From 1d8b1f890f45dabc3e1360d9475a747d4e6db2a6 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Mon, 29 Jun 2015 09:42:27 +0200
Subject: Do not include plots and R output in README

This does not get rendered properly on CRAN, and is easily
replaced by links to the static documentation on R-Forge.
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-# mkin
-
-The R package **mkin** provides calculation routines for the analysis of
-chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as
-needed for modelling the formation and decline of transformation products, or
-if several compartments are involved.
-
-## Installation
-
-You can install the latest released version from 
-[CRAN](http://cran.r-project.org/package=mkin) from within R:
-
-```{r, eval = FALSE}
-install.packages("mkin")
-```
-
-If looking for the latest features, you can install directly from 
-[github](http://github.com/jranke/mkin), e.g.  using the `devtools` package.
-Using `quick = TRUE` skips docs, multiple-architecture builds, demos, and
-vignettes, to make installation as fast and painless as possible.
-
-```{r, eval = FALSE}
-require(devtools)
-install_github("jranke/mkin", quick = TRUE)
-```
-
-## Background
-
-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.
-
-## Usage
-
-The simplest usage example that I can think of, using model shorthand notation
-(available since mkin 0.9-32) and a built-in dataset is
-
-```{r, message = FALSE}
-library(mkin)
-fit <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
-plot(fit, show_residuals = TRUE) 
-```
-
-```{r, eval = FALSE}
-# Output not shown in this README to avoid distraction
-summary(fit)
-```
-
-A still very simple usage example including the definition of the same data in R
-code would be
-
-```{r}
-example_data = data.frame(
-  name = rep("parent", 9),
-  time = c(0, 1, 3, 7, 14, 28, 63, 91, 119),
-  value = c(85.1, 57.9, 29.9, 14.6, 9.7, 6.6, 4, 3.9, 0.6)
-)
-fit2 <- mkinfit("FOMC", example_data, quiet = TRUE)
-plot(fit2, show_residuals = TRUE) 
-```
-
-A fairly complex usage example using another built-in dataset:
-
-```{r echo = FALSE, message = FALSE}
-# Somehow knitr does not find the signature() function
-require(methods)
-```
-
-```{r, message = FALSE}
-data <- mkin_wide_to_long(schaefer07_complex_case, time = "time")
-
-model <- mkinmod(
-  parent = mkinsub("SFO", c("A1", "B1", "C1"), sink = FALSE),
-  A1 = mkinsub("SFO", "A2"),
-  B1 = mkinsub("SFO"),
-  C1 = mkinsub("SFO"),
-  A2 = mkinsub("SFO"), use_of_ff = "max")
-
-fit3 <- mkinfit(model, data, method.modFit = "Port", quiet = TRUE)
-
-plot(fit3, show_residuals = TRUE) 
-#summary(fit3) # Commented out to avoid distraction from README content
-mkinparplot(fit3)
-```
-
-For more examples and to see results, have a look at the examples provided in the
-[`mkinfit`](http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html)
-documentation or the package vignettes referenced from the 
-[mkin package documentation page](http://kinfit.r-forge.r-project.org/mkin_static/index.html)
-
-## Features
-
-* Highly flexible model specification using
-  [`mkinmod`](http://kinfit.r-forge.r-project.org/mkin_static/mkinmod.html),
-  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.
-* Model solution (forward modelling) in the function
-  [`mkinpredict`](http://kinfit.r-forge.r-project.org/mkin_static/mkinpredict.html) 
-  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`).
-  These have decreasing efficiency, and are automatically chosen 
-  by default. 
-* As of mkin 0.9-36, model solution for models with more than one observed
-  variable is based on the inline package.  This is even faster than eigenvalue
-  based solution, at least in the example shown in the
-  [vignette `compiled_models`](http://rawgit.com/jranke/mkin/master/vignettes/compiled_models.html).
-  The autogeneration of C code was
-  inspired by the [`ccSolve`](https://github.com/karlines/ccSolve) package. Thanks
-  to Karline Soetaert for her work on that.
-* Model optimisation with 
-  [`mkinfit`](http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html)
-  internally using the `modFit` function from the `FME` package,
-  but using the Port routine `nlminb` per default.
-* By default, kinetic rate constants and kinetic formation fractions are
-  transformed internally using
-  [`transform_odeparms`](http://kinfit.r-forge.r-project.org/mkin_static/transform_odeparms.html)
-  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 René 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.
-* I recently added iteratively reweighted least squares in a similar way
-  it is done 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, 
-  either IORE kinetics or SFORB kinetics can be used for the metabolite, 
-  adding one respectively two parameters to the system.
-
-## GUI
-
-There is a graphical user interface that I consider useful for real work. Please
-refer to its [documentation page](http://kinfit.r-forge.r-project.org/gmkin_static)
-for installation instructions and a manual.
-  
-## News
-
-Yes, there is a ChangeLog, for the latest [CRAN release](http://cran.r-project.org/web/packages/mkin/NEWS.html)
-and one for the [github master branch](https://github.com/jranke/mkin/blob/master/NEWS.md).
-
-## Credits and historical remarks
-
-`mkin` would not be possible without the underlying software stack consisting
-of R and the packages [deSolve](http://cran.r-project.org/package=deSolve),
-[minpack.lm](http://cran.r-project.org/package=minpack.lm) and
-[FME](http://cran.r-project.org/package=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](http://focus.jrc.ec.europa.eu/dk),
-as detailed in their guidance document from 2006, slightly updated in 2011.
-
-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](http://kinfit.r-forge.r-project.org/kinfit_static/index.html) was 
-[started in 2008](https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=2) and 
-[first published](http://cran.r-project.org/src/contrib/Archive/kinfit/) on
-CRAN on 01 May 2010.
-
-The first `mkin` code was 
-[published on 11 May 2010](https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8) and the 
-[first CRAN version](http://cran.r-project.org/src/contrib/Archive/mkin)
-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](http://projects.tessella.com/cake), where you can also
-find a zip archive of the R scripts derived from `mkin`, published under the GPL
-license.
-
-Finally, there is 
-[KineticEval](http://github.com/zhenglei-gao/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.
-
-
-## Development
-
-Contributions are welcome! Your 
-[mkin fork](https://help.github.com/articles/fork-a-repo) 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](http://r-forge.r-project.org/R/?group_id=615) are welcome as well.
-Generally, the source code of the latest CRAN version should be available there.
-- 
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