, ggplot2


  • Johannes Ranke [aut, cre, cph]
  • Katrin Lindenberger [ctb]
  • RenĂ© Lehmann [ctb]
  • Eurofins Regulatory AG [cph]