morseDR

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference with several type of ecotoxicological data: 'binary' (e.g., survival, mobility), 'count' (e.g., reproduction) and 'continuous' (e.g., growth rate, length, weight).

Install from CRAN

library(remotes)
remotes::install_gitlab("mosaic-software/morsedr", host = "gitlab.in2p3.fr")

Development

Submission

Before a submission, you can look at prepare-for-cran , which is an open and collaborative list of things you have to check before submitting your package to the CRAN.

Otherwise, check "as-cran"" using the source package:

library(devtools)
# create documentation
devtools::document(roclets = c('rd', 'collate', 'namespace'))

Once the archive is done, check that '.Rbuildignore' was applied properly. Try to have a low size archive (< 2Mb)

Either directly

# build and check the archive
devtools::check()
# not: `devtools::check(cran = TRUE)` is the default

Or in 2 steps:

# 1. build the package. 
devtools::build()
# 2. check the archive. 
devtools::check_built("../morseDR_X.Y.Z.tar.gz")

See the CRAN status of your sumbmission:

• incoming R CRAN packages: Index of /incoming
• incoming dashboard: incoming dashboard

Instead of doing check and the build, we can do:

devtools::release()

Build the manual

library('devtools')
devtools::document(roclets = c('rd', 'collate', 'namespace'))
devtools::build_manual()

Coverage:

From R session

library(covr)
cov <- package_coverage("morseDR")

Style of process

The succession of steps

1. data: load the data set.
2. BinaryData, CountData or ContinuousData: make a ModelData object for binary, count and quantitative continuous data, respectively.
3. The above-mentioned objects inherit of data.frame
4. plot: plot a ModelData object.
5. summary: provides a summary of a ModelData object.
6. doseResponse: return a DoseResponse object.
7. plot: plot a DoseResponse object.
8. fit: fit a ModelData object and return a Fit object.
9. plot: plot a Fit object.
10. ppc: return a PPC object.
11. plot: plot a PPC object.

Coding Style

Object: BigCamelCase

class(x) <- append("ObjectCamelCase", class(x))

Methods: small_snake_case

methods_snake_case.ObjectCamelCase <- function(...){}

Function (no methods - not linked to object): smallCamelCase

smallCamelCase <- function(...){}

Container

A docker image ready to use

The dockerfile and the following script prepared a docker image ready to use in a docker machine.

Build the Docker Image: First, ensure that you have built your Docker image using a Dockerfile. You can do this with the following command:

docker build -t setup-morsedr .

Run the Docker Image: Once the image is built, you can run it as a container using the following command:

docker run -it setup-morsedr /bin/bash

push docker image on gitlab container registry

docker login gitlab-registry.in2p3.fr

# docker build -t gitlab-registry.in2p3.fr/mosaic-software/morsedr .
# docker push gitlab-registry.in2p3.fr/mosaic-software/morsedr

docker build -t gitlab-registry.in2p3.fr/mosaic-software/morsedr/morsedr-setup .
docker push gitlab-registry.in2p3.fr/mosaic-software/morsedr/morsedr-setup

docker run -it gitlab-registry.in2p3.fr/mosaic-software/morsedr/morsedr-setup /bin/bash