The <code>exprso</code> package includes these data process modules:
- <code><a rd-options="" href="/link/modHistory?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modHistory">modHistory</a></code>
- <code><a rd-options="" href="/link/modSubset?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modSubset">modSubset</a></code>
- <code><a rd-options="" href="/link/modFilter?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modFilter">modFilter</a></code>
- <code><a rd-options="" href="/link/modTransform?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modTransform">modTransform</a></code>
- <code><a rd-options="" href="/link/modSample?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modSample">modSample</a></code>
- <code><a rd-options="" href="/link/modPermute?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modPermute">modPermute</a></code>
- <code><a rd-options="" href="/link/modInclude?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modInclude">modInclude</a></code>
- <code><a rd-options="" href="/link/modNormalize?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modNormalize">modNormalize</a></code>
- <code><a rd-options="" href="/link/modTMM?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modTMM">modTMM</a></code>
- <code><a rd-options="" href="/link/modAcomp?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modAcomp">modAcomp</a></code>
- <code><a rd-options="" href="/link/modCLR?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modCLR">modCLR</a></code>
- <code><a rd-options="" href="/link/modRatios?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modRatios">modRatios</a></code>
- <code><a rd-options="" href="/link/modScale?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modScale">modScale</a></code>
- <code><a rd-options="" href="/link/modSkew?package=exprso&version=0.5.1" data-mini-rdoc="exprso::modSkew">modSkew</a></code>

Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly building and deploying
supervised machine learning in a high-throughput manner. This package provides a
user-friendly interface that empowers investigators to execute state-of-the-art
binary and multi-class classification, as well as regression, with minimal
programming experience necessary.

Thomas Quinn

exprso

Rapid Deployment of Machine Learning Algorithms

Daniel Tylee

Samuel Lee

mod function

The <code>exprso</code> package includes these data process modules:
- <code><a rd-options='' href='modHistory'>modHistory</a></code>
- <code><a rd-options='' href='modSubset'>modSubset</a></code>
- <code><a rd-options='' href='modFilter'>modFilter</a></code>
- <code><a rd-options='' href='modTransform'>modTransform</a></code>
- <code><a rd-options='' href='modSample'>modSample</a></code>
- <code><a rd-options='' href='modPermute'>modPermute</a></code>
- <code><a rd-options='' href='modInclude'>modInclude</a></code>
- <code><a rd-options='' href='modNormalize'>modNormalize</a></code>
- <code><a rd-options='' href='modTMM'>modTMM</a></code>
- <code><a rd-options='' href='modAcomp'>modAcomp</a></code>
- <code><a rd-options='' href='modCLR'>modCLR</a></code>
- <code><a rd-options='' href='modRatios'>modRatios</a></code>
- <code><a rd-options='' href='modScale'>modScale</a></code>
- <code><a rd-options='' href='modSkew'>modSkew</a></code>

mod: Process Data

Description

Arguments