Kinetic Evaluation of Chemical Degradation Data
Function to plot the confidence intervals obtained using mkinfit
Plot the observed data and the fitted model of an mkinfit object
Produce predictions from a kinetic model using specific parameters
Add normally distributed errors to simulated kinetic degradation data
Calculate the AIC for a column of an mmkin object
Anova method for saem.mmkin objects
Calculate Akaike weights for model averaging
logLik method for saem.mmkin objects
Export a list of datasets format to a CAKE study file
Confidence intervals for parameters of mkinfit objects
Create degradation functions for known analytical solutions
Double First-Order in Parallel kinetics
Function to calculate endpoints for further use from kinetic models fi...
Experimental datasets used for development and testing of error models
Subsetting method for mmkin objects
Normalisation factors for aerobic soil degradation according to FOCUS ...
First-Order Multi-Compartment kinetics
Retrieve a degradation function from the mmkin namespace
Likelihood ratio test for mkinfit models
Hierarchical kinetics template
Hockey-Stick kinetics
Method to get the names of ill-defined parameters
Function to perform isometric log-ratio transformation
Confidence intervals for parameters in saem.mmkin objects
Indeterminate order rate equation kinetics
Plot the distribution of log likelihoods from multistart objects
Lack-of-fit test for models fitted to data with replicates
Logistic kinetics
Calculated the log-likelihood of a fitted mkinfit object
Function to calculate maximum time weighted average concentrations fro...
Calculate mean degradation parameters for an mmkin row object
Fit nonlinear mixed-effects models built from one or more kinetic degr...
Create a mixed effects model from an mmkin row object
Convert a dataframe from long to wide format
Convert a dataframe with observations over time into long format
A dataset class for mkin
A class for dataset groups for mkin
Calculate the minimum error to assume in order to pass the variance te...
Function to plot squared residuals and the error model for an mkin obj...
Fit a kinetic model to data with one or more state variables
Function to set up a kinetic model with one or more state variables
Function to plot residuals stored in an mkin object
Fit one or more kinetic models with one or more state variables to one...
Perform a hierarchical model fit with multiple starting values
Evaluate parent kinetics using the NAFTA guidance
Create an nlme model for an mmkin row object
Helper functions to create nlme models from mmkin row objects
Number of observations on which an mkinfit object was fitted
Update an mkinfit model with different arguments
Extract model parameters
Plot parameter variability of multistart objects
Plot predictions from a fitted nonlinear mixed model obtained via an m...
Plot the observed data and the fitted model of an mkinfit object
Plot model fits (observed and fitted) and the residuals for a row or c...
Plot the results of the three models used in the NAFTA scheme.
Read datasets and relevant meta information from a spreadsheet file
Objects exported from other packages
Extract residuals from an mkinfit model
Fit nonlinear mixed models with SAEM
Set non-detects and unquantified values in residue series without repl...
Single First-Order kinetics
Single First-Order Reversible Binding kinetics
Two-component error model
Method to get status information for fit array objects
Summary method for class "mkinfit"
Summary method for class "mmkin"
Summary method for class "nlme.mmkin"
Summary method for class "saem.mmkin"
Display the output of a summary function according to the output forma...
Functions to transform and backtransform kinetic parameters for fittin...
Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Heteroscedasticity can be taken into account using variance by variable or two-component error models as described by Ranke and Meinecke (2018) <doi:10.3390/environments6120124>. Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end as described by Ranke et al. (2021) <doi:10.3390/environments8080071>. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.