Package Recipe 'r-sbpiper'

recipe r-sbpiper

Provides an API for analysing repetitive parameter estimations and simulations of mathematical models. Examples of mathematical models are Ordinary Differential equations (ODEs) or Stochastic Differential Equations (SDEs) models. Among the analyses for parameter estimation 'sbpiper' calculates statistics and generates plots for parameter density, PCA of the best fits, parameter profile likelihood estimations (PLEs), and 2D parameter PLEs. These results can be generated using all or a subset of the best computed parameter sets. Among the analyses for model simulation 'sbpiper' calculates statistics and generates plots for deterministic and stochastic time courses via cartesian and heatmap plots. Plots for the scan of one or two model parameters can also be generated. This package is primarily used by the software 'SBpipe'. Citation: Dalle Pezze P, Le NovÃ¨re N. SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks. BMC Systems Biology. 2017;11:46. <doi:10.1186/s12918-017-0423-3>.

Homepage:: https://github.com/pdp10/sbpiper
License:: MIT / MIT
Recipe:: /r-sbpiper/meta.yaml
Links:: doi: 10.1186/s12918-017-0423-3

package r-sbpiper¶

Versions:

1.9.0-9, 1.9.0-8, 1.9.0-7, 1.9.0-6, 1.9.0-5, 1.9.0-4, 1.9.0-3, 1.9.0-2, 1.9.0-1,

1.9.0-9, 1.9.0-8, 1.9.0-7, 1.9.0-6, 1.9.0-5, 1.9.0-4, 1.9.0-3, 1.9.0-2, 1.9.0-1, 1.8.0-0

Depends:

on r-base >=4.4,<4.5.0a0
on r-colorramps
on r-data.table
on r-factoextra
on r-factominer
on r-ggplot2 >=2.2.0
on r-hmisc
on r-reshape2
on r-scales
on r-stringr

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install r-sbpiper

to add into an existing workspace instead, run:

pixi add r-sbpiper

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install r-sbpiper

Alternatively, to install into a new environment, run:

conda create -n envname r-sbpiper

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/r-sbpiper:<tag>

(see r-sbpiper/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

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