recipe r-eztune

Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune.cv will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading.

Homepage:

https://CRAN.R-project.org/package=EZtune

License:

GPL3 / GPL-3

Recipe:

/r-eztune/meta.yaml

package r-eztune

versions:

3.1.1-1,  3.1.1-0

depends bioconductor-biocstyle:

depends r-ada:

depends r-base:

>=4.4,<4.5.0a0

depends r-e1071:

depends r-ga:

depends r-gbm:

depends r-glmnet:

depends r-optimx:

depends r-rocr:

depends r-rpart:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install r-eztune

and update with::

   mamba update r-eztune

To create a new environment, run:

mamba create --name myenvname r-eztune

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

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

(see `r-eztune/tags`_ for valid values for ``<tag>``)

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