- recipe r-bmix
Binomial and Beta-Binomial mixture models for counts data.
- Homepage:
- Documentation:
- License:
GPL3 / GPL-3.0-or-later
- Recipe:
BMix provides univariate Binomial and Beta-Binomial mixture models. Count-based mixtures can be used in a variety of settings, for instance to model genome sequencing data of somatic mutations in cancer. BMix fits these mixtures by maximum likelihood exploiting the Expectation Maximization algorithm. Model selection for the number of mixture components is by the Integrated Classification Likelihood, an extension of the Bayesian Information Criterion that includes the entropy of the latent variables.
- package r-bmix¶
- Versions:
1.0.0-0- Depends:
on r-base
>=4.4,<4.5.0a0on r-cli
on r-cowplot
on r-crayon
on r-dplyr
on r-easypar
on r-ggplot2
on r-knitr
on r-markdown
on r-pio
on r-tibble
on r-vgam
- 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-bmix
to add into an existing workspace instead, run:
pixi add r-bmix
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-bmix
Alternatively, to install into a new environment, run:
conda create -n envname r-bmix
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-bmix:<tag>
(see r-bmix/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:
[](http://bioconda.github.io/recipes/r-bmix/README.html)
