The cvasi package aims to ease the use of ecotox effect models by providing an intuitive workflow. Model inputs and parameters are encapsulated in scenario objects which can be piped to other functions. Operations can be chained using the tidyr syntax. The most time-consuming processes can be run in parallel if requested.

The package provides facilities to

• simulate effect models such as GUTS-RED, DEB, Lemna, Myriophyllum, and Algae
• calculate effect endpoints
• derive effect profiles (EPx values)
• import exposure time-series from FOCUS TOXSWA
• import fitted parameters from morse
• and more

A graphical user interface implemented in Shiny is also available, see the cvasi.ui package. Please have a look at the Changelog for an overview of user-facing updates and changes.

Install the package from CRAN:

install.packages("cvasi", dependencies=TRUE)

Or install the newest development version from GitHub:

install.packages("remotes", dependencies=TRUE)
remotes::install_github("cvasi-tktd/cvasi", dependencies=TRUE)

For installing cvasi from GitHub on Windows computers, please make sure that you also have Rtools installed on your machine. Rtools are required to compile the package’s C code.

The package contains the following vignettes

• User Manual
• Modeling Howto

They can also be accessed locally by executing an R statement such as:

vignette("manual", package="cvasi")

Basic usage:

library(cvasi)

# create and parameterize a GUTS-RED-IT scenario
GUTS_RED_IT() %>%
  set_param(c(kd=0.0005, hb=0, alpha=0.4, beta=1.5)) %>%
  set_exposure(data.frame(time=c(0, 100, 101, 200, 201, 400),
                          conc=c(0, 0, 0.1, 0.1, 0, 0))) -> scenario

# simulate scenario
scenario %>%
  simulate(times=1:400) %>%
  tail()
#>     time           D H
#> 395  395 0.004429420 0
#> 396  396 0.004427206 0
#> 397  397 0.004424993 0
#> 398  398 0.004422781 0
#> 399  399 0.004420570 0
#> 400  400 0.004418360 0

Calculation of effects:

# calculate effect level
scenario %>% effect()
#> # A tibble: 1 × 4
#>   scenario         L L.dat.start L.dat.end
#>   <list>       <dbl>       <dbl>     <dbl>
#> 1 <GutsRdIt> 0.00135           0       400

# create a dose-response curve
scenario %>% dose_response() -> drc
head(drc)
#>   endpoint        mf      effect
#> 1        L  3.812500 0.009920108
#> 2        L  4.799653 0.013948570
#> 3        L  6.042405 0.019601514
#> 4        L  7.606938 0.027459506
#> 5        L  9.576567 0.038355140
#> 6        L 12.056184 0.053336114

# plot the dose-response curve
library(ggplot2)
ggplot(drc) + geom_point(aes(mf,effect)) + scale_x_log10()

# derive EPx values
scenario %>% epx()
#> # A tibble: 1 × 3
#>   scenario   L.EP10 L.EP50
#>   <list>      <dbl>  <dbl>
#> 1 <GutsRdIt>   19.0   82.1

Multiple scenarios can be processed in parallel without modifications to the workflow:

# enable parallel processing
future::plan(future::multisession)

# derive EPx for a list of 100 scenarios in parallel
rep(c(scenario), 100) %>% epx()

# disable parallel processing
future::plan(future::sequential)

The package and its source code is free and open-source software available under the GPL-3.0 license.

If you find any issues or bugs within the package, please create a new issue on GitHub.

Contributions to the project are welcome! Please have a look at the Contribution Guidelines before submitting a Pull Request.

Financial support for creation and release of this software project was provided by Bayer Crop Science. This R package started as an internal project at Bayer Crop Science and the project owners would like to thank the people who have contributed (in no particular order):

Nils Kehrein, Johannes Witt, André Gergs, Thomas Preuss, Julian Heinrich, Zhenglei Gao, Tjalling Jager, Dirk Nickisch, Torben Wittwer, and Peter Vermeiren.